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How does benchmarking improve algorithm development?

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Benchmarking is a process of comparing the performance of different algorithms or methods for solving a problem. It can help you improve your algorithm development by identifying the strengths and weaknesses of your approach, finding the best practices and solutions from others, and setting realistic and achievable goals. In this article, you will learn how to use benchmarking effectively in the context of operations research, a field that applies mathematical and analytical techniques to optimize complex systems and decisions.

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Dirk Zechiel

Founder & CEO @ Quantagonia | Gurobi GmbH founder | DecisionAI
Darshan Pandya, Ph.D.

Assistant Professor, Operations Management and Data Sciences Area, School of Business Management, NMIMS University…

1 Why benchmarking matters

Benchmarking matters because it can help you evaluate and improve your algorithm development process. By benchmarking, you can measure the quality, efficiency, and effectiveness of your algorithm against some standards or criteria, such as accuracy, speed, scalability, robustness, or simplicity. You can also compare your algorithm with other existing or alternative algorithms that solve the same or similar problems, and learn from their advantages and disadvantages. Benchmarking can help you identify the gaps and opportunities for improvement, and guide you to make informed and evidence-based decisions.

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Darshan Pandya, Ph.D.

Assistant Professor, Operations Management and Data Sciences Area, School of Business Management, NMIMS University Mumbai | Ph.D. in Operations Management, Indian Institute of Management Raipur
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Benchmarking in operations research is like a race where algorithms are runners, and benchmarks serve as the stopwatch. It's a way to measure how new problem-solving methods stack up against known standards, highlighting which ones lead and which need improvement. This process fosters healthy competition among researchers, driving innovation and ensuring continuous progress in developing more efficient and effective algorithms. By using common benchmarks, researchers worldwide can compare results, facilitating a collaborative environment for advancement. For non-researchers, good benchmark performance acts as a seal of approval, signaling that an algorithm is reliable and ready for real-world applications.

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Adam J. Hepworth, PhD

Director | Global Military AI Expert | Visiting Fellow | Tech Innovation | Artificial Intelligence | Decision Science | Operations Research | Robotics & Autonomous Systems
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Benchmarking is an evaluation method that focuses on algorithm performance relative to existing (baseline) or state-of-the-art methods. Focusing on relative improvements rather than absolute metrics allows for identifying specific areas where an algorithm may lag, facilitating targeted enhancements. Compared to validation methods focused on module (internal) performance, such as ablation studies, benchmarking offers an externalised approach to compare total model performance. This approach helps to identify weaknesses and refine overall system performance by comparing against established standards or competing algorithms, driving innovation and efficiency in algorithm development.

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2 How to choose a benchmark

Choosing a suitable benchmark is a crucial step for benchmarking. A benchmark can be a data set, a problem instance, a performance metric, or a reference algorithm. You should choose a benchmark that is relevant, reliable, and representative of your problem domain and your algorithm objectives. You should also consider the availability, accessibility, and validity of the benchmark. Some possible sources of benchmarks are academic journals, conferences, competitions, repositories, or platforms that publish or host benchmark problems and solutions for operations research.

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Darshan Pandya, Ph.D.

Assistant Professor, Operations Management and Data Sciences Area, School of Business Management, NMIMS University Mumbai | Ph.D. in Operations Management, Indian Institute of Management Raipur
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Selecting a benchmark for algorithm evaluation requires an understanding of the algorithm's intended application and the specific challenges it aims to address. A suitable benchmark should align closely with real-world scenarios where the algorithm will operate, ensuring relevance. It must be comprehensive enough to test all critical performance aspects like speed, accuracy, and efficiency. Balancing difficulty is crucial; overly simple benchmarks won’t differentiate between good and excellent algorithms, while overly complex ones may discourage innovation. The benchmark should serve as a clear goalpost, guiding developers toward the most impactful improvements and ensuring that the algorithm can meet the demands of its intended use.

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Shameer Thaha

Founder & CEO | Entrepreneur | Speaker | Advisor | Board Member
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Benchmarking improves algorithm development by providing a way to measure and compare algorithm performance against established standards or alternative solutions. It helps identify areas for improvement, guide decision-making, and foster innovation. Choosing relevant benchmarks, conducting comprehensive studies, and learning from the results are key steps in this process. By continuously updating benchmarks, researchers and engineers can ensure that algorithm development remains aligned with evolving requirements.

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3 How to conduct a benchmarking study

Conducting a benchmarking study involves designing, executing, and analyzing a series of experiments that compare your algorithm with the chosen benchmark. It is important to follow a systematic and rigorous methodology that ensures the validity and reliability of your results. Additionally, you should document and report your methods, assumptions, parameters, data, results, and findings clearly and transparently. To do this effectively, you should first define the scope and objectives of your study. Then, you must select the benchmark and comparison criteria. Afterward, you must prepare the data and experimental environment before running the experiments and collecting the data. Finally, analyze the data to interpret the results and draw conclusions and recommendations.

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Dirk Zechiel

Founder & CEO @ Quantagonia | Gurobi GmbH founder | DecisionAI
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It is crucial to emphasize the significance of building a replicable environment for the benchmark runs. This includes using identical hardware and software configurations, especially when multiple computers are involved in the study. Even minimal differences in the hardware specifications can introduce variables that might skew the results, leading to inaccurate or false conclusions. Therefore, ensuring that all setups are as identical as possible is essential for maintaining the integrity and reliability of the benchmarking results.

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4 How to use benchmarking results

Using benchmarking results means applying the insights and knowledge gained from your study to improve your algorithm development. You should use the results to identify the strengths and weaknesses of your algorithm, and to compare it with the state-of-the-art or the best practices in your field. You should also use the results to set realistic and achievable goals for your algorithm improvement, and to prioritize the areas or aspects that need more attention or innovation. You should also use the results to communicate and demonstrate the value and impact of your algorithm to your stakeholders or customers.

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5 How to update and maintain your benchmarking

Updating and maintaining your benchmarking means keeping track of the changes and developments in your problem domain and your algorithm objectives, and adjusting your benchmarking accordingly. You should update and maintain your benchmarking regularly and consistently, to ensure that your benchmarking is always relevant, reliable, and representative. You should also update and maintain your benchmarking whenever you make significant changes or improvements to your algorithm, to evaluate and validate your progress and performance. You should also update and maintain your benchmarking when you encounter new or emerging problems or challenges that require new or modified solutions.

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6 How to learn from benchmarking

Learning from benchmarking means acquiring new skills, knowledge, and perspectives that can enhance your algorithm development. You can learn from benchmarking by reviewing and reflecting on your benchmarking process and results, and by seeking and applying feedback from others. You can also learn from benchmarking by exploring and experimenting with different benchmarks, methods, and solutions, and by discovering and adopting new ideas, techniques, or tools. You can also learn from benchmarking by collaborating and networking with other researchers, practitioners, or experts in your field, and by sharing and exchanging your experiences, insights, or best practices.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How does benchmarking improve algorithm development?

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1 Why benchmarking matters

2 How to choose a benchmark

3 How to conduct a benchmarking study

4 How to use benchmarking results

5 How to update and maintain your benchmarking

6 How to learn from benchmarking

7 Here’s what else to consider

Operations Research

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How does benchmarking improve algorithm development?

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3

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5

6

7

1 Why benchmarking matters

2 How to choose a benchmark

3 How to conduct a benchmarking study

4 How to use benchmarking results

5 How to update and maintain your benchmarking

6 How to learn from benchmarking

7 Here’s what else to consider

Operations Research

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