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Problem Solving Using Computer (Steps)

Computer based problem solving is a systematic process of designing, implementing and using programming tools during the problem solving stage. This method enables the computer system to be more intuitive with human logic than machine logic. Final outcome of this process is software tools which is dedicated to solve the problem under consideration. Software is just a collection of computer programs and programs are a set of instructions which guides computer’s hardware. These instructions need to be well specified for solving the problem. After its creation, the software should be error free and well documented. Software development is the process of creating such software, which satisfies end user’s requirements and needs.

The following six steps must be followed to solve a problem using computer.

Problem Analysis
Program Design - Algorithm, Flowchart and Pseudocode
Compilation and Execution
Debugging and Testing
Program Documentation

What Is Problem Solving? How Software Engineers Approach Complex Challenges

From debugging an existing system to designing an entirely new software application, a day in the life of a software engineer is filled with various challenges and complexities. The one skill that glues these disparate tasks together and makes them manageable? Problem solving .

Throughout this blog post, we’ll explore why problem-solving skills are so critical for software engineers, delve into the techniques they use to address complex challenges, and discuss how hiring managers can identify these skills during the hiring process.

What Is Problem Solving?

But what exactly is problem solving in the context of software engineering? How does it work, and why is it so important?

Problem solving, in the simplest terms, is the process of identifying a problem, analyzing it, and finding the most effective solution to overcome it. For software engineers, this process is deeply embedded in their daily workflow. It could be something as simple as figuring out why a piece of code isn’t working as expected, or something as complex as designing the architecture for a new software system.

In a world where technology is evolving at a blistering pace, the complexity and volume of problems that software engineers face are also growing. As such, the ability to tackle these issues head-on and find innovative solutions is not only a handy skill — it’s a necessity.

The Importance of Problem-Solving Skills for Software Engineers

Problem-solving isn’t just another ability that software engineers pull out of their toolkits when they encounter a bug or a system failure. It’s a constant, ongoing process that’s intrinsic to every aspect of their work. Let’s break down why this skill is so critical.

Driving Development Forward

Without problem solving, software development would hit a standstill. Every new feature, every optimization, and every bug fix is a problem that needs solving. Whether it’s a performance issue that needs diagnosing or a user interface that needs improving, the capacity to tackle and solve these problems is what keeps the wheels of development turning.

It’s estimated that 60% of software development lifecycle costs are related to maintenance tasks, including debugging and problem solving. This highlights how pivotal this skill is to the everyday functioning and advancement of software systems.

Innovation and Optimization

The importance of problem solving isn’t confined to reactive scenarios; it also plays a major role in proactive, innovative initiatives . Software engineers often need to think outside the box to come up with creative solutions, whether it’s optimizing an algorithm to run faster or designing a new feature to meet customer needs. These are all forms of problem solving.

Consider the development of the modern smartphone. It wasn’t born out of a pre-existing issue but was a solution to a problem people didn’t realize they had — a device that combined communication, entertainment, and productivity into one handheld tool.

Increasing Efficiency and Productivity

Good problem-solving skills can save a lot of time and resources. Effective problem-solvers are adept at dissecting an issue to understand its root cause, thus reducing the time spent on trial and error. This efficiency means projects move faster, releases happen sooner, and businesses stay ahead of their competition.

Improving Software Quality

Problem solving also plays a significant role in enhancing the quality of the end product. By tackling the root causes of bugs and system failures, software engineers can deliver reliable, high-performing software. This is critical because, according to the Consortium for Information and Software Quality, poor quality software in the U.S. in 2022 cost at least $2.41 trillion in operational issues, wasted developer time, and other related problems.

Problem-Solving Techniques in Software Engineering

So how do software engineers go about tackling these complex challenges? Let’s explore some of the key problem-solving techniques, theories, and processes they commonly use.

Decomposition

Breaking down a problem into smaller, manageable parts is one of the first steps in the problem-solving process. It’s like dealing with a complicated puzzle. You don’t try to solve it all at once. Instead, you separate the pieces, group them based on similarities, and then start working on the smaller sets. This method allows software engineers to handle complex issues without being overwhelmed and makes it easier to identify where things might be going wrong.

Abstraction

In the realm of software engineering, abstraction means focusing on the necessary information only and ignoring irrelevant details. It is a way of simplifying complex systems to make them easier to understand and manage. For instance, a software engineer might ignore the details of how a database works to focus on the information it holds and how to retrieve or modify that information.

Algorithmic Thinking

At its core, software engineering is about creating algorithms — step-by-step procedures to solve a problem or accomplish a goal. Algorithmic thinking involves conceiving and expressing these procedures clearly and accurately and viewing every problem through an algorithmic lens. A well-designed algorithm not only solves the problem at hand but also does so efficiently, saving computational resources.

Parallel Thinking

Parallel thinking is a structured process where team members think in the same direction at the same time, allowing for more organized discussion and collaboration. It’s an approach popularized by Edward de Bono with the “ Six Thinking Hats ” technique, where each “hat” represents a different style of thinking.

In the context of software engineering, parallel thinking can be highly effective for problem solving. For instance, when dealing with a complex issue, the team can use the “White Hat” to focus solely on the data and facts about the problem, then the “Black Hat” to consider potential problems with a proposed solution, and so on. This structured approach can lead to more comprehensive analysis and more effective solutions, and it ensures that everyone’s perspectives are considered.

This is the process of identifying and fixing errors in code . Debugging involves carefully reviewing the code, reproducing and analyzing the error, and then making necessary modifications to rectify the problem. It’s a key part of maintaining and improving software quality.

Testing and Validation

Testing is an essential part of problem solving in software engineering. Engineers use a variety of tests to verify that their code works as expected and to uncover any potential issues. These range from unit tests that check individual components of the code to integration tests that ensure the pieces work well together. Validation, on the other hand, ensures that the solution not only works but also fulfills the intended requirements and objectives.

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Evaluating Problem-Solving Skills

We’ve examined the importance of problem-solving in the work of a software engineer and explored various techniques software engineers employ to approach complex challenges. Now, let’s delve into how hiring teams can identify and evaluate problem-solving skills during the hiring process.

Recognizing Problem-Solving Skills in Candidates

How can you tell if a candidate is a good problem solver? Look for these indicators:

Previous Experience: A history of dealing with complex, challenging projects is often a good sign. Ask the candidate to discuss a difficult problem they faced in a previous role and how they solved it.
Problem-Solving Questions: During interviews, pose hypothetical scenarios or present real problems your company has faced. Ask candidates to explain how they would tackle these issues. You’re not just looking for a correct solution but the thought process that led them there.
Technical Tests: Coding challenges and other technical tests can provide insight into a candidate’s problem-solving abilities. Consider leveraging a platform for assessing these skills in a realistic, job-related context.

Assessing Problem-Solving Skills

Once you’ve identified potential problem solvers, here are a few ways you can assess their skills:

Solution Effectiveness: Did the candidate solve the problem? How efficient and effective is their solution?
Approach and Process: Go beyond whether or not they solved the problem and examine how they arrived at their solution. Did they break the problem down into manageable parts? Did they consider different perspectives and possibilities?
Communication: A good problem solver can explain their thought process clearly. Can the candidate effectively communicate how they arrived at their solution and why they chose it?
Adaptability: Problem-solving often involves a degree of trial and error. How does the candidate handle roadblocks? Do they adapt their approach based on new information or feedback?

Hiring managers play a crucial role in identifying and fostering problem-solving skills within their teams. By focusing on these abilities during the hiring process, companies can build teams that are more capable, innovative, and resilient.

Key Takeaways

As you can see, problem solving plays a pivotal role in software engineering. Far from being an occasional requirement, it is the lifeblood that drives development forward, catalyzes innovation, and delivers of quality software.

By leveraging problem-solving techniques, software engineers employ a powerful suite of strategies to overcome complex challenges. But mastering these techniques isn’t simple feat. It requires a learning mindset, regular practice, collaboration, reflective thinking, resilience, and a commitment to staying updated with industry trends.

For hiring managers and team leads, recognizing these skills and fostering a culture that values and nurtures problem solving is key. It’s this emphasis on problem solving that can differentiate an average team from a high-performing one and an ordinary product from an industry-leading one.

At the end of the day, software engineering is fundamentally about solving problems — problems that matter to businesses, to users, and to the wider society. And it’s the proficient problem solvers who stand at the forefront of this dynamic field, turning challenges into opportunities, and ideas into reality.

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Does a College Degree Still Matter for Developers in 2024?

Exploring the Problem Solving Cycle in Computer Science – Strategies, Techniques, and Tools

Post author By bicycle-u
Post date 08.12.2023

The world of computer science is built on the foundation of problem solving. Whether it’s finding a solution to a complex algorithm or analyzing data to make informed decisions, the problem solving cycle is at the core of every computer science endeavor.

At its essence, problem solving in computer science involves breaking down a complex problem into smaller, more manageable parts. This allows for a systematic approach to finding a solution by analyzing each part individually. The process typically starts with gathering and understanding the data or information related to the problem at hand.

Once the data is collected, computer scientists use various techniques and algorithms to analyze and explore possible solutions. This involves evaluating different approaches and considering factors such as efficiency, accuracy, and scalability. During this analysis phase, it is crucial to think critically and creatively to come up with innovative solutions.

After a thorough analysis, the next step in the problem solving cycle is designing and implementing a solution. This involves creating a detailed plan of action, selecting the appropriate tools and technologies, and writing the necessary code to bring the solution to life. Attention to detail and precision are key in this stage to ensure that the solution functions as intended.

The final step in the problem solving cycle is evaluating the solution and its effectiveness. This includes testing the solution against different scenarios and data sets to ensure its reliability and performance. If any issues or limitations are discovered, adjustments and optimizations are made to improve the solution.

In conclusion, the problem solving cycle is a fundamental process in computer science, involving analysis, data exploration, algorithm development, solution implementation, and evaluation. It is through this cycle that computer scientists are able to tackle complex problems and create innovative solutions that drive progress in the field of computer science.

Understanding the Importance

In computer science, problem solving is a crucial skill that is at the core of the problem solving cycle. The problem solving cycle is a systematic approach to analyzing and solving problems, involving various stages such as problem identification, analysis, algorithm design, implementation, and evaluation. Understanding the importance of this cycle is essential for any computer scientist or programmer.

Data Analysis and Algorithm Design

The first step in the problem solving cycle is problem identification, which involves recognizing and defining the issue at hand. Once the problem is identified, the next crucial step is data analysis. This involves gathering and examining relevant data to gain insights and understand the problem better. Data analysis helps in identifying patterns, trends, and potential solutions.

After data analysis, the next step is algorithm design. An algorithm is a step-by-step procedure or set of rules to solve a problem. Designing an efficient algorithm is crucial as it determines the effectiveness and efficiency of the solution. A well-designed algorithm takes into consideration the constraints, resources, and desired outcomes while implementing the solution.

Implementation and Evaluation

Once the algorithm is designed, the next step in the problem solving cycle is implementation. This involves translating the algorithm into a computer program using a programming language. The implementation phase requires coding skills and expertise in a specific programming language.

After implementation, the solution needs to be evaluated to ensure that it solves the problem effectively. Evaluation involves testing the program and verifying its correctness and efficiency. This step is critical to identify any errors or issues and to make necessary improvements or adjustments.

In conclusion, understanding the importance of the problem solving cycle in computer science is essential for any computer scientist or programmer. It provides a systematic and structured approach to analyze and solve problems, ensuring efficient and effective solutions. By following the problem solving cycle, computer scientists can develop robust algorithms, implement them in efficient programs, and evaluate their solutions to ensure their correctness and efficiency.

Identifying the Problem

In the problem solving cycle in computer science, the first step is to identify the problem that needs to be solved. This step is crucial because without a clear understanding of the problem, it is impossible to find a solution.

Identification of the problem involves a thorough analysis of the given data and understanding the goals of the task at hand. It requires careful examination of the problem statement and any constraints or limitations that may affect the solution.

During the identification phase, the problem is broken down into smaller, more manageable parts. This can involve breaking the problem down into sub-problems or identifying the different aspects or components that need to be addressed.

Identifying the problem also involves considering the resources and tools available for solving it. This may include considering the specific tools and programming languages that are best suited for the problem at hand.

By properly identifying the problem, computer scientists can ensure that they are focused on the right goals and are better equipped to find an effective and efficient solution. It sets the stage for the rest of the problem solving cycle, including the analysis, design, implementation, and evaluation phases.

Gathering the Necessary Data

Before finding a solution to a computer science problem, it is essential to gather the necessary data. Whether it’s writing a program or developing an algorithm, data serves as the backbone of any solution. Without proper data collection and analysis, the problem-solving process can become inefficient and ineffective.

The Importance of Data

In computer science, data is crucial for a variety of reasons. First and foremost, it provides the information needed to understand and define the problem at hand. By analyzing the available data, developers and programmers can gain insights into the nature of the problem and determine the most efficient approach for solving it.

Additionally, data allows for the evaluation of potential solutions. By collecting and organizing relevant data, it becomes possible to compare different algorithms or strategies and select the most suitable one. Data also helps in tracking progress and measuring the effectiveness of the chosen solution.

Data Gathering Process

The process of gathering data involves several steps. Firstly, it is necessary to identify the type of data needed for the particular problem. This may include numerical values, textual information, or other types of data. It is important to determine the sources of data and assess their reliability.

Once the required data has been identified, it needs to be collected. This can be done through various methods, such as surveys, experiments, observations, or by accessing existing data sets. The collected data should be properly organized, ensuring its accuracy and validity.

Data cleaning and preprocessing are vital steps in the data gathering process. This involves removing any irrelevant or erroneous data and transforming it into a suitable format for analysis. Properly cleaned and preprocessed data will help in generating reliable and meaningful insights.

Data Analysis and Interpretation

After gathering and preprocessing the data, the next step is data analysis and interpretation. This involves applying various statistical and analytical methods to uncover patterns, trends, and relationships within the data. By analyzing the data, programmers can gain valuable insights that can inform the development of an effective solution.

During the data analysis process, it is crucial to remain objective and unbiased. The analysis should be based on sound reasoning and logical thinking. It is also important to communicate the findings effectively, using visualizations or summaries to convey the information to stakeholders or fellow developers.

In conclusion, gathering the necessary data is a fundamental step in solving computer science problems. It provides the foundation for understanding the problem, evaluating potential solutions, and tracking progress. By following a systematic and rigorous approach to data gathering and analysis, developers can ensure that their solutions are efficient, effective, and well-informed.

Analyzing the Data

Once you have collected the necessary data, the next step in the problem-solving cycle is to analyze it. Data analysis is a crucial component of computer science, as it helps us understand the problem at hand and develop effective solutions.

To analyze the data, you need to break it down into manageable pieces and examine each piece closely. This process involves identifying patterns, trends, and outliers that may be present in the data. By doing so, you can gain insights into the problem and make informed decisions about the best course of action.

There are several techniques and tools available for data analysis in computer science. Some common methods include statistical analysis, data visualization, and machine learning algorithms. Each approach has its own strengths and limitations, so it’s essential to choose the most appropriate method for the problem you are solving.

Statistical Analysis

Statistical analysis involves using mathematical models and techniques to analyze data. It helps in identifying correlations, distributions, and other statistical properties of the data. By applying statistical tests, you can determine the significance and validity of your findings.

Data Visualization

Data visualization is the process of presenting data in a visual format, such as charts, graphs, or maps. It allows for a better understanding of complex data sets and facilitates the communication of findings. Through data visualization, patterns and trends can become more apparent, making it easier to derive meaningful insights.

Machine Learning Algorithms

Machine learning algorithms are powerful tools for analyzing large and complex data sets. These algorithms can automatically detect patterns and relationships in the data, leading to the development of predictive models and solutions. By training the algorithm on a labeled dataset, it can learn from the data and make accurate predictions or classifications.

In conclusion, analyzing the data is a critical step in the problem-solving cycle in computer science. It helps us gain a deeper understanding of the problem and develop effective solutions. Whether through statistical analysis, data visualization, or machine learning algorithms, data analysis plays a vital role in transforming raw data into actionable insights.

Exploring Possible Solutions

Once you have gathered data and completed the analysis, the next step in the problem-solving cycle is to explore possible solutions. This is where the true power of computer science comes into play. With the use of algorithms and the application of scientific principles, computer scientists can develop innovative solutions to complex problems.

During this stage, it is important to consider a variety of potential solutions. This involves brainstorming different ideas and considering their feasibility and potential effectiveness. It may be helpful to consult with colleagues or experts in the field to gather additional insights and perspectives.

Developing an Algorithm

One key aspect of exploring possible solutions is the development of an algorithm. An algorithm is a step-by-step set of instructions that outlines a specific process or procedure. In the context of problem solving in computer science, an algorithm provides a clear roadmap for implementing a solution.

The development of an algorithm requires careful thought and consideration. It is important to break down the problem into smaller, manageable steps and clearly define the inputs and outputs of each step. This allows for the creation of a logical and efficient solution.

Evaluating the Solutions

Once you have developed potential solutions and corresponding algorithms, the next step is to evaluate them. This involves analyzing each solution to determine its strengths, weaknesses, and potential impact. Consider factors such as efficiency, scalability, and resource requirements.

It may be helpful to conduct experiments or simulations to further assess the effectiveness of each solution. This can provide valuable insights and data to support the decision-making process.

Ultimately, the goal of exploring possible solutions is to find the most effective and efficient solution to the problem at hand. By leveraging the power of data, analysis, algorithms, and scientific principles, computer scientists can develop innovative solutions that drive progress and solve complex problems in the world of technology.

Evaluating the Options

Once you have identified potential solutions and algorithms for a problem, the next step in the problem-solving cycle in computer science is to evaluate the options. This evaluation process involves analyzing the potential solutions and algorithms based on various criteria to determine the best course of action.

Consider the Problem

Before evaluating the options, it is important to take a step back and consider the problem at hand. Understand the requirements, constraints, and desired outcomes of the problem. This analysis will help guide the evaluation process.

Analyze the Options

Next, it is crucial to analyze each solution or algorithm option individually. Look at factors such as efficiency, accuracy, ease of implementation, and scalability. Consider whether the solution or algorithm meets the specific requirements of the problem, and if it can be applied to related problems in the future.

Additionally, evaluate the potential risks and drawbacks associated with each option. Consider factors such as cost, time, and resources required for implementation. Assess any potential limitations or trade-offs that may impact the overall effectiveness of the solution or algorithm.

Select the Best Option

Based on the analysis, select the best option that aligns with the specific problem-solving goals. This may involve prioritizing certain criteria or making compromises based on the limitations identified during the evaluation process.

