Smoothed analysis of algorithms
Daniel A. Spielman, Shang-Hua Teng
TL;DR
This paper surveys the smoothed analysis framework, which explains the practical success of algorithms by blending worst-case and average-case perspectives, providing insights into algorithm performance.
Contribution
It offers a comprehensive overview of smoothed analysis applications and results, highlighting its role in understanding algorithm efficiency.
Findings
Smoothed analysis bridges worst-case and average-case analysis.
It explains the practical performance of algorithms.
The survey covers various smoothed analysis results.
Abstract
Spielman and Teng introduced the smoothed analysis of algorithms to provide a framework in which one could explain the success in practice of algorithms and heuristics that could not be understood through the traditional worst-case and average-case analyses. In this talk, we survey some of the smoothed analyses that have been performed.
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Taxonomy
TopicsAdvanced Optimization Algorithms Research · Matrix Theory and Algorithms · Polynomial and algebraic computation