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Variance Reduction in Population-Based Optimization: Application to Unit Commitment

  • Jean-Joseph Christophe
  • Jérémie Decock
  • Jialin LiuEmail author
  • Olivier Teytaud
Conference paper
  • 366 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9554)

Abstract

We consider noisy optimization and some traditional variance reduction techniques aimed at improving the convergence rate, namely (i) common random numbers (CRN), which is relevant for population-based noisy optimization and (ii) stratified sampling, which is relevant for most noisy optimization problems. We present artificial models of noise for which common random numbers are very efficient, and artificial models of noise for which common random numbers are detrimental. We then experiment on a desperately expensive unit commitment problem. As expected, stratified sampling is never detrimental. Nonetheless, in practice, common random numbers provided, by far, most of the improvement.

Keywords

Noisy optimization Variance reduction Stratified sampling Common random numbers 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jean-Joseph Christophe
    • 1
  • Jérémie Decock
    • 1
  • Jialin Liu
    • 1
    Email author
  • Olivier Teytaud
    • 1
  1. 1.Univ. Paris-SudGif-sur-YvetteFrance

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