Remember that the best option may not always be the most technically complex or advanced solution. Consider the practicality and feasibility of implementation, as well as the potential impact on the overall system or project.

In conclusion, evaluating the options is a critical step in the problem-solving cycle in computer science. By carefully analyzing the potential solutions and algorithms, considering the problem requirements, and considering the limitations and trade-offs, you can select the best option to solve the problem at hand.

Making a Decision

Decision-making is a critical component in the problem-solving process in computer science. Once you have analyzed the problem, identified the relevant data, and generated a potential solution, it is important to evaluate your options and choose the best course of action.

Consider All Factors

When making a decision, it is important to consider all relevant factors. This includes evaluating the potential benefits and drawbacks of each option, as well as understanding any constraints or limitations that may impact your choice.

In computer science, this may involve analyzing the efficiency of different algorithms or considering the scalability of a proposed solution. It is important to take into account both the short-term and long-term impacts of your decision.

Weigh the Options

Once you have considered all the factors, it is important to weigh the options and determine the best approach. This may involve assigning weights or priorities to different factors based on their importance.

Using techniques such as decision matrices or cost-benefit analysis can help you systematically compare and evaluate different options. By quantifying and assessing the potential risks and rewards, you can make a more informed decision.

Remember: Decision-making in computer science is not purely subjective or based on personal preference. It is crucial to use analytical and logical thinking to select the most optimal solution.

In conclusion, making a decision is a crucial step in the problem-solving process in computer science. By considering all relevant factors and weighing the options using logical analysis, you can choose the best possible solution to a given problem.

Implementing the Solution

Once the problem has been analyzed and a solution has been proposed, the next step in the problem-solving cycle in computer science is implementing the solution. This involves turning the proposed solution into an actual computer program or algorithm that can solve the problem.

In order to implement the solution, computer science professionals need to have a strong understanding of various programming languages and data structures. They need to be able to write code that can manipulate and process data in order to solve the problem at hand.

During the implementation phase, the proposed solution is translated into a series of steps or instructions that a computer can understand and execute. This involves breaking down the problem into smaller sub-problems and designing algorithms to solve each sub-problem.

Computer scientists also need to consider the efficiency of their solution during the implementation phase. They need to ensure that the algorithm they design is able to handle large amounts of data and solve the problem in a reasonable amount of time. This often requires optimization techniques and careful consideration of the data structures used.

Once the code has been written and the algorithm has been implemented, it is important to test and debug the solution. This involves running test cases and checking the output to ensure that the program is working correctly. If any errors or bugs are found, they need to be fixed before the solution can be considered complete.

In conclusion, implementing the solution is a crucial step in the problem-solving cycle in computer science. It requires strong programming skills and a deep understanding of algorithms and data structures. By carefully designing and implementing the solution, computer scientists can solve problems efficiently and effectively.

Testing and Debugging

In computer science, testing and debugging are critical steps in the problem-solving cycle. Testing helps ensure that a program or algorithm is functioning correctly, while debugging analyzes and resolves any issues or bugs that may arise.

Testing involves running a program with specific input data to evaluate its output. This process helps verify that the program produces the expected results and handles different scenarios correctly. It is important to test both the normal and edge cases to ensure the program’s reliability.

Debugging is the process of identifying and fixing errors or bugs in a program. When a program does not produce the expected results or crashes, it is necessary to go through the code to find and fix the problem. This can involve analyzing the program’s logic, checking for syntax errors, and using debugging tools to trace the flow of data and identify the source of the issue.

Data analysis plays a crucial role in both testing and debugging. It helps to identify patterns, anomalies, or inconsistencies in the program’s behavior. By analyzing the data, developers can gain insights into potential issues and make informed decisions on how to improve the program’s performance.

In conclusion, testing and debugging are integral parts of the problem-solving cycle in computer science. Through testing and data analysis, developers can verify the correctness of their programs and identify and resolve any issues that may arise. This ensures that the algorithms and programs developed in computer science are robust, reliable, and efficient.

Iterating for Improvement

In computer science, problem solving often involves iterating through multiple cycles of analysis, solution development, and evaluation. This iterative process allows for continuous improvement in finding the most effective solution to a given problem.

The problem solving cycle starts with problem analysis, where the specific problem is identified and its requirements are understood. This step involves examining the problem from various angles and gathering all relevant information.

Once the problem is properly understood, the next step is to develop an algorithm or a step-by-step plan to solve the problem. This algorithm is a set of instructions that, when followed correctly, will lead to the solution.

After the algorithm is developed, it is implemented in a computer program. This step involves translating the algorithm into a programming language that a computer can understand and execute.

Once the program is implemented, it is then tested and evaluated to ensure that it produces the correct solution. This evaluation step is crucial in identifying any errors or inefficiencies in the program and allows for further improvement.

If any issues or problems are found during testing, the cycle iterates, starting from problem analysis again. This iterative process allows for refinement and improvement of the solution until the desired results are achieved.

Iterating for improvement is a fundamental concept in computer science problem solving. By continually analyzing, developing, and evaluating solutions, computer scientists are able to find the most optimal and efficient approaches to solving problems.

Documenting the Process

Documenting the problem-solving process in computer science is an essential step to ensure that the cycle is repeated successfully. The process involves gathering information, analyzing the problem, and designing a solution.

During the analysis phase, it is crucial to identify the specific problem at hand and break it down into smaller components. This allows for a more targeted approach to finding the solution. Additionally, analyzing the data involved in the problem can provide valuable insights and help in designing an effective solution.

Once the analysis is complete, it is important to document the findings. This documentation can take various forms, such as written reports, diagrams, or even code comments. The goal is to create a record that captures the problem, the analysis, and the proposed solution.

Documenting the process serves several purposes. Firstly, it allows for easy communication and collaboration between team members or future developers. By documenting the problem, analysis, and solution, others can easily understand the thought process behind the solution and potentially build upon it.

Secondly, documenting the process provides an opportunity for reflection and improvement. By reviewing the documentation, developers can identify areas where the problem-solving cycle can be strengthened or optimized. This continuous improvement is crucial in the field of computer science, as new challenges and technologies emerge rapidly.

In conclusion, documenting the problem-solving process is an integral part of the computer science cycle. It allows for effective communication, collaboration, and reflection on the solutions devised. By taking the time to document the process, developers can ensure a more efficient and successful problem-solving experience.

Communicating the Solution

Once the problem solving cycle is complete, it is important to effectively communicate the solution. This involves explaining the analysis, data, and steps taken to arrive at the solution.

Analyzing the Problem

During the problem solving cycle, a thorough analysis of the problem is conducted. This includes understanding the problem statement, gathering relevant data, and identifying any constraints or limitations. It is important to clearly communicate this analysis to ensure that others understand the problem at hand.

Presenting the Solution

The next step in communicating the solution is presenting the actual solution. This should include a detailed explanation of the steps taken to solve the problem, as well as any algorithms or data structures used. It is important to provide clear and concise descriptions of the solution, so that others can understand and reproduce the results.

Overall, effective communication of the solution in computer science is essential to ensure that others can understand and replicate the problem solving process. By clearly explaining the analysis, data, and steps taken, the solution can be communicated in a way that promotes understanding and collaboration within the field of computer science.

Reflecting and Learning

Reflecting and learning are crucial steps in the problem solving cycle in computer science. Once a problem has been solved, it is essential to reflect on the entire process and learn from the experience. This allows for continuous improvement and growth in the field of computer science.

During the reflecting phase, one must analyze and evaluate the problem solving process. This involves reviewing the initial problem statement, understanding the constraints and requirements, and assessing the effectiveness of the chosen algorithm and solution. It is important to consider the efficiency and accuracy of the solution, as well as any potential limitations or areas for optimization.

By reflecting on the problem solving cycle, computer scientists can gain valuable insights into their own strengths and weaknesses. They can identify areas where they excelled and areas where improvement is needed. This self-analysis helps in honing problem solving skills and becoming a better problem solver.

Learning from Mistakes

Mistakes are an integral part of the problem solving cycle, and they provide valuable learning opportunities. When a problem is not successfully solved, it is essential to analyze the reasons behind the failure and learn from them. This involves identifying errors in the algorithm or solution, understanding the underlying concepts or principles that were misunderstood, and finding alternative approaches or strategies.

Failure should not be seen as a setback, but rather as an opportunity for growth. By learning from mistakes, computer scientists can improve their problem solving abilities and expand their knowledge and understanding of computer science. It is through these failures and the subsequent learning process that new ideas and innovations are often born.

Continuous Improvement

Reflecting and learning should not be limited to individual problem solving experiences, but should be an ongoing practice. As computer science is a rapidly evolving field, it is crucial to stay updated with new technologies, algorithms, and problem solving techniques. Continuous learning and improvement contribute to staying competitive and relevant in the field.

Computer scientists can engage in continuous improvement by seeking feedback from peers, participating in research and development activities, attending conferences and workshops, and actively seeking new challenges and problem solving opportunities. This dedication to learning and improvement ensures that one’s problem solving skills remain sharp and effective.

In conclusion, reflecting and learning are integral parts of the problem solving cycle in computer science. They enable computer scientists to refine their problem solving abilities, learn from mistakes, and continuously improve their skills and knowledge. By embracing these steps, computer scientists can stay at the forefront of the ever-changing world of computer science and contribute to its advancements.

Applying Problem Solving in Real Life

In computer science, problem solving is not limited to the realm of programming and algorithms. It is a skill that can be applied to various aspects of our daily lives, helping us to solve problems efficiently and effectively. By using the problem-solving cycle and applying the principles of analysis, data, solution, algorithm, and cycle, we can tackle real-life challenges with confidence and success.

The first step in problem-solving is to analyze the problem at hand. This involves breaking it down into smaller, more manageable parts and identifying the key issues or goals. By understanding the problem thoroughly, we can gain insights into its root causes and potential solutions.

For example, let’s say you’re facing a recurring issue in your daily commute – traffic congestion. By analyzing the problem, you may discover that the main causes are a lack of alternative routes and a lack of communication between drivers. This analysis helps you identify potential solutions such as using navigation apps to find alternate routes or promoting carpooling to reduce the number of vehicles on the road.

Gathering and Analyzing Data

Once we have identified the problem, it is important to gather relevant data to support our analysis. This may involve conducting surveys, collecting statistics, or reviewing existing research. By gathering data, we can make informed decisions and prioritize potential solutions based on their impact and feasibility.

Continuing with the traffic congestion example, you may gather data on the average commute time, the number of vehicles on the road, and the impact of carpooling on congestion levels. This data can help you analyze the problem more accurately and determine the most effective solutions.

Generating and Evaluating Solutions

After analyzing the problem and gathering data, the next step is to generate potential solutions. This can be done through brainstorming, researching best practices, or seeking input from experts. It is important to consider multiple options and think outside the box to find innovative and effective solutions.

For our traffic congestion problem, potential solutions can include implementing a smart traffic management system that optimizes traffic flow or investing in public transportation to incentivize people to leave their cars at home. By evaluating each solution’s potential impact, cost, and feasibility, you can make an informed decision on the best course of action.

Implementing and Iterating

Once a solution has been chosen, it is time to implement it in real life. This may involve developing a plan, allocating resources, and executing the solution. It is important to monitor the progress and collect feedback to learn from the implementation and make necessary adjustments.

For example, if the chosen solution to address traffic congestion is implementing a smart traffic management system, you would work with engineers and transportation authorities to develop and deploy the system. Regular evaluation and iteration of the system’s performance would ensure that it is effective and making a positive impact on reducing congestion.

By applying the problem-solving cycle derived from computer science to real-life situations, we can approach challenges with a systematic and analytical mindset. This can help us make better decisions, improve our problem-solving skills, and ultimately achieve more efficient and effective solutions.

Building Problem Solving Skills

In the field of computer science, problem-solving is a fundamental skill that is crucial for success. Whether you are a computer scientist, programmer, or student, developing strong problem-solving skills will greatly benefit your work and studies. It allows you to approach challenges with a logical and systematic approach, leading to efficient and effective problem resolution.

The Problem Solving Cycle

Problem-solving in computer science involves a cyclical process known as the problem-solving cycle. This cycle consists of several stages, including problem identification, data analysis, solution development, implementation, and evaluation. By following this cycle, computer scientists are able to tackle complex problems and arrive at optimal solutions.

Importance of Data Analysis

Data analysis is a critical step in the problem-solving cycle. It involves gathering and examining relevant data to gain insights and identify patterns that can inform the development of a solution. Without proper data analysis, computer scientists may overlook important information or make unfounded assumptions, leading to subpar solutions.

To effectively analyze data, computer scientists can employ various techniques such as data visualization, statistical analysis, and machine learning algorithms. These tools enable them to extract meaningful information from large datasets and make informed decisions during the problem-solving process.

Developing Effective Solutions

Developing effective solutions requires creativity, critical thinking, and logical reasoning. Computer scientists must evaluate multiple approaches, consider various factors, and assess the feasibility of different solutions. They should also consider potential limitations and trade-offs to ensure that the chosen solution addresses the problem effectively.

Furthermore, collaboration and communication skills are vital when building problem-solving skills. Computer scientists often work in teams and need to effectively communicate their ideas, propose solutions, and address any challenges that arise during the problem-solving process. Strong interpersonal skills facilitate collaboration and enhance problem-solving outcomes.

Mastering programming languages and algorithms
Staying updated with technological advancements in the field
Practicing problem solving through coding challenges and projects
Seeking feedback and learning from mistakes
Continuing to learn and improve problem-solving skills

By following these strategies, individuals can strengthen their problem-solving abilities and become more effective computer scientists or programmers. Problem-solving is an essential skill in computer science and plays a central role in driving innovation and advancing the field.

Questions and answers:

What is the problem solving cycle in computer science.

The problem solving cycle in computer science refers to a systematic approach that programmers use to solve problems. It involves several steps, including problem definition, algorithm design, implementation, testing, and debugging.

How important is the problem solving cycle in computer science?

The problem solving cycle is extremely important in computer science as it allows programmers to effectively tackle complex problems and develop efficient solutions. It helps in organizing the thought process and ensures that the problem is approached in a logical and systematic manner.

What are the steps involved in the problem solving cycle?

The problem solving cycle typically consists of the following steps: problem definition and analysis, algorithm design, implementation, testing, and debugging. These steps are repeated as necessary until a satisfactory solution is achieved.

Can you explain the problem definition and analysis step in the problem solving cycle?

During the problem definition and analysis step, the programmer identifies and thoroughly understands the problem that needs to be solved. This involves analyzing the requirements, constraints, and possible inputs and outputs. It is important to have a clear understanding of the problem before proceeding to the next steps.

Why is testing and debugging an important step in the problem solving cycle?

Testing and debugging are important steps in the problem solving cycle because they ensure that the implemented solution functions as intended and is free from errors. Through testing, the programmer can identify and fix any issues or bugs in the code, thereby improving the quality and reliability of the solution.

What is the problem-solving cycle in computer science?

The problem-solving cycle in computer science refers to the systematic approach that computer scientists use to solve problems. It involves various steps, including problem analysis, algorithm design, coding, testing, and debugging.

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What is Computational Thinking?

Inclusive Integration of Computational Thinking
Data Practices
Creating Algorithms
Understanding Systems with Computational Models

Computational thinking is an interrelated set of skills and practices for solving complex problems, a way to learn topics in many disciplines, and a necessity for fully participating in a computational world.

Many different terms are used when talking about computing, computer science, computational thinking, and programming. Computing encompasses the skills and practices in both computer science and computational thinking. While computer science is an individual academic discipline, computational thinking is a problem-solving approach that integrates across activities, and programming is the practice of developing a set of instructions that a computer can understand and execute, as well as debugging, organizing, and applying that code to appropriate problem-solving contexts. The skills and practices requiring computational thinking are broader, leveraging concepts and skills from computer science and applying them to other contexts, such as core academic disciplines (e.g. arts, English language arts, math, science, social studies) and everyday problem solving. For educators integrating computational thinking into their classrooms, we believe computational thinking is best understood as a series of interrelated skills and competencies.

A Venn diagram showing the relationship between computer science (CS), computational thinking (CT), programming and computing.

Figure 1. The relationship between computer science (CS), computational thinking (CT), programming and computing.

In order to integrate computational thinking into K-12 teaching and learning, educators must define what students need to know and be able to do to be successful computational thinkers. Our recommended framework has three concentric circles.

Computational thinking skills , in the outermost circle, are the cognitive processes necessary to engage with computational tools to solve problems. These skills are the foundation to engage in any computational problem solving and should be integrated into early learning opportunities in K-3.
Computational thinking practices , in the middle circle, combine multiple computational skills to solve an applied problem. Students in the older grades (4-12) may use these practices to develop artifacts such as a computer program, data visualization, or computational model.
Inclusive pedagogies , in the innermost circle, are strategies for engaging all learners in computing, connecting applications to students’ interests and experiences, and providing opportunities to acknowledge, and combat biases and stereotypes within the computing field.

A pie chart extruding from a Venn diagram to illustrate a framework for computational thinking integration.

Figure 2. A framework for computational thinking integration.

What does inclusive computational thinking look like in a classroom? In the image below, we provide examples of inclusive computing pedagogies in the classroom. The pedagogies are divided into three categories to emphasize different pedagogical approaches to inclusivity. Designing Accessible Instruction refers to strategies teachers should use to engage all learners in computing. Connecting to Students’ Interests, Homes, and Communities refers to drawing on the experiences of students to design learning experiences that are connected with their homes, communities, interests and experiences to highlight the relevance of computing in their lives. Acknowledging and Combating Inequity refers to a teacher supporting students to recognize and take a stand against the oppression of marginalized groups in society broadly and specifically in computing. Together these pedagogical approaches promote a more inclusive computational thinking classroom environment, life-relevant learning, and opportunities to critique and counter inequalities. Educators should attend to each of the three approaches as they plan and teach lessons, especially related to computing.

Figure 3. Examples of inclusive pedagogies for teaching computing in the classroom adapted from Israel et al., 2017; Kapor Center, 2021; Madkins et al., 2020; National Center for Women & Information Technology, 2021b; Paris & Alim, 2017; Ryoo, 2019; CSTeachingTips, 2021

Micro-credentials for computational thinking

A micro-credential is a digital certificate that verifies an individual’s competence in a specific skill or set of skills. To earn a micro-credential, teachers submit evidence of student work from classroom activities, as well as documentation of lesson planning and reflection.

Because the integration of computational thinking is new to most teachers, micro-credentials can be a useful tool for professional learning and/or credentialing pathways. Digital Promise has created micro-credentials for Computational Thinking Practices . These micro-credentials are framed around practices because the degree to which students have built foundational skills cannot be assessed until they are manifested through the applied practices.

Visit Digital Promise’s micro-credential platform to find out more and start earning micro-credentials today!

