A Theoretician's Guide to the Experimental Analysis of Algorithms
đź“ś Abstract
The major goal of analysis of algorithms is to improve performance. In this article we discuss how this goal can be furthered by supplementing traditional asymptotic analysis with experimental studies. The main thesis is that such studies provide insights that are hard to obtain by theoretical analysis alone. They provide a basis for validating theoretical results and frequently lead to suggestive ideas that help guide subsequent theoretical efforts. Experimental analysis often reveals characteristic performance patterns that can be applied directly to optimize algorithm design choices. It also identifies pitfalls of theoretical models, and helps to focus analytic attention on problem areas where more detailed theory can lead to significant performance gains.
✨ Summary
Catherine McGeoch’s paper, “A Theoretician’s Guide to the Experimental Analysis of Algorithms,” has become an important resource in the computational field, emphasizing the importance of complementing theoretical analysis of algorithms with experimental studies. The paper argues that experimental analysis can offer significant insights that theoretical analysis may miss, such as validating theoretical results, driving algorithm optimization, and identifying theoretical model limitations. This approach has been influential in guiding subsequent research in algorithm design and performance evaluation. However, specific references or citations indicating the widespread impact of the paper were limited, predominately because it serves as a conceptual guide rather than a direct influence on a particular technological advancement. Despite this, its ideas are central in disciplines where empirical validation of computational models is crucial. The paper is referenced in educational materials and methodological discussions about algorithmic research, such as in this ACM digital library entry.