Stochastic Optimization Algorithms

Details Stochastic Optimization Algorithms Stochastic Optimization Algorithms

2007-04-28

arxiv.org/abs/0704.3780v1

Rennard, J.-P., Handbook of Research on Nature Inspired Computing for Economics and Management, IGR, 2006

Computer Science

Neural and Evolutionary Computing

16 pages, 4 figures, 2 tables

Scientific paper

When looking for a solution, deterministic methods have the enormous advantage that they do find global optima. Unfortunately, they are very CPU-intensive, and are useless on untractable NP-hard problems that would require thousands of years for cutting-edge computers to explore. In order to get a result, one needs to revert to stochastic algorithms, that sample the search space without exploring it thoroughly. Such algorithms can find very good results, without any guarantee that the global optimum has been reached; but there is often no other choice than using them. This chapter is a short introduction to the main methods used in stochastic optimization.

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