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2.2 Computer Science Fundamentals

Wrap your mind around computational thinking, from everyday tasks to algorithms.

Making Decisions

Computers use decision trees to turn many simple decisions into one big decision.

Searching for Solutions

Sometimes, the right way to solve a computational problem is by “brute force.”

Parallelism

When Pierre the baker wants to get lots of things done, it helps to do many things at once.

End of Unit 1

Complete all lessons above to reach this milestone.

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Resource Tradeoffs

Computer scientists deal with tradeoffs all the time. So does Farhad when he does his chores.

Order and Search

Information needs to be organized for use by humans or computers, as Tiye the librarian knows well.

Computer systems and people need to be able to reliably find and access people and resources.

Abstraction

Mayor Jing uses abstraction—a critical tool in computer science—to help her run City Hall.

Abstractions have interfaces that explain what they can and cannot do.

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Algorithms and Implementations

Algorithms are step-by-step processes for achieving an outcome. They can be very specific or quite general.

Divide and Conquer

Problems often get easier when you split them in half, as the 20 Questions guessing game shows.

Binary Search

Binary search is a more algorithm-friendly version of the 20 Questions game.

Thinking with Graphs

Graphs are a powerful tool for understanding problems and solving them in clever ways.

Representing Games and Puzzles

Graphs can help us plan solutions to complex problems, like this classic river-crossing puzzle.

Graph Search

Some of the most fundamental algorithms on graphs are designed to get you from point A to point B.

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Course description

Learn the key ideas of computer science with this interactive course – no coding required! This course is ideal for a high school or college student who wants to learn the fundamentals, or an early professional who wants to strengthen their knowledge of core computer science concepts. Whether you're exploring computer science for the first time or looking to deepen your understanding, this course will allow you to develop the problem-solving techniques you need to think like a computer scientist. Follow librarians, cooks, and mayors to see how computer science problem solving techniques affect their daily lives. Get hands-on with a few specific algorithms, and learn the general principles demonstrated by these algorithms.

Topics covered

Brute-Force Search
Concurrency
Decision Trees
Graph Abstractions
Greedy Algorithms
Programming

Prerequisites and next steps

You don’t need any previous computer science experience to take this course! This course is for anyone excited to actively learn more about how computer scientists think and understand our world.

3.1 Next Steps in Python

Boost your proficiency in Python by learning how to access social media data with public functions.

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Course info.

Prof. John Guttag

Departments

Electrical Engineering and Computer Science

As Taught In

Computer Science

Introduction to Computer Science and Programming

Lecture 3: problem solving.

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CS2104: Introduction to Problem Solving in Computer Science

This course introduces the student to a broad range of heuristics for solving problems in a range of settings. Emphasis on problem-solving techniques that aid programmers and computer scientists. Heuristics for solving problems ''in the small'' (classical math and word problems), generating potential solutions to ''real-life'' problems encountered in the profession, and problem solving in teams.

Having successfully completed this course, the student will be able to:

Identify skills and personality traits of successful problem solving.
Apply standard problem-solving heuristics to aid in problem solving.
Apply problem-solving techniques to programming activities.
Apply problem-solving techniques to school and personal interactions.
Apply pairs and team problem-solving techniques.
Generate potential solutions to problems with standard heuristics.
Formulate and successfully communicate the solutions to problems.

Prerequisites: MATH 1205 or MATH 1225 or MATH 1526.

Taught By: Alexey Onufriev Dwight Barnette Layne Watson Margaret Ellis Cliff Shaffer William McQuain

35 problem-solving techniques and methods for solving complex problems

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How do you identify problems?

How do you identify the right solution.

Tips for more effective problem-solving

Complete problem-solving methods

Problem-solving techniques to identify and analyze problems
Problem-solving techniques for developing solutions

Problem-solving warm-up activities

Closing activities for a problem-solving process.

Before you can move towards finding the right solution for a given problem, you first need to identify and define the problem you wish to solve.

Here, you want to clearly articulate what the problem is and allow your group to do the same. Remember that everyone in a group is likely to have differing perspectives and alignment is necessary in order to help the group move forward.

Identifying a problem accurately also requires that all members of a group are able to contribute their views in an open and safe manner. It can be scary for people to stand up and contribute, especially if the problems or challenges are emotive or personal in nature. Be sure to try and create a psychologically safe space for these kinds of discussions.

Remember that problem analysis and further discussion are also important. Not taking the time to fully analyze and discuss a challenge can result in the development of solutions that are not fit for purpose or do not address the underlying issue.

Successfully identifying and then analyzing a problem means facilitating a group through activities designed to help them clearly and honestly articulate their thoughts and produce usable insight.

With this data, you might then produce a problem statement that clearly describes the problem you wish to be addressed and also state the goal of any process you undertake to tackle this issue.

Finding solutions is the end goal of any process. Complex organizational challenges can only be solved with an appropriate solution but discovering them requires using the right problem-solving tool.

After you’ve explored a problem and discussed ideas, you need to help a team discuss and choose the right solution. Consensus tools and methods such as those below help a group explore possible solutions before then voting for the best. They’re a great way to tap into the collective intelligence of the group for great results!

Remember that the process is often iterative. Great problem solvers often roadtest a viable solution in a measured way to see what works too. While you might not get the right solution on your first try, the methods below help teams land on the most likely to succeed solution while also holding space for improvement.

Every effective problem solving process begins with an agenda . A well-structured workshop is one of the best methods for successfully guiding a group from exploring a problem to implementing a solution.

In SessionLab, it’s easy to go from an idea to a complete agenda . Start by dragging and dropping your core problem solving activities into place . Add timings, breaks and necessary materials before sharing your agenda with your colleagues.

The resulting agenda will be your guide to an effective and productive problem solving session that will also help you stay organized on the day!

Tips for more effective problem solving

Problem-solving activities are only one part of the puzzle. While a great method can help unlock your team’s ability to solve problems, without a thoughtful approach and strong facilitation the solutions may not be fit for purpose.

Let’s take a look at some problem-solving tips you can apply to any process to help it be a success!

Clearly define the problem

Jumping straight to solutions can be tempting, though without first clearly articulating a problem, the solution might not be the right one. Many of the problem-solving activities below include sections where the problem is explored and clearly defined before moving on.

This is a vital part of the problem-solving process and taking the time to fully define an issue can save time and effort later. A clear definition helps identify irrelevant information and it also ensures that your team sets off on the right track.

Don’t jump to conclusions

It’s easy for groups to exhibit cognitive bias or have preconceived ideas about both problems and potential solutions. Be sure to back up any problem statements or potential solutions with facts, research, and adequate forethought.

The best techniques ask participants to be methodical and challenge preconceived notions. Make sure you give the group enough time and space to collect relevant information and consider the problem in a new way. By approaching the process with a clear, rational mindset, you’ll often find that better solutions are more forthcoming.

Try different approaches

Problems come in all shapes and sizes and so too should the methods you use to solve them. If you find that one approach isn’t yielding results and your team isn’t finding different solutions, try mixing it up. You’ll be surprised at how using a new creative activity can unblock your team and generate great solutions.

Don’t take it personally

Depending on the nature of your team or organizational problems, it’s easy for conversations to get heated. While it’s good for participants to be engaged in the discussions, ensure that emotions don’t run too high and that blame isn’t thrown around while finding solutions.

You’re all in it together, and even if your team or area is seeing problems, that isn’t necessarily a disparagement of you personally. Using facilitation skills to manage group dynamics is one effective method of helping conversations be more constructive.

Get the right people in the room

Your problem-solving method is often only as effective as the group using it. Getting the right people on the job and managing the number of people present is important too!

If the group is too small, you may not get enough different perspectives to effectively solve a problem. If the group is too large, you can go round and round during the ideation stages.

Creating the right group makeup is also important in ensuring you have the necessary expertise and skillset to both identify and follow up on potential solutions. Carefully consider who to include at each stage to help ensure your problem-solving method is followed and positioned for success.

Document everything

The best solutions can take refinement, iteration, and reflection to come out. Get into a habit of documenting your process in order to keep all the learnings from the session and to allow ideas to mature and develop. Many of the methods below involve the creation of documents or shared resources. Be sure to keep and share these so everyone can benefit from the work done!

Bring a facilitator

Facilitation is all about making group processes easier. With a subject as potentially emotive and important as problem-solving, having an impartial third party in the form of a facilitator can make all the difference in finding great solutions and keeping the process moving. Consider bringing a facilitator to your problem-solving session to get better results and generate meaningful solutions!

Develop your problem-solving skills

It takes time and practice to be an effective problem solver. While some roles or participants might more naturally gravitate towards problem-solving, it can take development and planning to help everyone create better solutions.

You might develop a training program, run a problem-solving workshop or simply ask your team to practice using the techniques below. Check out our post on problem-solving skills to see how you and your group can develop the right mental process and be more resilient to issues too!

Design a great agenda

Workshops are a great format for solving problems. With the right approach, you can focus a group and help them find the solutions to their own problems. But designing a process can be time-consuming and finding the right activities can be difficult.

Check out our workshop planning guide to level-up your agenda design and start running more effective workshops. Need inspiration? Check out templates designed by expert facilitators to help you kickstart your process!

In this section, we’ll look at in-depth problem-solving methods that provide a complete end-to-end process for developing effective solutions. These will help guide your team from the discovery and definition of a problem through to delivering the right solution.

If you’re looking for an all-encompassing method or problem-solving model, these processes are a great place to start. They’ll ask your team to challenge preconceived ideas and adopt a mindset for solving problems more effectively.

Six Thinking Hats
Lightning Decision Jam
Problem Definition Process
Discovery & Action Dialogue

Design Sprint 2.0

Open Space Technology

1. Six Thinking Hats

Individual approaches to solving a problem can be very different based on what team or role an individual holds. It can be easy for existing biases or perspectives to find their way into the mix, or for internal politics to direct a conversation.

Six Thinking Hats is a classic method for identifying the problems that need to be solved and enables your team to consider them from different angles, whether that is by focusing on facts and data, creative solutions, or by considering why a particular solution might not work.

Like all problem-solving frameworks, Six Thinking Hats is effective at helping teams remove roadblocks from a conversation or discussion and come to terms with all the aspects necessary to solve complex problems.

2. Lightning Decision Jam

Featured courtesy of Jonathan Courtney of AJ&Smart Berlin, Lightning Decision Jam is one of those strategies that should be in every facilitation toolbox. Exploring problems and finding solutions is often creative in nature, though as with any creative process, there is the potential to lose focus and get lost.

Unstructured discussions might get you there in the end, but it’s much more effective to use a method that creates a clear process and team focus.

In Lightning Decision Jam, participants are invited to begin by writing challenges, concerns, or mistakes on post-its without discussing them before then being invited by the moderator to present them to the group.

From there, the team vote on which problems to solve and are guided through steps that will allow them to reframe those problems, create solutions and then decide what to execute on.

By deciding the problems that need to be solved as a team before moving on, this group process is great for ensuring the whole team is aligned and can take ownership over the next stages.

Lightning Decision Jam (LDJ) #action #decision making #problem solving #issue analysis #innovation #design #remote-friendly The problem with anything that requires creative thinking is that it’s easy to get lost—lose focus and fall into the trap of having useless, open-ended, unstructured discussions. Here’s the most effective solution I’ve found: Replace all open, unstructured discussion with a clear process. What to use this exercise for: Anything which requires a group of people to make decisions, solve problems or discuss challenges. It’s always good to frame an LDJ session with a broad topic, here are some examples: The conversion flow of our checkout Our internal design process How we organise events Keeping up with our competition Improving sales flow

3. Problem Definition Process

While problems can be complex, the problem-solving methods you use to identify and solve those problems can often be simple in design.

By taking the time to truly identify and define a problem before asking the group to reframe the challenge as an opportunity, this method is a great way to enable change.

Begin by identifying a focus question and exploring the ways in which it manifests before splitting into five teams who will each consider the problem using a different method: escape, reversal, exaggeration, distortion or wishful. Teams develop a problem objective and create ideas in line with their method before then feeding them back to the group.

This method is great for enabling in-depth discussions while also creating space for finding creative solutions too!

Problem Definition #problem solving #idea generation #creativity #online #remote-friendly A problem solving technique to define a problem, challenge or opportunity and to generate ideas.

4. The 5 Whys

Sometimes, a group needs to go further with their strategies and analyze the root cause at the heart of organizational issues. An RCA or root cause analysis is the process of identifying what is at the heart of business problems or recurring challenges.

The 5 Whys is a simple and effective method of helping a group go find the root cause of any problem or challenge and conduct analysis that will deliver results.

By beginning with the creation of a problem statement and going through five stages to refine it, The 5 Whys provides everything you need to truly discover the cause of an issue.

The 5 Whys #hyperisland #innovation This simple and powerful method is useful for getting to the core of a problem or challenge. As the title suggests, the group defines a problems, then asks the question “why” five times, often using the resulting explanation as a starting point for creative problem solving.

5. World Cafe

World Cafe is a simple but powerful facilitation technique to help bigger groups to focus their energy and attention on solving complex problems.

World Cafe enables this approach by creating a relaxed atmosphere where participants are able to self-organize and explore topics relevant and important to them which are themed around a central problem-solving purpose. Create the right atmosphere by modeling your space after a cafe and after guiding the group through the method, let them take the lead!

Making problem-solving a part of your organization’s culture in the long term can be a difficult undertaking. More approachable formats like World Cafe can be especially effective in bringing people unfamiliar with workshops into the fold.

World Cafe #hyperisland #innovation #issue analysis World Café is a simple yet powerful method, originated by Juanita Brown, for enabling meaningful conversations driven completely by participants and the topics that are relevant and important to them. Facilitators create a cafe-style space and provide simple guidelines. Participants then self-organize and explore a set of relevant topics or questions for conversation.

6. Discovery & Action Dialogue (DAD)

One of the best approaches is to create a safe space for a group to share and discover practices and behaviors that can help them find their own solutions.

With DAD, you can help a group choose which problems they wish to solve and which approaches they will take to do so. It’s great at helping remove resistance to change and can help get buy-in at every level too!

This process of enabling frontline ownership is great in ensuring follow-through and is one of the methods you will want in your toolbox as a facilitator.

Discovery & Action Dialogue (DAD) #idea generation #liberating structures #action #issue analysis #remote-friendly DADs make it easy for a group or community to discover practices and behaviors that enable some individuals (without access to special resources and facing the same constraints) to find better solutions than their peers to common problems. These are called positive deviant (PD) behaviors and practices. DADs make it possible for people in the group, unit, or community to discover by themselves these PD practices. DADs also create favorable conditions for stimulating participants’ creativity in spaces where they can feel safe to invent new and more effective practices. Resistance to change evaporates as participants are unleashed to choose freely which practices they will adopt or try and which problems they will tackle. DADs make it possible to achieve frontline ownership of solutions.

7. Design Sprint 2.0

Want to see how a team can solve big problems and move forward with prototyping and testing solutions in a few days? The Design Sprint 2.0 template from Jake Knapp, author of Sprint, is a complete agenda for a with proven results.

Developing the right agenda can involve difficult but necessary planning. Ensuring all the correct steps are followed can also be stressful or time-consuming depending on your level of experience.

Use this complete 4-day workshop template if you are finding there is no obvious solution to your challenge and want to focus your team around a specific problem that might require a shortcut to launching a minimum viable product or waiting for the organization-wide implementation of a solution.

8. Open space technology

Open space technology- developed by Harrison Owen – creates a space where large groups are invited to take ownership of their problem solving and lead individual sessions. Open space technology is a great format when you have a great deal of expertise and insight in the room and want to allow for different takes and approaches on a particular theme or problem you need to be solved.

Start by bringing your participants together to align around a central theme and focus their efforts. Explain the ground rules to help guide the problem-solving process and then invite members to identify any issue connecting to the central theme that they are interested in and are prepared to take responsibility for.

Once participants have decided on their approach to the core theme, they write their issue on a piece of paper, announce it to the group, pick a session time and place, and post the paper on the wall. As the wall fills up with sessions, the group is then invited to join the sessions that interest them the most and which they can contribute to, then you’re ready to begin!

Everyone joins the problem-solving group they’ve signed up to, record the discussion and if appropriate, findings can then be shared with the rest of the group afterward.

Open Space Technology #action plan #idea generation #problem solving #issue analysis #large group #online #remote-friendly Open Space is a methodology for large groups to create their agenda discerning important topics for discussion, suitable for conferences, community gatherings and whole system facilitation

Techniques to identify and analyze problems

Using a problem-solving method to help a team identify and analyze a problem can be a quick and effective addition to any workshop or meeting.

While further actions are always necessary, you can generate momentum and alignment easily, and these activities are a great place to get started.

We’ve put together this list of techniques to help you and your team with problem identification, analysis, and discussion that sets the foundation for developing effective solutions.

Let’s take a look!

The Creativity Dice
Fishbone Analysis
Problem Tree
SWOT Analysis
Agreement-Certainty Matrix
The Journalistic Six
LEGO Challenge
What, So What, Now What?
Journalists

Individual and group perspectives are incredibly important, but what happens if people are set in their minds and need a change of perspective in order to approach a problem more effectively?

Flip It is a method we love because it is both simple to understand and run, and allows groups to understand how their perspectives and biases are formed.

Participants in Flip It are first invited to consider concerns, issues, or problems from a perspective of fear and write them on a flip chart. Then, the group is asked to consider those same issues from a perspective of hope and flip their understanding.

No problem and solution is free from existing bias and by changing perspectives with Flip It, you can then develop a problem solving model quickly and effectively.

Flip It! #gamestorming #problem solving #action Often, a change in a problem or situation comes simply from a change in our perspectives. Flip It! is a quick game designed to show players that perspectives are made, not born.

10. The Creativity Dice

One of the most useful problem solving skills you can teach your team is of approaching challenges with creativity, flexibility, and openness. Games like The Creativity Dice allow teams to overcome the potential hurdle of too much linear thinking and approach the process with a sense of fun and speed.

In The Creativity Dice, participants are organized around a topic and roll a dice to determine what they will work on for a period of 3 minutes at a time. They might roll a 3 and work on investigating factual information on the chosen topic. They might roll a 1 and work on identifying the specific goals, standards, or criteria for the session.

Encouraging rapid work and iteration while asking participants to be flexible are great skills to cultivate. Having a stage for idea incubation in this game is also important. Moments of pause can help ensure the ideas that are put forward are the most suitable.

The Creativity Dice #creativity #problem solving #thiagi #issue analysis Too much linear thinking is hazardous to creative problem solving. To be creative, you should approach the problem (or the opportunity) from different points of view. You should leave a thought hanging in mid-air and move to another. This skipping around prevents premature closure and lets your brain incubate one line of thought while you consciously pursue another.

