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Review of Large-Scale Simulation Optimization

Weiwei Fan, L. Jeff Hong, Guangxin Jiang, Jun Luo

TL;DR

Several widely employed techniques for addressing large-scale SO problems, such as divide-and-conquer, dimension reduction, and gradient-based algorithms are reviewed, as well as parallelization techniques leveraging widely accessible parallel computing environments to facilitate the resolution of large-scale SO problems.

Abstract

Large-scale simulation optimization (SO) problems encompass both large-scale ranking-and-selection problems and high-dimensional discrete or continuous SO problems, presenting significant challenges to existing SO theories and algorithms. This paper begins by providing illustrative examples that highlight the differences between large-scale SO problems and those of a more moderate scale. Subsequently, it reviews several widely employed techniques for addressing large-scale SO problems, such as divide and conquer, dimension reduction, and gradient-based algorithms. Additionally, the paper examines parallelization techniques leveraging widely accessible parallel computing environments to facilitate the resolution of large-scale SO problems.

Review of Large-Scale Simulation Optimization

TL;DR

Several widely employed techniques for addressing large-scale SO problems, such as divide-and-conquer, dimension reduction, and gradient-based algorithms are reviewed, as well as parallelization techniques leveraging widely accessible parallel computing environments to facilitate the resolution of large-scale SO problems.

Abstract

Large-scale simulation optimization (SO) problems encompass both large-scale ranking-and-selection problems and high-dimensional discrete or continuous SO problems, presenting significant challenges to existing SO theories and algorithms. This paper begins by providing illustrative examples that highlight the differences between large-scale SO problems and those of a more moderate scale. Subsequently, it reviews several widely employed techniques for addressing large-scale SO problems, such as divide and conquer, dimension reduction, and gradient-based algorithms. Additionally, the paper examines parallelization techniques leveraging widely accessible parallel computing environments to facilitate the resolution of large-scale SO problems.
Paper Structure (21 sections, 12 equations)

This paper contains 21 sections, 12 equations.

Table of Contents

  1. Introduction
  2. Large Is Different
  3. Sample Optimality for Fixed-Budget R&S
  4. Uniform Sampling of COMPASS
  5. Higher-Order Smoothness Matters
  6. Cost of IPA
  7. Divide and Conquer
  8. Divide-and-Conquer in R&S Algorithms
  9. Divide-and-Conquer in Random Search Algorithms
  10. Divide-and-Conquer in Bayesian Optimization
  11. Dimension Reduction
  12. Low Effective Dimension
  13. Utilizing Special Structures
  14. Gradient-Based Algorithms
  15. Gradient Estimation
  16. ...and 6 more sections