# Evaluating Las Vegas Algorithms - Pitfalls and Remedies

arXiv: https://arxiv.org/abs/1301.7383 · HTML: https://mlsanity.com/paper/1301.7383/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2013.

## Built on by (most cited first, 2 of 2)

- [Evidence for Long-Tails in SLS Algorithms](https://mlsanity.com/paper/2107.00378/) (2021)
- [Towards an Understanding of Long-Tailed Runtimes of SLS Algorithms](https://mlsanity.com/paper/2210.13159/) (2022)

## Abstract (arXiv)

Stochastic search algorithms are among the most sucessful approaches for solving hard combinatorial problems. A large class of stochastic search approaches can be cast into the framework of Las Vegas Algorithms (LVAs). As the run-time behavior of LVAs is characterized by random variables, the detailed knowledge of run-time distributions provides important information for the analysis of these algorithms. In this paper we propose a novel methodology for evaluating the performance of LVAs, based on the identification of empirical run-time distributions. We exemplify our approach by applying it to Stochastic Local Search (SLS) algorithms for the satisfiability problem (SAT) in propositional logic. We point out pitfalls arising from the use of improper empirical methods and discuss the benefits of the proposed methodology for evaluating and comparing LVAs.