11. Fishbone Analysis

Organizational or team challenges are rarely simple, and it’s important to remember that one problem can be an indication of something that goes deeper and may require further consideration to be solved.

Fishbone Analysis helps groups to dig deeper and understand the origins of a problem. It’s a great example of a root cause analysis method that is simple for everyone on a team to get their head around.

Participants in this activity are asked to annotate a diagram of a fish, first adding the problem or issue to be worked on at the head of a fish before then brainstorming the root causes of the problem and adding them as bones on the fish.

Using abstractions such as a diagram of a fish can really help a team break out of their regular thinking and develop a creative approach.

Fishbone Analysis #problem solving ##root cause analysis #decision making #online facilitation A process to help identify and understand the origins of problems, issues or observations.

12. Problem Tree

Encouraging visual thinking can be an essential part of many strategies. By simply reframing and clarifying problems, a group can move towards developing a problem solving model that works for them.

In Problem Tree, groups are asked to first brainstorm a list of problems – these can be design problems, team problems or larger business problems – and then organize them into a hierarchy. The hierarchy could be from most important to least important or abstract to practical, though the key thing with problem solving games that involve this aspect is that your group has some way of managing and sorting all the issues that are raised.

Once you have a list of problems that need to be solved and have organized them accordingly, you’re then well-positioned for the next problem solving steps.

Problem tree #define intentions #create #design #issue analysis A problem tree is a tool to clarify the hierarchy of problems addressed by the team within a design project; it represents high level problems or related sublevel problems.

13. SWOT Analysis

Chances are you’ve heard of the SWOT Analysis before. This problem-solving method focuses on identifying strengths, weaknesses, opportunities, and threats is a tried and tested method for both individuals and teams.

Start by creating a desired end state or outcome and bare this in mind – any process solving model is made more effective by knowing what you are moving towards. Create a quadrant made up of the four categories of a SWOT analysis and ask participants to generate ideas based on each of those quadrants.

Once you have those ideas assembled in their quadrants, cluster them together based on their affinity with other ideas. These clusters are then used to facilitate group conversations and move things forward.

SWOT analysis #gamestorming #problem solving #action #meeting facilitation The SWOT Analysis is a long-standing technique of looking at what we have, with respect to the desired end state, as well as what we could improve on. It gives us an opportunity to gauge approaching opportunities and dangers, and assess the seriousness of the conditions that affect our future. When we understand those conditions, we can influence what comes next.

14. Agreement-Certainty Matrix

Not every problem-solving approach is right for every challenge, and deciding on the right method for the challenge at hand is a key part of being an effective team.

The Agreement Certainty matrix helps teams align on the nature of the challenges facing them. By sorting problems from simple to chaotic, your team can understand what methods are suitable for each problem and what they can do to ensure effective results.

If you are already using Liberating Structures techniques as part of your problem-solving strategy, the Agreement-Certainty Matrix can be an invaluable addition to your process. We’ve found it particularly if you are having issues with recurring problems in your organization and want to go deeper in understanding the root cause.

Agreement-Certainty Matrix #issue analysis #liberating structures #problem solving You can help individuals or groups avoid the frequent mistake of trying to solve a problem with methods that are not adapted to the nature of their challenge. The combination of two questions makes it possible to easily sort challenges into four categories: simple, complicated, complex , and chaotic . A problem is simple when it can be solved reliably with practices that are easy to duplicate. It is complicated when experts are required to devise a sophisticated solution that will yield the desired results predictably. A problem is complex when there are several valid ways to proceed but outcomes are not predictable in detail. Chaotic is when the context is too turbulent to identify a path forward. A loose analogy may be used to describe these differences: simple is like following a recipe, complicated like sending a rocket to the moon, complex like raising a child, and chaotic is like the game “Pin the Tail on the Donkey.” The Liberating Structures Matching Matrix in Chapter 5 can be used as the first step to clarify the nature of a challenge and avoid the mismatches between problems and solutions that are frequently at the root of chronic, recurring problems.

Organizing and charting a team’s progress can be important in ensuring its success. SQUID (Sequential Question and Insight Diagram) is a great model that allows a team to effectively switch between giving questions and answers and develop the skills they need to stay on track throughout the process.

Begin with two different colored sticky notes – one for questions and one for answers – and with your central topic (the head of the squid) on the board. Ask the group to first come up with a series of questions connected to their best guess of how to approach the topic. Ask the group to come up with answers to those questions, fix them to the board and connect them with a line. After some discussion, go back to question mode by responding to the generated answers or other points on the board.

It’s rewarding to see a diagram grow throughout the exercise, and a completed SQUID can provide a visual resource for future effort and as an example for other teams.

SQUID #gamestorming #project planning #issue analysis #problem solving When exploring an information space, it’s important for a group to know where they are at any given time. By using SQUID, a group charts out the territory as they go and can navigate accordingly. SQUID stands for Sequential Question and Insight Diagram.

16. Speed Boat

To continue with our nautical theme, Speed Boat is a short and sweet activity that can help a team quickly identify what employees, clients or service users might have a problem with and analyze what might be standing in the way of achieving a solution.

Methods that allow for a group to make observations, have insights and obtain those eureka moments quickly are invaluable when trying to solve complex problems.

In Speed Boat, the approach is to first consider what anchors and challenges might be holding an organization (or boat) back. Bonus points if you are able to identify any sharks in the water and develop ideas that can also deal with competitors!

Speed Boat #gamestorming #problem solving #action Speedboat is a short and sweet way to identify what your employees or clients don’t like about your product/service or what’s standing in the way of a desired goal.

17. The Journalistic Six

Some of the most effective ways of solving problems is by encouraging teams to be more inclusive and diverse in their thinking.

Based on the six key questions journalism students are taught to answer in articles and news stories, The Journalistic Six helps create teams to see the whole picture. By using who, what, when, where, why, and how to facilitate the conversation and encourage creative thinking, your team can make sure that the problem identification and problem analysis stages of the are covered exhaustively and thoughtfully. Reporter’s notebook and dictaphone optional.

The Journalistic Six – Who What When Where Why How #idea generation #issue analysis #problem solving #online #creative thinking #remote-friendly A questioning method for generating, explaining, investigating ideas.

18. LEGO Challenge

Now for an activity that is a little out of the (toy) box. LEGO Serious Play is a facilitation methodology that can be used to improve creative thinking and problem-solving skills.

The LEGO Challenge includes giving each member of the team an assignment that is hidden from the rest of the group while they create a structure without speaking.

What the LEGO challenge brings to the table is a fun working example of working with stakeholders who might not be on the same page to solve problems. Also, it’s LEGO! Who doesn’t love LEGO!

LEGO Challenge #hyperisland #team A team-building activity in which groups must work together to build a structure out of LEGO, but each individual has a secret “assignment” which makes the collaborative process more challenging. It emphasizes group communication, leadership dynamics, conflict, cooperation, patience and problem solving strategy.

19. What, So What, Now What?

If not carefully managed, the problem identification and problem analysis stages of the problem-solving process can actually create more problems and misunderstandings.

The What, So What, Now What? problem-solving activity is designed to help collect insights and move forward while also eliminating the possibility of disagreement when it comes to identifying, clarifying, and analyzing organizational or work problems.

Facilitation is all about bringing groups together so that might work on a shared goal and the best problem-solving strategies ensure that teams are aligned in purpose, if not initially in opinion or insight.

Throughout the three steps of this game, you give everyone on a team to reflect on a problem by asking what happened, why it is important, and what actions should then be taken.

This can be a great activity for bringing our individual perceptions about a problem or challenge and contextualizing it in a larger group setting. This is one of the most important problem-solving skills you can bring to your organization.

W³ – What, So What, Now What? #issue analysis #innovation #liberating structures You can help groups reflect on a shared experience in a way that builds understanding and spurs coordinated action while avoiding unproductive conflict. It is possible for every voice to be heard while simultaneously sifting for insights and shaping new direction. Progressing in stages makes this practical—from collecting facts about What Happened to making sense of these facts with So What and finally to what actions logically follow with Now What . The shared progression eliminates most of the misunderstandings that otherwise fuel disagreements about what to do. Voila!

20. Journalists

Problem analysis can be one of the most important and decisive stages of all problem-solving tools. Sometimes, a team can become bogged down in the details and are unable to move forward.

Journalists is an activity that can avoid a group from getting stuck in the problem identification or problem analysis stages of the process.

In Journalists, the group is invited to draft the front page of a fictional newspaper and figure out what stories deserve to be on the cover and what headlines those stories will have. By reframing how your problems and challenges are approached, you can help a team move productively through the process and be better prepared for the steps to follow.

Journalists #vision #big picture #issue analysis #remote-friendly This is an exercise to use when the group gets stuck in details and struggles to see the big picture. Also good for defining a vision.

Problem-solving techniques for developing solutions

The success of any problem-solving process can be measured by the solutions it produces. After you’ve defined the issue, explored existing ideas, and ideated, it’s time to narrow down to the correct solution.

Use these problem-solving techniques when you want to help your team find consensus, compare possible solutions, and move towards taking action on a particular problem.

Improved Solutions
Four-Step Sketch
15% Solutions
How-Now-Wow matrix
Impact Effort Matrix

21. Mindspin

Brainstorming is part of the bread and butter of the problem-solving process and all problem-solving strategies benefit from getting ideas out and challenging a team to generate solutions quickly.

With Mindspin, participants are encouraged not only to generate ideas but to do so under time constraints and by slamming down cards and passing them on. By doing multiple rounds, your team can begin with a free generation of possible solutions before moving on to developing those solutions and encouraging further ideation.

This is one of our favorite problem-solving activities and can be great for keeping the energy up throughout the workshop. Remember the importance of helping people become engaged in the process – energizing problem-solving techniques like Mindspin can help ensure your team stays engaged and happy, even when the problems they’re coming together to solve are complex.

MindSpin #teampedia #idea generation #problem solving #action A fast and loud method to enhance brainstorming within a team. Since this activity has more than round ideas that are repetitive can be ruled out leaving more creative and innovative answers to the challenge.

22. Improved Solutions

After a team has successfully identified a problem and come up with a few solutions, it can be tempting to call the work of the problem-solving process complete. That said, the first solution is not necessarily the best, and by including a further review and reflection activity into your problem-solving model, you can ensure your group reaches the best possible result.

One of a number of problem-solving games from Thiagi Group, Improved Solutions helps you go the extra mile and develop suggested solutions with close consideration and peer review. By supporting the discussion of several problems at once and by shifting team roles throughout, this problem-solving technique is a dynamic way of finding the best solution.

Improved Solutions #creativity #thiagi #problem solving #action #team You can improve any solution by objectively reviewing its strengths and weaknesses and making suitable adjustments. In this creativity framegame, you improve the solutions to several problems. To maintain objective detachment, you deal with a different problem during each of six rounds and assume different roles (problem owner, consultant, basher, booster, enhancer, and evaluator) during each round. At the conclusion of the activity, each player ends up with two solutions to her problem.

23. Four Step Sketch

Creative thinking and visual ideation does not need to be confined to the opening stages of your problem-solving strategies. Exercises that include sketching and prototyping on paper can be effective at the solution finding and development stage of the process, and can be great for keeping a team engaged.

By going from simple notes to a crazy 8s round that involves rapidly sketching 8 variations on their ideas before then producing a final solution sketch, the group is able to iterate quickly and visually. Problem-solving techniques like Four-Step Sketch are great if you have a group of different thinkers and want to change things up from a more textual or discussion-based approach.

Four-Step Sketch #design sprint #innovation #idea generation #remote-friendly The four-step sketch is an exercise that helps people to create well-formed concepts through a structured process that includes: Review key information Start design work on paper, Consider multiple variations , Create a detailed solution . This exercise is preceded by a set of other activities allowing the group to clarify the challenge they want to solve. See how the Four Step Sketch exercise fits into a Design Sprint

24. 15% Solutions

Some problems are simpler than others and with the right problem-solving activities, you can empower people to take immediate actions that can help create organizational change.

Part of the liberating structures toolkit, 15% solutions is a problem-solving technique that focuses on finding and implementing solutions quickly. A process of iterating and making small changes quickly can help generate momentum and an appetite for solving complex problems.

Problem-solving strategies can live and die on whether people are onboard. Getting some quick wins is a great way of getting people behind the process.

It can be extremely empowering for a team to realize that problem-solving techniques can be deployed quickly and easily and delineate between things they can positively impact and those things they cannot change.

15% Solutions #action #liberating structures #remote-friendly You can reveal the actions, however small, that everyone can do immediately. At a minimum, these will create momentum, and that may make a BIG difference. 15% Solutions show that there is no reason to wait around, feel powerless, or fearful. They help people pick it up a level. They get individuals and the group to focus on what is within their discretion instead of what they cannot change. With a very simple question, you can flip the conversation to what can be done and find solutions to big problems that are often distributed widely in places not known in advance. Shifting a few grains of sand may trigger a landslide and change the whole landscape.

25. How-Now-Wow Matrix

The problem-solving process is often creative, as complex problems usually require a change of thinking and creative response in order to find the best solutions. While it’s common for the first stages to encourage creative thinking, groups can often gravitate to familiar solutions when it comes to the end of the process.

When selecting solutions, you don’t want to lose your creative energy! The How-Now-Wow Matrix from Gamestorming is a great problem-solving activity that enables a group to stay creative and think out of the box when it comes to selecting the right solution for a given problem.

Problem-solving techniques that encourage creative thinking and the ideation and selection of new solutions can be the most effective in organisational change. Give the How-Now-Wow Matrix a go, and not just for how pleasant it is to say out loud.

How-Now-Wow Matrix #gamestorming #idea generation #remote-friendly When people want to develop new ideas, they most often think out of the box in the brainstorming or divergent phase. However, when it comes to convergence, people often end up picking ideas that are most familiar to them. This is called a ‘creative paradox’ or a ‘creadox’. The How-Now-Wow matrix is an idea selection tool that breaks the creadox by forcing people to weigh each idea on 2 parameters.

26. Impact and Effort Matrix

All problem-solving techniques hope to not only find solutions to a given problem or challenge but to find the best solution. When it comes to finding a solution, groups are invited to put on their decision-making hats and really think about how a proposed idea would work in practice.

The Impact and Effort Matrix is one of the problem-solving techniques that fall into this camp, empowering participants to first generate ideas and then categorize them into a 2×2 matrix based on impact and effort.

Activities that invite critical thinking while remaining simple are invaluable. Use the Impact and Effort Matrix to move from ideation and towards evaluating potential solutions before then committing to them.

Impact and Effort Matrix #gamestorming #decision making #action #remote-friendly In this decision-making exercise, possible actions are mapped based on two factors: effort required to implement and potential impact. Categorizing ideas along these lines is a useful technique in decision making, as it obliges contributors to balance and evaluate suggested actions before committing to them.

27. Dotmocracy

If you’ve followed each of the problem-solving steps with your group successfully, you should move towards the end of your process with heaps of possible solutions developed with a specific problem in mind. But how do you help a group go from ideation to putting a solution into action?

Dotmocracy – or Dot Voting -is a tried and tested method of helping a team in the problem-solving process make decisions and put actions in place with a degree of oversight and consensus.

One of the problem-solving techniques that should be in every facilitator’s toolbox, Dot Voting is fast and effective and can help identify the most popular and best solutions and help bring a group to a decision effectively.

Dotmocracy #action #decision making #group prioritization #hyperisland #remote-friendly Dotmocracy is a simple method for group prioritization or decision-making. It is not an activity on its own, but a method to use in processes where prioritization or decision-making is the aim. The method supports a group to quickly see which options are most popular or relevant. The options or ideas are written on post-its and stuck up on a wall for the whole group to see. Each person votes for the options they think are the strongest, and that information is used to inform a decision.

All facilitators know that warm-ups and icebreakers are useful for any workshop or group process. Problem-solving workshops are no different.

Use these problem-solving techniques to warm up a group and prepare them for the rest of the process. Activating your group by tapping into some of the top problem-solving skills can be one of the best ways to see great outcomes from your session.

Check-in/Check-out
Doodling Together
Show and Tell
Constellations
Draw a Tree

28. Check-in / Check-out

Solid processes are planned from beginning to end, and the best facilitators know that setting the tone and establishing a safe, open environment can be integral to a successful problem-solving process.

Check-in / Check-out is a great way to begin and/or bookend a problem-solving workshop. Checking in to a session emphasizes that everyone will be seen, heard, and expected to contribute.

If you are running a series of meetings, setting a consistent pattern of checking in and checking out can really help your team get into a groove. We recommend this opening-closing activity for small to medium-sized groups though it can work with large groups if they’re disciplined!

Check-in / Check-out #team #opening #closing #hyperisland #remote-friendly Either checking-in or checking-out is a simple way for a team to open or close a process, symbolically and in a collaborative way. Checking-in/out invites each member in a group to be present, seen and heard, and to express a reflection or a feeling. Checking-in emphasizes presence, focus and group commitment; checking-out emphasizes reflection and symbolic closure.

29. Doodling Together

Thinking creatively and not being afraid to make suggestions are important problem-solving skills for any group or team, and warming up by encouraging these behaviors is a great way to start.

Doodling Together is one of our favorite creative ice breaker games – it’s quick, effective, and fun and can make all following problem-solving steps easier by encouraging a group to collaborate visually. By passing cards and adding additional items as they go, the workshop group gets into a groove of co-creation and idea development that is crucial to finding solutions to problems.

Doodling Together #collaboration #creativity #teamwork #fun #team #visual methods #energiser #icebreaker #remote-friendly Create wild, weird and often funny postcards together & establish a group’s creative confidence.

30. Show and Tell

You might remember some version of Show and Tell from being a kid in school and it’s a great problem-solving activity to kick off a session.

Asking participants to prepare a little something before a workshop by bringing an object for show and tell can help them warm up before the session has even begun! Games that include a physical object can also help encourage early engagement before moving onto more big-picture thinking.

By asking your participants to tell stories about why they chose to bring a particular item to the group, you can help teams see things from new perspectives and see both differences and similarities in the way they approach a topic. Great groundwork for approaching a problem-solving process as a team!

Show and Tell #gamestorming #action #opening #meeting facilitation Show and Tell taps into the power of metaphors to reveal players’ underlying assumptions and associations around a topic The aim of the game is to get a deeper understanding of stakeholders’ perspectives on anything—a new project, an organizational restructuring, a shift in the company’s vision or team dynamic.

31. Constellations

Who doesn’t love stars? Constellations is a great warm-up activity for any workshop as it gets people up off their feet, energized, and ready to engage in new ways with established topics. It’s also great for showing existing beliefs, biases, and patterns that can come into play as part of your session.

Using warm-up games that help build trust and connection while also allowing for non-verbal responses can be great for easing people into the problem-solving process and encouraging engagement from everyone in the group. Constellations is great in large spaces that allow for movement and is definitely a practical exercise to allow the group to see patterns that are otherwise invisible.

Constellations #trust #connection #opening #coaching #patterns #system Individuals express their response to a statement or idea by standing closer or further from a central object. Used with teams to reveal system, hidden patterns, perspectives.

32. Draw a Tree

Problem-solving games that help raise group awareness through a central, unifying metaphor can be effective ways to warm-up a group in any problem-solving model.

Draw a Tree is a simple warm-up activity you can use in any group and which can provide a quick jolt of energy. Start by asking your participants to draw a tree in just 45 seconds – they can choose whether it will be abstract or realistic.

Once the timer is up, ask the group how many people included the roots of the tree and use this as a means to discuss how we can ignore important parts of any system simply because they are not visible.

All problem-solving strategies are made more effective by thinking of problems critically and by exposing things that may not normally come to light. Warm-up games like Draw a Tree are great in that they quickly demonstrate some key problem-solving skills in an accessible and effective way.

Draw a Tree #thiagi #opening #perspectives #remote-friendly With this game you can raise awarness about being more mindful, and aware of the environment we live in.

Each step of the problem-solving workshop benefits from an intelligent deployment of activities, games, and techniques. Bringing your session to an effective close helps ensure that solutions are followed through on and that you also celebrate what has been achieved.

Here are some problem-solving activities you can use to effectively close a workshop or meeting and ensure the great work you’ve done can continue afterward.

One Breath Feedback
Who What When Matrix
Response Cards

How do I conclude a problem-solving process?

All good things must come to an end. With the bulk of the work done, it can be tempting to conclude your workshop swiftly and without a moment to debrief and align. This can be problematic in that it doesn’t allow your team to fully process the results or reflect on the process.

At the end of an effective session, your team will have gone through a process that, while productive, can be exhausting. It’s important to give your group a moment to take a breath, ensure that they are clear on future actions, and provide short feedback before leaving the space.

The primary purpose of any problem-solving method is to generate solutions and then implement them. Be sure to take the opportunity to ensure everyone is aligned and ready to effectively implement the solutions you produced in the workshop.

Remember that every process can be improved and by giving a short moment to collect feedback in the session, you can further refine your problem-solving methods and see further success in the future too.

33. One Breath Feedback

Maintaining attention and focus during the closing stages of a problem-solving workshop can be tricky and so being concise when giving feedback can be important. It’s easy to incur “death by feedback” should some team members go on for too long sharing their perspectives in a quick feedback round.

One Breath Feedback is a great closing activity for workshops. You give everyone an opportunity to provide feedback on what they’ve done but only in the space of a single breath. This keeps feedback short and to the point and means that everyone is encouraged to provide the most important piece of feedback to them.

One breath feedback #closing #feedback #action This is a feedback round in just one breath that excels in maintaining attention: each participants is able to speak during just one breath … for most people that’s around 20 to 25 seconds … unless of course you’ve been a deep sea diver in which case you’ll be able to do it for longer.

34. Who What When Matrix

Matrices feature as part of many effective problem-solving strategies and with good reason. They are easily recognizable, simple to use, and generate results.

The Who What When Matrix is a great tool to use when closing your problem-solving session by attributing a who, what and when to the actions and solutions you have decided upon. The resulting matrix is a simple, easy-to-follow way of ensuring your team can move forward.

Great solutions can’t be enacted without action and ownership. Your problem-solving process should include a stage for allocating tasks to individuals or teams and creating a realistic timeframe for those solutions to be implemented or checked out. Use this method to keep the solution implementation process clear and simple for all involved.

Who/What/When Matrix #gamestorming #action #project planning With Who/What/When matrix, you can connect people with clear actions they have defined and have committed to.

35. Response cards

Group discussion can comprise the bulk of most problem-solving activities and by the end of the process, you might find that your team is talked out!

Providing a means for your team to give feedback with short written notes can ensure everyone is head and can contribute without the need to stand up and talk. Depending on the needs of the group, giving an alternative can help ensure everyone can contribute to your problem-solving model in the way that makes the most sense for them.

Response Cards is a great way to close a workshop if you are looking for a gentle warm-down and want to get some swift discussion around some of the feedback that is raised.

Response Cards #debriefing #closing #structured sharing #questions and answers #thiagi #action It can be hard to involve everyone during a closing of a session. Some might stay in the background or get unheard because of louder participants. However, with the use of Response Cards, everyone will be involved in providing feedback or clarify questions at the end of a session.

Save time and effort discovering the right solutions

A structured problem solving process is a surefire way of solving tough problems, discovering creative solutions and driving organizational change. But how can you design for successful outcomes?

With SessionLab, it’s easy to design engaging workshops that deliver results. Drag, drop and reorder blocks to build your agenda. When you make changes or update your agenda, your session timing adjusts automatically , saving you time on manual adjustments.

Collaborating with stakeholders or clients? Share your agenda with a single click and collaborate in real-time. No more sending documents back and forth over email.

Explore how to use SessionLab to design effective problem solving workshops or watch this five minute video to see the planner in action!

Over to you

The problem-solving process can often be as complicated and multifaceted as the problems they are set-up to solve. With the right problem-solving techniques and a mix of creative exercises designed to guide discussion and generate purposeful ideas, we hope we’ve given you the tools to find the best solutions as simply and easily as possible.

Is there a problem-solving technique that you are missing here? Do you have a favorite activity or method you use when facilitating? Let us know in the comments below, we’d love to hear from you!

thank you very much for these excellent techniques

Certainly wonderful article, very detailed. Shared!

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Problem Solving with Computers I

Prerequisite : Mathematics 3A or 2A with a grade of C or better (may be taken concurrently), CS 8 or Engineering 3 or ECE 3 with a grade of C or better, or significant prior programming experience. Repeat Comments : Legal repeat of CMPSC 10. Fundamental building blocks for solving problems using computers. Topics include basic computer organization and programming constructs: memory CPU, binary arithmetic, variables, expressions, statements, conditionals, iteration, functions, parameters, recursion, primitive and composite data types, and basic operating system and debugging tools.

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Computer Science Fundamentals

Free set of elementary curricula that introduces students to the foundational concepts of computer science and challenges them to explore how computing and technology can impact the world.

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Curricula at a glance

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Create programs with sequencing, loops, and events. Investigate problem-solving techniques and develop strategies for building positive communities both online and offline. Create interactive games that students can share.

Review of the concepts found in earlier courses, including loops and events. Afterward, students will develop their understanding of algorithms, nested loops, while loops, conditionals, and more.

Make fun, interactive projects that reinforce learning about online safety. Engage in more complex coding such as nested loops, functions, and conditionals.

Look at how users make choices in the apps they use. Make a variety of Sprite Lab apps that also offer choices for the user. Learn more advanced concepts, including variables and “for” loops.

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Frequently asked questions

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Home » Tech Basics » Digital Literacy » Troubleshooting Basic Computer Problems

How to Troubleshoot Basic Computer Problems to Fix Your Computer

How to Troubleshoot Computer Problems header

A big reason that discourages some people from trying to become familiar with computers and other digital technology devices is that they seem far too complex to understand. While that can be true in some respects, in a lot of other ways, it’s not. In fact, most consumer-grade digital technology is being made more accessible and intuitive all the time.

Just a heads-up that some of the services we’re reviewing here have affiliate partnerships with us, so we may earn a commission if you visit one of them and buy something. You can read more about how this works at https://techboomers.com/how-to-support-techboomers .

Part of that is making computers easier to fix when something doesn’t work correctly. And even computer troubleshooting isn’t always as difficult as you might think. To demonstrate, this article will give you some simple tips and techniques for how to fix a computer. Here’s a quick rundown of our agenda:

What is troubleshooting?

10 common computer problems and how to troubleshoot them, 7 general tips for troubleshooting computer problems.

Stick with us, and soon you’ll be a basic computer troubleshooting whiz!

“Troubleshooting” usually describes the act of fixing problems that cause machines (especially computers) to stop working, work sub-optimally, or otherwise do something irregular. Specifically, though, it actually refers to a particular problem-solving system that works through several steps.

A basic representation of the troubleshooting process looks something like this:

Identify the specific problem – Determine exactly what it is your computer is not doing that you want it to do, or doing that you don’t want it to do.
Consider relations to similar scenarios – Think about problems that you may have had with your computer before that were like your current one. Recall how those problems were solved, and consider what was the same or different in those instances.
Hypothesize and treat common causes – Brainstorm some simple things that could make your computer not act in the particular way you want it to. Check and fix these things, and then see if that stops the problem and keeps it from coming back soon afterward. If it does, you’re done! If not, proceed to step 4.
Test relevant components – If none of the common solutions work, methodically check parts (all of them, if you have to) of each system on your computer that could be responsible for the problem.
Implement a solution on the problem component – If you think you’ve narrowed down what part of your computer is causing your issue, come up with a strategy for how to fix it, and then test it out.
Verify that the issue is resolved – After working on the allegedly faulty computer component, if the problem is gone and doesn’t quickly start reoccurring (and, therefore, your computer is working normally again), you’re done! If not, go back to step 4.

Now that you know what the troubleshooting process is and how it works, let’s see it in action! Here are some common computer maladies and how to work through them.

1. My computer runs slowly.

General description:

Your computer takes a long time to boot up, programs take a long time to open, and both system and application functions take longer than usual to respond to your inputs.

Common causes:

You have too many windows open at once, or too many programs running at once.
Your computer’s registry is fragmented or corrupted.
Your computer has installed a virus or other malware program.
Your computer doesn’t have enough RAM to run all the programs you want it to.

Troubleshooting suggestions:

Close any windows and quit any programs that you aren’t immediately using.
Go to your Control Panel and uninstall old programs that you don’t use anymore.
Open your Task Manager and stop programs or processes that don’t need to be running.
Install and run an antivirus program to repair infected files.
Take your computer to a repair shop to get its RAM upgraded.

2. I’m stuck on the “Blue Screen of Death.”

A "STOP" error, or the Blue Screen of Death

This is more officially known as a “STOP error,” and it shows up when your computer’s operating system determines that it can’t run your computer safely at the moment. It has various causes, some of which are more serious than others. Be sure to read the specific error message for hints on what went wrong.

A piece of hardware on your computer is malfunctioning or is not compatible.
The software controlling a piece of hardware (i.e. “driver” or “firmware”) is out of date.
Your main hard disk doesn’t have enough free memory space available on it.
Your BIOS or other hardware settings are misconfigured.
Use System Restore to revert your computer to a state where it was working.
Install updates for your operating system, device drivers, BIOS, and other programs.
Change the settings for your BIOS and hardware back to their defaults.
Make sure all hardware components of your computer are properly installed.
Test your computer’s hardware for failures (likely RAM or hard disk ), and replace if necessary.

3. My computer won’t turn on.

Female businessperson frustrated that her computer won't turn on

You press the power button and your computer won’t turn on, or it powers up and then promptly shuts down. The good news (if you can call it that) is that this is almost always a hardware problem, so you can usually immediately rule out any software-related culprits.

One or more power switches to your computer system aren’t turned on.
Your computer’s power supply or power cord is missing, unplugged, or faulty.
One or more of your computer’s hardware components are improperly connected or faulty.
Your computer’s CMOS battery is out of power.
The power button itself isn’t working.
Make sure each switch leading to your computer’s power supply is on.
Make sure all power cables on your computer are connected and set to the correct voltage.
Remove all intermediary power sources and plug your computer directly into an outlet.
Disconnect all peripheral devices (mouse, keyboard, etc.) before trying to start your computer.
Open your computer case and make sure all hardware components are properly connected.
Replace your CMOS battery .
Replace your hard drive or motherboard.

4. My computer continually or randomly restarts or shuts down, especially while it’s booting.

Your computer randomly restarts or shuts down on you while you’re using it. Or, shortly after booting up, your computer crashes and shuts down or forces you to restart it. Like the “Blue Screen of Death” (which is sometimes associated with this problem), this can be caused by a few different things, and some of them require more work to fix than others.

Your operating system is missing a key update.
One or more of your computer’s device driver programs are out of date.
Your computer’s motherboard, RAM, and/or hard disk are damaged.

How to troubleshoot it:

Make sure you have installed all recommended update packages for your operating system.
Make sure the device drivers for your computer’s hardware components are all up-to-date.
Install, update, and run an antivirus program to repair infected files.
Take out a RAM module and start your computer. If it doesn’t work, put it back and try another.
Replace your hard drive and/or RAM modules, with an experienced repair person if necessary.

5. Parts of my computer are beeping or making strange noises.

A computer beeping as it experiences an error

You should hear minimal noise coming from your computer if it’s running optimally. But if the hard drive starts beeping, or other hardware parts start making more noise than usual, it could be a sign of a problem with your computer.

A CD/DVD-ROM or external disk drive may be trying to access files (most of which is normal).
One or more of your computer’s cooling fans are broken or working sub-optimally.
A RAM module or other piece of hardware is malfunctioning or is incorrectly installed.
The hard drive is close to failing.
The motherboard has detected some other manner of problem .
Remove any disks from your CD/DVD-ROM drive and disconnect any external disk drives.
Clean your computer fans with something, like a compressed air can, or repair or replace them.
Check the BIOS settings to make sure they’re running the fans correctly.
Remove all RAM modules and then properly re-insert them.
Remove any newly-installed hardware components and re-insert, repair, or replace them.
Back up the files on your hard drive and then get it replaced.

6. My computer is overheating.

A computer's CPU overheating until it's on fire

Parts of your computer feel abnormally hot when you touch them or put your extremities near them. This is usually a problem with your computer’s fans or other cooling systems, but that may have indirect causes as well, such as putting a bigger data load on your computer than it can handle.

The airflow through your computer’s case is restricted or blocked.
Your BIOS settings are running your computer faster than it can handle (“overclocking”).
Adjust the BIOS settings to run the fans correctly and not run your CPU faster than it can handle.
Make sure the vents to and from your computer’s fans aren’t blocked.
Install and use a program that can control the speed of your computer’s fans.

7. My computer’s peripheral devices, like the mouse and keyboard, aren’t working.

Broken computer keyboard and mouse with screwdrivers

Extra hardware devices that you attach to your computer – like a keyboard, mouse, or external disk drive – aren’t working or are acting differently than they normally do. This is mainly a connection or driver issue, but it may be something else.

One or more of your computer’s connection ports aren’t working properly.
Settings on your computer regarding the device have been improperly configured.
The component itself isn’t working due to being damaged or dirty.
Unplug the faulty device from your computer, then plug it back in (to another port, if you can).
Go to your Control Panel and change your settings (start with “Ease of Access”).
Clean the faulty device (taking it apart if you need to), or get it repaired or replaced.

8. My computer suddenly freezes while I’m using it.

Male businessman's computer repeatedly freezing

You’re using your computer normally, when all of a sudden, the screen stops moving and your input devices don’t respond. It happens to most of us every once in a while, but if it happens repeatedly, you may have a bigger problem on your hands.

A software program on your computer has experienced an error.
One or more of your computer’s processes is taking up too much memory and CPU power.
Open your Task Manager and stop programs that aren’t responding.
Open your Task Manager and stop processes that are taking up a lot of memory or CPU power.

9. I think my computer has installed a virus or other form of malware.

Concept of a virus or malware computer program

As we’ve discussed, a virus or malware program can be responsible for all sorts of nasty things that happen to your computer: it slows down, unwantedly runs or changes programs, sporadically restarts or shuts down, repeatedly crashes, and more. Fortunately, its possible causes are relatively limited, and there’s quite a bit that you can do about one.

You opened or downloaded a file that contained a malware program.
You connected a device to your computer whose files were infected with malware.
You visited a website that was infected with malware.
Disconnect all removable disk drives from your computer.
Download, install, and update the Microsoft Malicious Software Removal Tool .
Download, install, and update a leading antivirus program .
Disconnect your computer from the Internet.
Reboot your computer in Safe Mode .
Run the Malicious Software Removal Tool and the antivirus program you installed.
Re-connect to the Internet.
Make sure your computer’s built-in Microsoft Security Features are operating and up-to-date.
Know how to spot and avoid unsafe files and sites on the Internet .
Install browser extensions to detect and block malware before it reaches your computer.

10. My computer can’t maintain a connection to the Internet.

Internet connection to man's tablet interrupted

You can’t use a web browser or other Internet-related service because you can’t connect to the Internet. Or, while you’re using the Internet, you get an error message because your computer has disconnected.

Your Internet router may not be plugged in or working properly.
There may be too much distance or interference between your computer and your router.
The driver for your computer’s network card may be out of date.
Your computer’s network card may not be receiving the power it needs.
Make sure your Internet router is plugged in and working. If not, repair or replace it.
Move your computer closer to your router, and/or move obstacles out of the way.
Update the driver program for your computer’s network card.
Go to “Wireless Adapter Settings” in “Power Options” and set to “Maximum Performance.”
Call your Internet Service Provider and inquire about the problem.

If you’re not quite sure what the problem is, or none of those solutions worked for you, our tips below might help you get your computer up and running again.

1. Turn it off, and then turn it on again.

Shutting down and restarting your computer resets programs and processes that may have been experiencing errors or glitches. This will usually solve minor problems quickly and easily.

2. If you don’t know something, ask!

Write down information that you don’t quite understand as it appears while you troubleshoot, like error messages. Then, discuss it with a computer-savvy friend or family member, or look it up online. Chances are good that someone else knows what it means and what to do about it.

3. Start with simple fixes, and then rule out possibilities from there.

Save yourself time, money, and hassle by starting with the easy solutions first, then trying more complicated fixes if those don’t work.

4. Check your device connections.

Something on your computer may not be working simply because it isn’t plugged in properly, or at all. Make sure all parts are secured snugly to each other and that all sockets are working normally.

5. Boot your computer in Safe Mode.

Starting your computer in Safe Mode disables some advanced functions of your computer. If Safe Mode works fine but a normal boot-up doesn’t, you’ll know that your computer has a problem with something like its device drivers, a rogue start-up program, or a faulty peripheral device.

6. Keep your programs up-to-date.

Updating your software can not only potentially fix current computer problems, but it may prevent future ones by fixing glitches and other security vulnerabilities that can be exploited.

7. If all else fails, re-install the operating system.

This will reset everything, including (theoretically) any programs, malware, or faulty settings that were giving your computer trouble. Make sure to back up your data before you do this, though, because you’ll need to load it back onto the system afterwards.

Now you have some basic knowledge for how to fix your computer if something goes wrong with it! For more info on how to make your computer and Internet experiences as smooth as possible, visit our Internet 101 section. Or check out our entire Technology Basics category, which includes the Digital Literacy section with information and tips on mastering all things digital!

Computer Basics - Basic Troubleshooting Techniques

Computer basics -, basic troubleshooting techniques, computer basics basic troubleshooting techniques.

Computer Basics: Basic Troubleshooting Techniques

Lesson 19: basic troubleshooting techniques.

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Troubleshooting

Do you know what to do if your screen goes blank? What if you can't seem to close an application, or can't hear any sound from your speakers? Whenever you have a problem with your computer, don't panic! There are many basic troubleshooting techniques you can use to fix issues like this. In this lesson, we'll show you some simple things to try when troubleshooting, as well as how to solve common problems you may encounter.

General tips to keep in mind

There are many different things that could cause a problem with your computer. No matter what's causing the issue, troubleshooting will always be a process of trial and error —in some cases, you may need to use several different approaches before you can find a solution; other problems may be easy to fix. We recommend starting by using the following tips.

Write down your steps : Once you start troubleshooting, you may want to write down each step you take. This way, you'll be able to remember exactly what you've done and can avoid repeating the same mistakes. If you end up asking other people for help, it will be much easier if they know exactly what you've tried already.
Take notes about error messages : If your computer gives you an error message , be sure to write down as much information as possible. You may be able to use this information later to find out if other people are having the same error.

Restart the computer : When all else fails, restarting the computer is a good thing to try. This can solve a lot of basic issues you may experience with your computer.

Using the process of elimination

If you're having an issue with your computer, you may be able to find out what's wrong using the process of elimination . This means you'll make a list of things that could be causing the problem and then test them out one by one to eliminate them. Once you've identified the source of your computer issue, it will be easier to find a solution.

Let's say you're trying to print out invitations for a birthday party, but the printer won't print. You have some ideas about what could be causing this, so you go through them one by one to see if you can eliminate any possible causes.

First, you check the printer to see that it's turned on and plugged in to the surge protector . It is, so that's not the issue. Next, you check to make sure the printer's ink cartridge still has ink and that there is paper loaded in the paper tray . Things look good in both cases, so you know the issue has nothing to do with ink or paper.

Now you want to make sure the printer and computer are communicating correctly . If you recently downloaded an update to your operating system , it might interfere with the printer. But you know there haven't been any recent updates and the printer was working yesterday, so you'll have to look elsewhere.

You check the printer's USB cord and find that it's not plugged in. You must have unplugged it accidentally when you plugged something else into the computer earlier. Once you plug in the USB cord, the printer starts working again. It looks like this printer issue is solved!

This is just one example of an issue you might encounter while using a computer. In the rest of this lesson, we'll talk about other common computer problems and some ways to solve them.

Simple solutions to common problems

Most of the time, problems can be fixed using simple troubleshooting techniques, like closing and reopening the program. It's important to try these simple solutions before resorting to more extreme measures. If the problem still isn't fixed, you can try other troubleshooting techniques.

Problem: Power button will not start computer

Solution 1 : If your computer does not start , begin by checking the power cord to confirm that it is plugged securely into the back of the computer case and the power outlet.
Solution 2 : If it is plugged into an outlet, make sure it is a working outlet . To check your outlet, you can plug in another electrical device , such as a lamp .

Solution 4 : If you are using a laptop , the battery may not be charged. Plug the AC adapter into the wall, then try to turn on the laptop. If it still doesn't start up, you may need to wait a few minutes and try again.

Problem: An application is running slowly

Solution 1 : Close and reopen the application.

Problem: An application is frozen

Sometimes an application may become stuck, or frozen . When this happens, you won't be able to close the window or click any buttons within the application.

Solution 2 : Restart the computer. If you are unable to force quit an application, restarting your computer will close all open apps.

Problem: All programs on the computer run slowly

Solution 2 : Your computer may be running out of hard drive space. Try deleting any files or programs you don't need.
Solution 3 : If you're using a PC , you can run Disk Defragmenter . To learn more about Disk Defragmenter , check out our lesson on Protecting Your Computer .

Problem: The computer is frozen

Sometimes your computer may become completely unresponsive, or frozen . When this happens, you won't be able to click anywhere on the screen, open or close applications, or access shut-down options.

restarting Windows Explorer in Windows 10

Solution 3 : Press and hold the Power button. The Power button is usually located on the front or side of the computer, typically indicated by the power symbol . Press and hold the Power button for 5 to 10 seconds to force the computer to shut down.
Solution 4 : If the computer still won't shut down, you can unplug the power cable from the electrical outlet. If you're using a laptop, you may be able to remove the battery to force the computer to turn off. Note : This solution should be your last resort after trying the other suggestions above.

Problem: The mouse or keyboard has stopped working

Solution 2 : If you're using a wireless mouse or keyboard, make sure it's turned on and that its batteries are charged.

Problem: The sound isn't working

Solution 1 : Check the volume level. Click the audio button in the top-right or bottom-right corner of the screen to make sure the sound is turned on and that the volume is up.
Solution 2 : Check the audio player controls. Many audio and video players will have their own separate audio controls. Make sure the sound is turned on and that the volume is turned up in the player.
Solution 3 : Check the cables. Make sure external speakers are plugged in, turned on, and connected to the correct audio port or a USB port. If your computer has color-coded ports, the audio output port will usually be green .

Problem: The screen is blank

Solution 1 : The computer may be in Sleep mode. Click the mouse or press any key on the keyboard to wake it.
Solution 2 : Make sure the monitor is plugged in and turned on .
Solution 3 : Make sure the computer is plugged in and turned on .
Solution 4 : If you're using a desktop, make sure the monitor cable is properly connected to the computer tower and the monitor.

Solving more difficult problems

If you still haven't found a solution to your problem, you may need to ask someone else for help. As an easy starting point, we'd recommend searching the Web . It's possible that other users have had similar problems, and solutions to these problems are often posted online. Also, if you have a friend or family member who knows a lot about computers, they may be able to help you.

Keep in mind that most computer problems have simple solutions, although it may take some time to find them. For difficult problems, a more drastic solution may be required, like reformatting your hard drive or reinstalling your operating system. If you think you might need a solution like this, we recommend consulting a professional first. If you're not a computer expert, it's possible that attempting these solutions could make the situation worse.

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Problem Solving Techniques in Computer Science

Problem-solving is the process of identifying a problem and finding the best solution for it. Problem-solving is a technique that can be developed by following a well-organized approach. Every day we encounter many problems and solve them.

Every problem is different. Some problems are very difficult and are needed more attention to recognize the solution.

A problem may be solved by multiple methods. One solution may be faster, cheaper, and more reliable than others. It is important to choose a suitable worthy solution.

Different strategies, techniques, and tools are used to solve a problem. Computers are used as a tool to solve complex problems by developing computer programs.

Computer programs contain different instructions for computers. A programmer writes instructions and the computer executes these instructions to solve a problem. A person can be a good programmer if he has the skill of solving problems.

Table of Contents

Problem-Solving Techniques.

There are three different types of problem-solving techniques.

A set of instructions given to a computer to solve a problem is called a program.

A computer works according to the given instructions in the program. Computer programs are written in programming languages. A person who develops a program is called a programmer.

The programmer develops programs to instruct the computer on how to process data into information. The programmer uses programming languages or tools to write programs.

Advantages of Computer Program

Different advantages of computer programs are as follows:

A computer program can solve many problems by giving instructions to the computer.
A computer program can be used to perform a task again and again and fastly.
A program can process a large amount of data easily.
It can display the results in different styles.
The processing of a program is more efficient and less time-consuming.
Different types of programs are used in different fields to perform certain tasks.

Algorithms & Pseudo Code

An algorithm is a step-by-step procedure to solve a problem. The process of solving

problem becomes simpler and easier with help of algorithm. It is better to write an algorithm

before writing the actual computer program.

Properties of Algorithm

Following are some properties of an algorithm:

The given problem should be broken down into simple and meaningful steps.
The steps should be numbered sequentially.
The steps should be descriptive and written in simple English.

Algorithms are written in a language that is similar to simple English called pseudocode. There is no standard to write pseudo code. It is used to specify program logic in an English-like manner that is independent of any particular programming language.

Pseudocode simplifies program development by separating it into two main parts.

Logic Design

In this part, the logic of the program is designed. We specify different steps required to solve the problem and the sequence of these steps.

In this part, the algorithm is converted into a program. The steps of the algorithm are

translated into instructions of any programming language.

The use of pseudo-code allows the programmer to focus on the planning of the program. After the planning is final, it can be written in any programming language.

The following algorithm inputs two numbers calculate the sum and then displays the result on the screen.

4. Total A+B

5. Display Total

The following algorithm inputs the radius from the user and calculates the area of a circle.

Hint: Area 3.14* radius* radius)

2. Input radius in r

3. area = 3.14* r* r

4. Print area

Advantages of Algorithm

There are many advantages of an algorithm

Reduce complexity

Writing algorithm and program separately simplifies the overall task by dividing it into two simpler tasks. While writing the algorithm, we can focus on solving the problem instead of concentrating on a particular language.

Increased Flexibility

An algorithm is written so that the code may be written in any language. Using an algorithm, the program could be written in Visual Basic, Java or C++, etc.

Ease of Understanding

It is not necessary to understand a particular programming language to understand an algorithm. It is written in an English-like manner.

A flowchart is a combination of two words flow and chart. A chart consists of different symbols to display information about any program. Flow indicates the direction processing that takes place in the program.

Flowchart is a graphical representation of an algorithm. It is a way of visually presenting the flow of data, operations performed on data, and the sequence of these operations.

Flowchart is similar to the layout plan of a building. A designer draws the layout plan of the building before constructing it. Similarly, a programmer prefers to design the flowchart before writing the computer program. Flowchart is designed according to the defined rule.

Uses of Logic Flowchart

Flowchart is used for the following reasons

Flowchart is used to represent an algorithm in a simple graphical manner.
Flowchart is used to show the steps of an algorithm easily.
Flowchart is used to understand the flow of the program.
Flowchart is used to improve the logic for solving a problem.
Programs can be reviewed and debugged easily.
Chapter-Getting Started with C

Programming Languages | Language processor

Debugging in Turbo C++ | Different types of Error in C/ C++

Structured Programming Technique-Advantages and Disadvantages

Introduction to Flowchart | Difference between Flowchart and Algorithm

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Computer Science > Human-Computer Interaction

Title: an artificial neuron for enhanced problem solving in large language models.

Abstract: Recent advancements in artificial intelligence have propelled the capabilities of Large Language Models, yet their ability to mimic nuanced human reasoning remains limited. This paper introduces a novel conceptual enhancement to LLMs, termed the Artificial Neuron, designed to significantly bolster cognitive processing by integrating external memory systems. This enhancement mimics neurobiological processes, facilitating advanced reasoning and learning through a dynamic feedback loop mechanism. We propose a unique framework wherein each LLM interaction specifically in solving complex math word problems and common sense reasoning tasks is recorded and analyzed. Incorrect responses are refined using a higher capacity LLM or human in the loop corrections, and both the query and the enhanced response are stored in a vector database, structured much like neuronal synaptic connections. This Artificial Neuron thus serves as an external memory aid, allowing the LLM to reference past interactions and apply learned reasoning strategies to new problems. Our experimental setup involves training with the GSM8K dataset for initial model response generation, followed by systematic refinements through feedback loops. Subsequent testing demonstrated a significant improvement in accuracy and efficiency, underscoring the potential of external memory systems to advance LLMs beyond current limitations. This approach not only enhances the LLM's problem solving precision but also reduces computational redundancy, paving the way for more sophisticated applications of artificial intelligence in cognitive tasks. This paper details the methodology, implementation, and implications of the Artificial Neuron model, offering a transformative perspective on enhancing machine intelligence.

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Published: 24 April 2024

Energy-efficient superparamagnetic Ising machine and its application to traveling salesman problems

Jia Si ORCID: orcid.org/0000-0003-0737-4905 1 , 2 ,
Shuhan Yang 1 ,
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Electrical and electronic engineering
Magnetic devices

The growth of artificial intelligence leads to a computational burden in solving non-deterministic polynomial-time (NP)-hard problems. The Ising computer, which aims to solve NP-hard problems faces challenges such as high power consumption and limited scalability. Here, we experimentally present an Ising annealing computer based on 80 superparamagnetic tunnel junctions (SMTJs) with all-to-all connections, which solves a 70-city traveling salesman problem (TSP, 4761-node Ising problem). By taking advantage of the intrinsic randomness of SMTJs, implementing global annealing scheme, and using efficient algorithm, our SMTJ-based Ising annealer outperforms other Ising schemes in terms of power consumption and energy efficiency. Additionally, our approach provides a promising way to solve complex problems with limited hardware resources. Moreover, we propose a cross-bar array architecture for scalable integration using conventional magnetic random-access memories. Our results demonstrate that the SMTJ-based Ising computer with high energy efficiency, speed, and scalability is a strong candidate for future unconventional computing schemes.

Introduction

The demands for future data-intensive and energy-efficient computing tasks overwhelm the computational power of conventional von Neumann architectures 1 . For example, NP-hard problems are often encountered in combinatorial optimizations 2 , resource allocation 3 , cryptography 4 , finance 5 , image processing 6 , tour planning 7 , and job sequencing 8 , and their computational time and hardware resources increase exponentially with the problem size, which makes them very difficult or impossible to be solved by conventional computers in a finite time. These problems can be mapped to the Ising model, a mathematical model to characterize interactions between magnetic spins 9 . The dynamics of the model is algorithm- based, i.e. by constructing a proper coupling matrix and allowing the system to evolve utilizing an intrinsic convergence property of the Ising model, the ground state could be obtained as a solution to the corresponding problems. However, as the system might be trapped in many local minima, the annealing process has usually been adopted in Ising computers to address such limitations. It is commonly agreed that adding fluctuations prevents the Ising computer from being stuck at the local minima.

Efficient algorithms and hardware systems for finding an optimal or near-optimal solution of an Ising model at a fast speed and low power have been sought. Adiabatic quantum computing (AQC) 10 , 11 and quantum computing 12 , 13 , 14 , 15 based on superconducting qubits are capable of converging the Ising model by tunneling out of local minima to the global minima. A 100-node Maxcut problem was solved using a quantum computer of 2048 spins with huge power consumption 16 . Besides the high cost and complexity of cryogenic temperature, this proof-of-concept system was limited by the sparse connections only between the nearest neighbors, which leads to sub-optimal outcomes 17 . Simulated annealing based on CMOS implementations was exploited for parallel Ising computing, including central processing units (CPU) 18 , 19 , graphics processing units (GPU) 20 , and field-programmable gate array (FPGA) 21 , 22 . These hardware have reported as large as 16,384 spins, however, it requires huge hardware resources for generating random numbers to introduce stochasticity to escape from the local minima 4 , 18 , 23 , 24 . Coherent Ising machine (CIM) is an optical scheme with competitive energy efficiency. However, it requires a long fiber ring cavity and relies on external FPGA for implementing coupling 25 , 26 . The temporal multiplexing process is also time-consuming and hard to expand to large systems. Recently, experiments and simulation works have investigated various devices to emulate the behavior of Ising spins by taking advantage of their intrinsic physics. An 8-spin asynchronous probabilistic computer based on superparamagnetic tunnel junctions for solving integer factorization tasks of values up to 945 was demonstrated 4 . SPICE simulations of 16-city TSP using simulated annealing method were presented 27 . Other works such as 8-spin phase-transition nano-oscillators 28 , multiferroic oxide devices with a high thermal stability 29 , and magnetoresistive random access memory (MRAM) 30 , 31 have also conceptually proved that spin-based devices are suitable for representing Ising units. However, these works have encountered challenges in either partially-connected Ising spins or small scalability which limit the Ising computer from solving practical problems.

TSP discussed in this paper is a well-known problem which is much beyond the limitation of locally connected Ising models. Other combinatorial optimization problems, such as knapsack problems, coloring problems, and number partitioning, need all-to-all connection to satisfy specific constraints 9 . In practice, an additional graph embedding process is often required when mapping to 2-dimensional CMOS circuitry which only considered the coupling between adjacent spins 32 , 33 , 34 . Since the embedding increases the required number of auxiliary spins and causes spin connections to change, the annealing accuracy is degraded significantly, especially when the problem size is large. This means that supporting a fully connected Ising model is highly recommended for dealing with a wide range of problems. Another problem is the rapidly increasing connectivity when considering large-scale systems, which usually results in huge energy consumption and latency. Since the number of spins that a particular annealing processor can handle limit the scale of the problem that can be solved, how to solve complex problems with limited hardware in an energy-efficient way has also drawn significant attention.

In this work, we experimentally report a scalable Ising computer based on 80 SMTJs with all-to-all connections and successfully solve the 4761-node TSP problem. The intrinsic stochasticity in SMTJ enables ultra-fast and low-power Ising annealing without using extra resources for random number generation and Metropolis determining process 7 . By combining global annealing with intrinsic annealing in SMTJ, the convergence of the Ising problem is guaranteed especially in large-scale Ising problems. The method to determine parameters of global annealing is discussed. With an all-to-all connection among Ising spins, the combinatorial optimization of 9-city TSP is solved with the optimal solution. We further develop the algorithm for constrained TSP (CTSP) with no extra auxiliary Ising bits both in algorithm and hardware, indicating the superiority and flexibility of this Ising computer. Furthermore, we propose an optimization strategy based on graph partitioning (GP) and CTSP and experimentally solved a 70-city TSP, which typically needs 4761 nodes, on our 80-node Ising computer with a near-optimal solution. The system can obtain the lowest power consumption of 0.64 mW as well as high energy efficiency of 39 solutions per second per watt among state-of-art Ising annealers. We have experimentally demonstrated that large-scale Ising problems can be solved by small-scale hardware in an energy-efficient way.

SMTJ-based artificial Ising spin

Various NP-hard problems can be solved by constructing corresponding Ising models and observing the ground states during evolution processes. Figure 1a shows an all-to-all connected Ising model, whose Ising Hamiltonian can be written as

where $H$ is the total energy of the system, $N$ is the total number of spins, ${s}_{i}$ is the $\,i$ -th spin with one of two states; “+1” (Ising spin up) or “−1” (Ising spin down), ${J}_{i,j}$ is the coefficient of coupling between the $i$ -th and the $j$ -th spins, and ${h}_{i}$ is the external field of the $\,i$ -th spin. For a fixed configuration of other spins than ${s}_{k}$ , the probability of ${s}_{k}$ staying in the down-state is given by

where $\Lambda=\frac{\partial H}{\partial {s}_{k}}$ (see Supplementary Note 1 ).

a All-to-all connected 12-spin Ising model with s represents the spin and J 1,6 represents the coupling between s1 and s6. b Sigmoidal fit of probability of AP state ( ${p}_{{AP}}$ ) of an SMTJ under different input currents ( I ). ${p}_{{{{{{\rm{AP}}}}}}}=\frac{1}{1+{e}^{-4.672\times (I-3.905{{{{{\rm{\mu }}}}}}{{{{{\rm{A}}}}}})}}$ . Inset: diagram of an SMTJ. A tunneling barrier layer is sandwiched by a reference layer and a free layer. c Time-dependent resistance of an SMTJ under different input currents ( I ). d Photograph and schematic diagram of SMTJ-based Ising computer. The system contains 8 processing elements (PEs), 4 digital-to-analog converters (DACs), a comparator array, a multiplexer and a microcontroller unit (MCU). Each PE has 10 SMTJ computing units. Each computing unit includes a transistor and a resistor to adjust the property into stochastic. Blue lines and orange arrows represent the control and data flow, respectively.

One natural implementation of this Ising spin is based on a stochastic nanomagnet. The inset of Fig. 1b shows the sketch of an SMTJ, consisting of a tunneling barrier sandwiched by a reference layer and a free layer (see Methods section). Because of the small device diameter (~50 nm), the energy barrier of the free layer between the anti-parallel (AP) and parallel (P) states is low that the retention time of either state is in the range of μs to ms, similar to previous studies 4 , 35 . The SMTJ resistance, measured as a function of time in Fig. 1c , shows preferred AP states at high currents and P states at low currents. When the current ( I ) is ~4 μA, SMTJ shows an equal chance of AP and P states. The probability of the AP state under different input currents over 0.1 s is fitted in Fig. 1b by a sigmoid function:

where ${{{{{\rm{a}}}}}}=4.67 \, {{{{{\rm{and\; b}}}}}}=3.9 \, {{{{{\rm{\mu }}}}}}{{{{{\rm{A}}}}}}.$ In order to emulate Ising spin ${s}_{k}$ with our SMTJ device, we only need to make the probability of the down-state of ${s}_{k}$ to be equal to that for the AP state of SMTJ, namely ${p}{\_}{{{{{{\rm{\_}}}}}}{{{{{\rm{AP}}}}}}}={p}{\_}{{{{{{\rm{\_}}}}}}\downarrow }$ , with two calibration coefficients. Thus, we can derive the form of the current ${{I\_}}_{k}$ injected to SMTJ as (see Supplementary Note 1 ):

where $c=1/{kT}$ is the effective inverse temperature which can be conducted for global annealing.

Intrinsic annealing in SMTJs-based Ising computer

By integrating 80 SMTJs with a peripheral circuit and a microcontroller unit (MCU), we build an 80-node Ising computer (see Supplementary Note 2 ). Each Ising spin in Eq. ( 1 ) is emulated by an SMTJ with intrinsic randomness, where P (AP) state represents spin-up (down). Figure 1d shows the photograph of the printed circuit board (PCB) and the diagram of the system (see Methods section). The system contains 8 processing elements (PEs); each PE has 10 SMTJ computing units. Each SMTJ computing unit includes a transistor and a resistor to adjust the state of SMTJ into stochastic. During the computing process, an MCU examines the states of all SMTJs by reading the output of comparator arrays through multiplexers and generates new input voltages for digital-to-analog converters (DACs) according to the updating rule in Eq. ( 4 ) (see Supplementary Note 3 for calibration of 80 SMTJ computing units).

During the evolution process, an Ising solver could be easily trapped in a local minimum state. To avoid this non-optimal solution, annealing algorithms such as simulated annealing (SA) or quantum annealing (QA) were developed. The general idea of SA is to make the system evolve from a high temperature to a low temperature gradually 7 . The convergence and relaxation of SA can be mathematically provable 36 . During each iteration, a random number is generated for stochasticity and introduced to determine whether the result in this iteration should be accepted or not. In QA, quantum fluctuations cause quantum tunneling between states 17 . In both SA and QA, stochasticity needs to be introduced into the annealing process. In contrast, our Ising system utilizes the intrinsic stochastic behaviors of SMTJ to perform the Metropolis process of standard SA in hardware, which greatly saves the solution time and hardware resources for generating randomness (see Supplementary Note 4 ). Besides, our Ising computer has an all-to-all connection which has wider application scenarios, as well as a better capability of escaping from local minima.

Ising mapping of N-city TSP and CTSP

We have applied our Ising computer to the TSP problem, one of the combinatorial optimization problems, which applies to various sectors, such as vehicle routing, logistics, planning, and scheduling. The goal is to find the shortest route that visits all listed cities once and only once given distances between the cities in the list. In order to solve this problem, we first map N -city-TSP to an ${N}^{2}$ -spin Ising model, or ${(N-1)}^{2}$ -spin model assuming a fixed starting city. Figure 2a shows the coordinates of 9 cities and Fig. 2b shows the 81-spin Ising model, whose rows indicate the cities and columns indicate the visiting order. We define the binary spin, s , as ${s}_{i,j}$ = 1 if city i is visited as j -th city or ${s}_{i,j}$ = −1 otherwise. The total Hamiltonian of TSP is expressed by 9

where the first term is a constraint that represents only one city is visited at the j -th visit, and the second term represents one city is visited only one time. $w$ is a constant small enough ( $0 \, < \, w \, < \, 1$ ) not to violate the two constraints of the TSP cycle. ${d}_{i,{i{{\hbox{'}}}}}$ is the distance between city $i$ and city ${i{{\hbox{'}}}}$ . According to Eqs. ( 1 ) and ( 5 ), coupling matrix $J$ of 81 spins could be obtained, as shown in Fig. 2c (see Supplementary Note 5 ). It shows that spins in the same row or column have strong coupling, as indicated by the first two terms in Eq. ( 5 ).

a Coordinates of all 9 cities used in this problem which are the first 9 cities in the dataset Burma14 from TSPLIB. b Ising spin representation for 9-city TSP (81 spins). Rows indicate names of cities and columns indicate the visiting order. Each spin can be 1 (visited) or −1 (not visited) in each iteration. c Color map of the coupling matrix J TSP of 9-city TSP, and the color bar represents an effective energy with the unit of kT . Here, k is the Boltzmann constant and T is the temperature. d Constrained TSP (CTSP) with a fixed vising sequence from city 2 to city 7 or from city 7 to city 2. The arrows represent the visiting sequence. e The Ising spin representation for CTSP with the fixed visiting sequence in d . Arrows represent possible vising sequences. f Color map of the difference of coupling matrix between TSP (J TSP ) in a and CTSP (J CTSP ) in d . Arrows represent the fixed vising sequences from city 2 to city 7 or from city 7 to 2.

We define CTSP as the visiting orders of some cities are enforced during the traveling. This is quite useful in real-life scenarios. For example, a delivery man collects food and drinks at shop A and must deliver hot drinks to B first even though the total cost is higher than optimal. We propose an algorithm for solving CTSP by adding negative “distance” to the Hamiltonian. For example, suppose that city A and city B are required to be connected in the CTSP as city 2 and city 7 shown in Fig. 2d , and then we add the term

such that the energy of a path, where city A and city B are connected, is always lowered by $\theta .$ When $\theta$ is sufficiently large, the optimal path must have city 2 and city 7 connected. Thus, the total Hamiltonian of the CTSP is expressed by

Constructing an Ising model for CTSP is exactly the same as TSP except for extra allowed visiting sequences, as shown in Fig. 2e . This would lead to a modification of the coupling matrix of $J$ according to Eq. ( 7 ) (see the deduction of ${J}_{{CTSP}}$ in Supplementary Note 6 ). From Fig. 2f we can clearly see the differences between ${J}_{{CTSP}}$ and ${J}_{{TSP}}$ . This algorithm of CTSP fits for arbitrary constraints of visiting sequences as well as their combinations.

Experimental demonstration of 9-city TSP

We first run a 9-city TSP in the 80 SMTJ-based Ising computer at a relatively low but non-zero effective temperature to examine the intrinsic annealing in SMTJ. The iteration time is set comparable to the longest retention time of SMTJs to avoid reading previous spin states. In our experiments, we set the iteration time as 0.1 ms. As shown in Fig. 3a , as the effective inverse temperature ( c ) is increased quickly to 0.5, the system converges rapidly to a low energy state within 50 iterations and reaches the ground state after 4000 iterations. It should be noted that the intrinsic stochasticity in SMTJs helps the system escape from local minima without an extra annealing process, as shown in the right inset of Fig. 3a . Figure 3b illustrates the evolution of 9 spins out of 81 spins. The evolution of all 81 spins can be found in Supplementary Note 7 .

a Total energy transition of 9-city TSP with 5000 iterations (the optimal solution with the energy of 18.23 corresponds to the dashed horizontal line). Insets: effective inverse temperature ( c ) and total energy within 3500–4500 iterations. b Evolution of 9 representative SMTJ states in 5000 iterations. An offset is used in the y -axis to show each SMTJ clearly. c Visiting routes of state A, B, C, and D in a . d Corresponding Ising spins of state A, B, C, and D in a . The yellow squares represent ‘visited ( ${s}_{i,j}=1$ )’ and the purple squares represent ‘not visited ( ${s}_{i,j}=-1$ )’. e Total energy transition with increasing c from 0.2 to 1.8. Left inset: zoom-in view of total energy transition with increasing c from 0.392 to 0.52. Right inset: transition of c with iterations. The red dashed line represents the optimal path (success). f Success probability of solving TSP with varying the node size. The data points and shadows represent the median value and the interquartile range (IQR), respectively.

We choose four states in Fig. 3a to inspect the traveling path in Fig. 3c and their Ising spins, namely ${s}_{i,j}$ , as shown in Fig. 3d . The yellow square in Fig. 3d represents ${s}_{i,j}=1$ (visited) and the blue square represents ${s}_{i,j}=-1$ (not visited). In an initial state A, the spin states are randomly set and then converge to a relatively low energy at state B. State C is an intermediate solution during the annealing process. State D is the optimal solution satisfying two constraints of the TSP. Because we anneal the system to a relatively low but non-zero temperature so that the convergence to a sub-optimal state could be guaranteed, and at the same time, the intrinsic randomness in SMTJ helps the system to escape from local minima and find a ground state quickly. We test 10 different random initial states each with 5000 iterations and find that in all cases the system can obtain a relatively small energy, as shown in Supplementary Note 8 . However, there is a probability that the system jumps out of the ground state because of the non-zero temperature. If we continue to observe the evolution in a large timescale, the system would move back to the global minimum state. In some cases, where the speed and near-optimal solution matter but the accurate optimal solution is not, the number of iterations can be chosen to be small.

Further global annealing of the system to a lower effective temperature may guarantee the convergence of the computation. Here we use linear annealing as an example to examine the convergence of this algorithm in a very large-iteration limit. The initial temperature should be chosen sufficiently high to ensure that the thermal energy exceeds any energy barrier ( $\Delta H{=H}_{\max }-{H}_{\min }$ ) within the system, while still adhering to the fundamental constraints of the specific Ising model. For a given N-city TSP, ${H}_{\max }$ in Eq. ( 5 ) can be estimated as $w\times N\times \bar{d}$ , assuming that the distance between any two cities is the same as the average distance $\bar{d}$ . Similarly, ${H}_{\min }$ can be estimated as $w\times N\times {d}_{\min }$ . Therefore, the initial $c$ of 9-city TSP in our experiment can be estimated as ${c}_{{{{{{\rm{initial}}}}}}}\, \sim 1/\Delta H=0.07$ , where $w=0.5$ , $N=9$ for a total of 9 cities, $\bar{d}=4$ and ${d}_{\min }=0.8$ for the average and shortest distance of each two cities, respectively in Fig. 3c . We then choose ${c}_{{{{{{\rm{initial}}}}}}}$ = 0.2 which is sufficiently safe for annealing. As the temperature linearly decreases, the dynamical system gradually stabilizes. The final temperature should be low enough i.e., ${c}_{{{{{{\rm{final}}}}}}} \, \gg \, 1/\Delta H$ , to freeze all possible fluctuations. Here we set ${c}_{{{{{{\rm{final}}}}}}}=1.8$ which is at least one order larger than $1/\Delta H$ . This can also be verified by observing randomly generated states under ${c}_{{{{{{\rm{final}}}}}}}$ for long iterations. Regarding the annealing speed, if several changes in the spin configuration are observed under each value of c , then this annealing speed is valid. Plenty trials are required to find the proper annealing speed (details in Supplementary Note 8 ).

In Fig. 3e we can find the first global minimum energy appears after 16,500 iterations, and converge to the ground state after 40,000 iterations. Temperature schedules can be optimized to reduce iteration numbers, e.g. increase the effective temperature in the first few time steps, and then decrease gradually, or learned by the reinforcement learning method 37 . In practice, we use one memory to store the minimum energy state during the computation, and another memory to record the final energy state. We take the minimum value of these two results as the solution. Figure 3f shows the success probability (defined as finding the optimal path) of TSP with various node sizes. The success probability of 9-city TSP reaches 95% after 10 4 iterations. The success probability with the parameter $w$ in Eq. ( 5 ) which determines the relative strength of the constrain term and distance term is also discussed. If the $w$ is too large, then the probabilities of violations, namely the invalid path, would increase, as shown in Supplementary Note 8 . If $w$ is too small, then the effect of the distance term is small, which results in a slower convergence to the ground state.

The advantages of this annealer are threefold: (1) Selective working modes by using different temperature schemes. One is the probabilistic sampling mode working at a constant temperature, which is similar to an asynchronous probabilistic computer 4 ; the other is the annealing mode conducted by reducing the effective temperature. (2) Fast speed and low power consumption to find the ground state because of the intrinsic annealing properties in SMTJ. (3) Global annealing outperforms probabilistic sampling in achieving efficient convergence, especially for large-scale problems.

We have implemented a synchronous design with a lower requirement on the speed of peripheral circuits. This design also effectively mitigates issues such as leakage, sneak currents, and parasitic resistances which might encountered in asynchronous hardware with a memristive (or resistive) crossbar array.

Compressing 70-city TSP to 80-node Ising computer

Generally, the number of spins required for an N -city TSP is ( N -1) 2 , which limits the scalability of TSP on state-of-the-art computing systems. Here, we propose a graph Ising compressing algorithm based on CTSP that can significantly reduce the number of spins and interactions for solving a TSP. Figure 4a is an example of how we apply this algorithm to our 80-node SMTJ Ising computer for solving a 70-city TSP (4761 nodes, st70 data set from TSPLIB 38 ). The major steps of this algorithm can be described as follows: (a) divide the cities into several smaller groups until the number of cities in each group is less than 10 by GP method; (b) solve TSP within each group separately; (c) integrate neighboring groups to obtain an initial path of the whole group; and (d) optimize the path in (c) by a CTSP window sliding over the whole map.

a Optimization algorithm for 70-city TSP. b Number of required SMTJs for various problems using different methods. Burma14, berlin52, eil76, and eil101 are TSP of 14, 52, 76, and 101 cities, respectively. c Comparison of total Ising energy (path) and total clock cycles for final solution with different SA-based algorithms, including symbiotic organisms search 40 , ant colony optimazation 41 , multi-offspring genetic algorithm 42 , and gene-expression programming 7 . Our method is tested on our Ising system and others are tested on Intel Core-i7 PC. In this comparison, our system runs at a main frequency of 10 kHz.

It is worth mentioning that GP is also an Ising problem. When converting a global TSP into local TSPs, using GP would be more hardware-friendly for our Ising computer compared to other clustering algorithms. It is based on the idea that the original graph can be separated into multiple sub-graphs depending on the Euclidean distance. The number of spins required for solving GP is ~ N and thus, GP is quite efficient for local TSPs since the problem size can be reduced to ~ ${\left(N-1\right)}^{2}/a$ , where $a$ is the number of groups, and each TSP can be optimized independently (see GP mapping in Supplementary Note 9 ).

The final step (d) is based on CTSP, where a rectangular window slides over the path and cuts it into several disconnected lines, among which the two longest lines are chosen and the edge cities are connected as a circular path (Supplementary Note 10 ). The CTSP is solved within each window for sub-area optimization without changing the visiting order of edge cities. After this, the two lines at the edge cities are opened and CTSP is carried out again after sliding to the next window. GP-CTSP-based optimization algorithm provides an efficient way of finding near-optimal solutions for large-scale TSP on limited hardware resources.

Figure 4b shows the comparison of numbers of spins for different TSPs by a conventional Ising method 9 , cluster Ising method 39 , and our method. The required number of spins in our method is relatively unchanged for various TSPs, while that of other methods increases substantially with the scale of the problem. Figure 4c shows the total path of 70-city TSP as a function of iteration number using different SA-based algorithms, including symbiotic organisms search 40 , ant colony optimization 41 , multi-offspring genetic algorithm 42 , and gene-expression programming 7 . Finally, we obtain the near-optimal path with a total energy of 700.71, which is slightly higher than the optimal solution of 675. However, the iteration number for an optimized solution is 4.9 $\times$ 10 6 by our method, which is two to three orders lower than that of SA-based algorithms running on Intel Core-i7 CPU 7 with the main frequency of 3 GHz, as shown in Fig. 4c .

Ising computer scaling and cross-bar architecture

The above experimental demonstration shows our Ising computer with 80 SMTJs is capable of finding a near-optimal solution to a medium-scale NP-hard problem. We then explore the performance with increasing from 70 to 200 cities. The simulation of complete TSP task is carried out using MATLAB, incorporating a stochastic model of the SMTJ employed in our experiment (details in Supplementary Note 11 ). The solution quality is defined as

Figure 5a illustrates the solution quality of the best results obtained for each TSP task (Supplementary Note 12 for the best solutions). Notably, as the number of SMTJ (M) increases, higher quality solutions can be attained. It is worth emphasizing that the shortest path obtained for the 101-city TSP is 640.9755 in our study, surpassing the optimal path of 642.3095 provided by TSPLIB (Eil101.opt.tour). This outcome serves as evidence of the superiority of our method. The utilization of more SMTJs solving TSP per sliding window leads to improved optimization of CTSP annealing, resulting in an enhanced solution quality, as depicted in Fig. 5b . Consequently, the time to convergence s would also increase with the use of more SMTJS. When dealing with a fixed hardware capacity, an appropriate number of SMTJs for CTSP optimization can be assigned, taking into account both the solution quality and convergence speed. Figure 5c showcases the success rate (defined as achieving 95% solution quality) as the problem size increases. The success probability of 200-city TSP, whose complexity is ~40,000 nodes, can reach as high as 90%, demonstrating the scalability of our method compared to typical TSP (without GP and CTSP) 9 .

a Solution quality of various problems using different number of SMTJs (M) in the array. The datasets used are St70, Eil101 and KroA200, for 70, 101 and 200 cities, respectively. b Total length of KroA200 TSP at different convergence speeds using different number of SMTJs. The dashed line represents the best demonstrated solution. c Success probability of different TSP algorithm (without/with GP and CTSP) as the number of cities increases after running for 50 times. A total of 512 SMTJs are used. Here we define the success as achieving the solution quality of 95%. d SMTJ cross-bar array which contains row decoder, SMTJ, select transistor and read sense amplifier (RSA). BL represents bit line, WL represents word line, Vin, Vout and Vdd represent the input voltage, output voltage and supply voltage of RSA. e Circuit of one RSA which contains a current mirror, voltage equalization circuit (VEC, with a control signal of EQ which initializes the voltages in Q and QB points, under a reference voltage of Vdd/2), voltage sense amplifier (VSA, with a control signal of SEN), reference resistance ( ${{{{{\rm{Rref}}}}}}=\frac{1}{2}({{{{{\rm{Rap}}}}}}+{{{{{\rm{Rp}}}}}})$ , Rap and Rp represent SMTJ’s resistance in AP and P state respectively), and control transistors. f Signals of writing/reading two adjacent SMTJ cells in one BL, selected by WL0 and WL1 in sequence. All signals are defined in e and f .

We also propose a cross-bar architecture for large-scale Ising computer implementation, which can be integrated by using modern MRAM and CMOS technologies. The core part of this architecture consists of SMTJ bit cells organized as a cross-bar array, integrated with row decoders and read sense amplifiers (RSA), as shown in Fig. 5d . Each SMTJ bit cell contains one select transistor and one SMTJ (1T1SMTJ), whereas the gate of the select transistor is driven by word lines (WL), and the source of all bit cells are connected to the ground. Each bit line is assigned with an RSA. The current flows through SMTJ can be continuously adjusted by Vin of RSA, and the state of SMTJ can be read by RSA at the same time. Figure 5e illustrated the circuit of RSA, in which two clamp transistors control the current flow through the bit cell path and reference path by the gate voltage (Vin), and a current mirror is used to guarantee the same current of the above two paths. Then different voltages would show in the Q and QB point when the resistance of SMTJ is higher or lower than the reference resistor (Rref). By utilizing an enabled voltage sense amplifier (VSA), the voltages at the Q and QB points are sensed, allowing the SMTJ state to be determined as either Vdd (P state) or 0 V (AP state). Particularly, a voltage equalization circuit (VEC) is designed for initializing VSA to avoid incorrect readout. Electrical coupling through a resistance change 43 is evaluated to have neglectable effects (details in Supplementary Note 11 ). Figure 5f shows the signals to control and read bit cells. In phase 0 (PH0), one row of SMTJs is selected by WL, and Vin prepared by peripheral circuit is applied to the corresponding RSA. EQ is set high to initialize Q, QB and Vout as Vdd/2. In phase 1 (PH1), the SMTJ fluctuates from the falling edge to next rising edge of EQ. Finally, in phase 2 (PH2), RSAs read the data of one row in parallel at the falling edge of SEN. After the first row has been retrieved, the partial sum starts to be computed. Meanwhile, the same process for the second row can be started, so and so forth. To avoid reading the previous state, the duration of PH1 is preferred to be comparable with the retention time of SMTJ, which limits the main frequency of the system (see details in Supplementary Note 11 ).

We compare our system with other state-of-art Ising solvers, including CMOS annealer (Intel Core i7 processor) 7 , quantum annealer (D-Wave 2000Q) 16 , 17 , CIM with FPGA 26 , memristor Hopfield neural networks (mem-HNN) 44 , and phase-transition nano-oscillators (PTNO) 28 in solving 4761-node TSP70, as shown in Table 1 . We use the experimental data for benchmarking from literature, and two kinds of SMTJs for comparison. One is our perpendicular anisotropy SMTJ device and the other is assuming recently reported in-plane anisotropy SMTJ with a retention time of 8 ns 45 , 46 . The major attributes are the main frequency (defined as 1/iteration time), power, time-to-solution as well as energy efficiency (defined as solutions per second per watt). As quantum computers, CIM, mem-HNN, and PTNO only demonstrated ~100-node max-cut problems, we estimate the time-to-solution for solving TSP70 by assuming that the algorithm and the total number of spins to find a near-optimal solution is the same as our work (details in Supplementary Note 13 ). Here, we set 80-spin Ising computer as a standard and fix the number of iterations of 400,000 for a good solution to TSP70. Only Ising computing parts are calculated for power consumption.

In Table 1 , although the main frequency of CPU is the highest among all candidates, the energy efficiency is lower than our SMTJ-based approach. This is due to the redundant logic and data transfer delay between the memory and PEs in a conventional von-Neumann architecture. The SMTJ-based approach currently outperforms the quantum annealer both in the power consumption as well as time to solution. The power of quantum annealer is huge which needs to be optimized further for real applications. CIM is another promising architecture with a fast speed and acceptable power consumption. Current CIM systems are proof-of-concept systems which are not at present optimized for energy efficiency. Mem-HNN has a relatively fast speed assuming the 180-nm CMOS technology. However, the required number of devices is large, which limits the integrated density. The PTNO approach uses capacitors or resistors to mimic spin coupling, whose main frequency would be limited by the system scale and parasitic effects. It is reported that the ideal main frequency would decrease from 500 to 87 MHz when the system scale increases from 8-node to 100-node 28 . Our SMTJ-based Ising computer outperforms other approaches with low power consumption with 0.64 mW (details in Supplementary Note 13 ).

We experimentally demonstrate perpendicular MTJs with a retention time of ~0.1 ms and solve TSP70 Ising problems at an energy efficiency of 39 solutions per second per watt. Furthermore, we simulate an Ising computer with 4 Kb SMTJs using 40 nm commercial CMOS technology. The simulated energy efficiency for solving TSP70 by using the same SMTJ can reach 68 solutions per second per watt. By using reported in-plane SMTJ 45 and advanced CMOS, the system could obtain the highest energy efficiency of $5.4\times {10}^{3}$ , which shows several orders of magnitude improvement over other approaches. This result suggests that an SMTJ-based Ising computer can be a good candidate for solving dense Ising problems in a highly energy-efficient and fast way.

In summary, we have experimentally demonstrated an intrinsic all-to-all Ising computer based on 80 SMTJs, and solved 9-city TSP with the optimal solution. Furthermore, a compressing strategy based on CTSP and GP is proposed to experimentally solve 4761-node 70-city TSP on an 80-node system with a near-optimum solution as well as ultra-low energy consumption. A cross-bar architecture is then proposed for large-scale Ising computers and the 200 city TSP task is simulated. Our system provides a feasible solution to fast, energy-efficient, and scalable Ising computing schemes to solve NP-hard problems.

Sample growth and device fabrication

Thin film samples of substrate/[W (3)/Ru (10)] 2 /W (3)/Pt (3)/Co (0.25)/Pt (0.2)/[Co (0.25)/Pt (0.5)] 5 /Co (0.6)/Ru (0.85)/Co (0.6)/Pt (0.2)/Co (0.3)/Pt (0.2)/Co (0.5)/W (0.3)/CoFeB (0.9)/MgO (1.1)/CoFeB (1.5)/Ta (3)/Ru (7)/Ta (5) were deposited via DC (metallic layers) and RF magnetron (MgO layer) sputtering on the Si substrates with thermal oxide of 300 nm with a base pressure of less than $2\times {10}^{-8}$ Torr at room temperature. The numbers in parentheses are thicknesses in nanometers. To fabricate the superparamagnetic tunnel junctions, bottom electrode structures with a width of 10 µm were firstly patterned via photolithography and Ar ion milling. MTJ pillar structures with a diameter of ~50 nm for the superparamagnetic behavior were patterned by using e-beam lithography. The encapsulation layer of Si 3 N 4 was in-situ deposited after ion milling without breaking vacuum by using RF magnetron sputtering, and top electrode structures with a width of 10 µm were patterned via photolithography and top electrodes of Ta (5 nm)/Cu (40 nm) were deposited by using DC magnetron sputtering.

MTJ characterization by probe station

The setup includes a source meter (Keithley 2400) for supplying DC bias currents and a data acquisition card (NI-DAQmx USB-6363) for the read operation. A single SMTJ operation cycle comprises two steps (i.e. bias and read). A small DC input current with an amplitude of 1–20 μA is applied to SMTJ. Simultaneously, the DAQ card reads the voltage signal across the SMTJ at a maximum sampling rate of 2 MHz. The MTJ switching probability varies in accordance with the amplitude of applied currents. The retention time of MTJ is determined from random telegraph noise measurements over 250 ms. The expectation values of event time τ is determined by fitting an exponential function to the experimental results.

80 SMTJ arrays and peripheral circuits are integrated on a 12 cm × 15 cm PCB, controlled by an MCU (Arduino Mega 2560 Rev3). Four 12-bit rail-to-rail DACs (AD5381) with 160 output channels in total are used to generate analog DC inputs for PE and comparator arrays. Half of the DAC output channels are used to provide stimulation to the gate terminal of NMOSs (2N7002DW-G), and others are used to provide reference voltages to comparators (AD8694). The drain voltages of NMOS are compared with reference voltages and generate outputs in parallel. Outputs of comparator arrays are read by MCU through four multiplexers (FST16233) and then are calculated to obtain new inputs for DACs. The supply voltage of the PCB board and SMTJs is 5 V and 0.8 V, respectively. The value of resistors in each computing unit can be designed to adjust the center of sigmoidal curves.

Data availability

The data generated during this study are available within the article and the Supplementary Information file. Source data are provided with this paper.

Code availability

The codes that support this study can be available from the corresponding author upon request.

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Acknowledgements

This work was supported by National Research Foundation (NRF), Prime Minister’s Office, Singapore, under its Competitive Research Programme (NRF-000214-00 to H.Y.), Advanced Research and Technology Innovation Center (ARTIC to H.Y.), the National University of Singapore under Grant (project number: A-0005947-19-00 to H.Y.), and Ministry of Education, Singapore, under Tier 2 (T2EP50123-0025 to H.Y.). We thank Yuqi Su, and Chne-Wuen Tsai from National University of Singapore and Zhi-Da Song from Peking University for useful discussions.

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Jia Si, Shuhan Yang, Yunuo Cen, Jiaer Chen, Yingna Huang, Zhaoyang Yao, Dong-Jun Kim, Kaiming Cai, Jerald Yoo, Xuanyao Fong & Hyunsoo Yang

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J.S. and H.Y. conceived and designed the experiments. J.S. designed, fabricated, and coded the hardware system. D.K., and S.Y. fabricated the devices. J.S., S.Y., and K.C. performed device measurements. Z.Y. bonded the components on PCB. J.S. designed SMTJ-based Ising system. J.S., J.C., Y.C., Y.H. and X.F. developed the optimization algorithm and performed simulations. J.S., S.Y., Y.C., J.Y., X.F. and H.Y. analyzed the data. J.S. and H.Y. wrote the manuscript. H.Y. proposed and supervised this work. All authors discussed the results and revised the manuscript.

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Si, J., Yang, S., Cen, Y. et al. Energy-efficient superparamagnetic Ising machine and its application to traveling salesman problems. Nat Commun 15 , 3457 (2024). https://doi.org/10.1038/s41467-024-47818-z

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Storing and utilizing energy with innovative sulfur-based cathodes

by Kathrin Anna Kirstein, Humboldt-Universität zu Berlin

Electric vehicles and portable electronic devices such as laptops and mobile phones are unthinkable without lithium-ion batteries. The problem is highly toxic materials such as cobalt are often used for the cathodes of these batteries, which jeopardize the environment and the health of people in the countries where they are mined. In addition, the reserves of these metals are very limited.

A research team at Humboldt-Universität zu Berlin (HU) has now achieved a decisive breakthrough in battery technology. The team, led by Prof Dr. Michael J. Bojdys, has developed a high-performance sulfur-based cathode.

Sulfur is a sustainable alternative to the materials commonly used in lithium-ion batteries because it is less toxic and—unlike cobalt—is abundant. However, the storage capacity of batteries in which sulfur is used as a cathode material has so far declined rapidly.

The researchers have now been able to solve this problem. The results of the study have been published in Angewandte Chemie .

"Our development paves the way for sulfur electrodes as a viable alternative to conventional metal-based cathodes. It could fundamentally change the way we store and use energy and represents an important step towards a more sustainable future," explains Prof Bojdys.

Solving the sulfur-shuttle problem using polymer chemistry

With sulfur-based cathodes, the mobility of the sulfur has so far led to a degradation of the battery—an effect known as the sulfur-shuttle. In the newly developed solution, the sulfur is encapsulated in a special microporous polymer network so that the sulfur particles are retained. This battery technology not only increases the performance and service life of batteries but also avoids the problem of scarce resources.

Prof Dr. Michael J. Bojdys is an expert in sustainable energy materials and is helping to transform the chemical industry in the Berlin capital region by combining science and industry to create a circular economy based on sustainable raw materials.

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Investigate problem-solving techniques and develop strategies for building positive communities both online and offline. Create interactive games that students can share. See course details ... Learn to create computer programs, develop problem-solving skills, and work through fun challenges! Make games and creative projects to share with ...
How to Troubleshoot Basic Computer Problems to Fix Your Computer
Unplug the faulty device from your computer, then plug it back in (to another port, if you can). Go to your Control Panel and change your settings (start with "Ease of Access"). Clean the faulty device (taking it apart if you need to), or get it repaired or replaced. 8.
Computer Basics: Basic Troubleshooting Techniques
Restart the computer: When all else fails, restarting the computer is a good thing to try. This can solve a lot of basic issues you may experience with your computer. Using the process of elimination. If you're having an issue with your computer, you may be able to find out what's wrong using the process of elimination.This means you'll make a list of things that could be causing the problem ...
What is Problem Solving? Steps, Process & Techniques
Finding a suitable solution for issues can be accomplished by following the basic four-step problem-solving process and methodology outlined below. Step. Characteristics. 1. Define the problem. Differentiate fact from opinion. Specify underlying causes. Consult each faction involved for information. State the problem specifically.
Problem Solving Techniques in Computer Science
Computers are used as a tool to solve complex problems by developing computer programs. Computer programs contain different instructions for computers. A programmer writes instructions and the computer executes these instructions to solve a problem. A person can be a good programmer if he has the skill of solving problems.
First steps for solving computer problems
1. Restart your system. 2. Apply software updates. 3. Did adding a new software program cause a problem? 4. Still having issues? [Video] How to get the latest drivers and BIOS with Lenovo Vantage.
[2404.14222] An Artificial Neuron for Enhanced Problem Solving in Large
Computer Science > Human-Computer Interaction. arXiv:2404.14222 (cs) ... This approach not only enhances the LLM's problem solving precision but also reduces computational redundancy, paving the way for more sophisticated applications of artificial intelligence in cognitive tasks. This paper details the methodology, implementation, and ...
Energy-efficient superparamagnetic Ising machine and its ...
The growth of artificial intelligence leads to a computational burden in solving non-deterministic polynomial-time (NP)-hard problems. The Ising computer, which aims to solve NP-hard problems ...
PROBLEM OF THE DAY : 24/04/2024
Welcome to the daily solving of our PROBLEM OF THE DAY with Yash Dwivedi.We will discuss the entire problem step-by-step and work towards developing an optimized solution. This will not only help you brush up on your concepts of Backtracking but also build up problem-solving skills. In this problem, You are standing on a point (x, y) and you want to go to the origin (0, 0) by taking steps ...
NSF Award Search: Award # 2101026
The computer-adaptive problem-solving measures have sufficient reliability and strong validity evidence, and may limit test-taker fatigue. Finally, the project will benchmark current grades 6-8 instruments using an objective standard-setting method, which allows for improved score interpretations with content-related feedback. ...
Storing and utilizing energy with innovative sulfur-based cathodes
Solving the sulfur-shuttle problem using polymer chemistry. With sulfur-based cathodes, the mobility of the sulfur has so far led to a degradation of the battery—an effect known as the sulfur-shuttle. In the newly developed solution, the sulfur is encapsulated in a special microporous polymer network so that the sulfur particles are retained.
Risk identification and safety assessment of the human-computer
To solve the problem of risk identification and quantitative assessment for human-computer interaction (HCI) in complex avionics systems, an HCI safety analysis Risk identification and safety assessment of the human-computer interaction in the integrated avionics based on STAMP | BIAI Journals & Magazine | IEEE Xplore

Problem Solving Using Computer (Steps)

What Is Problem Solving? How Software Engineers Approach Complex Challenges

What Is Problem Solving?

The Importance of Problem-Solving Skills for Software Engineers

Driving Development Forward

Innovation and Optimization

Increasing Efficiency and Productivity

Improving Software Quality

Problem-Solving Techniques in Software Engineering

Decomposition

Abstraction

Algorithmic Thinking

Parallel Thinking

Testing and Validation

Evaluating Problem-Solving Skills

Recognizing Problem-Solving Skills in Candidates

Assessing Problem-Solving Skills

Key Takeaways

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Recommended topics

Does a College Degree Still Matter for Developers in 2024?

Exploring the Problem Solving Cycle in Computer Science – Strategies, Techniques, and Tools

Understanding the Importance

Data Analysis and Algorithm Design

Implementation and Evaluation

Identifying the Problem

Gathering the Necessary Data

The Importance of Data

Data Gathering Process

Data Analysis and Interpretation

Analyzing the Data

Statistical Analysis

Data Visualization

Exploring Possible Solutions

Developing an Algorithm

Evaluating the Solutions

Evaluating the Options

Consider the Problem

Analyze the Options

Select the Best Option

Making a Decision

Consider All Factors

Weigh the Options

Implementing the Solution

Testing and Debugging

Iterating for Improvement

Documenting the Process

Communicating the Solution

Analyzing the Problem

Presenting the Solution

Reflecting and Learning

Learning from Mistakes

Continuous Improvement

Applying Problem Solving in Real Life

Gathering and Analyzing Data

Generating and Evaluating Solutions

Implementing and Iterating

Building Problem Solving Skills

The Problem Solving Cycle

Importance of Data Analysis

Developing Effective Solutions

Questions and answers:

How important is the problem solving cycle in computer science?

What are the steps involved in the problem solving cycle?

Can you explain the problem definition and analysis step in the problem solving cycle?

Why is testing and debugging an important step in the problem solving cycle?

What is the problem-solving cycle in computer science?

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2.2 Computer Science Fundamentals

End of Unit 1

End of Unit 2

End of Unit 3

Course description

Topics covered

Prerequisites and next steps

3.1 Next Steps in Python