# Review of Large-Scale Simulation Optimization

arXiv: https://arxiv.org/abs/2403.15669 · HTML: https://mlsanity.com/paper/2403.15669/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2024.

## Built on by (most cited first, 3 of 3)

- [Contextual Strongly Convex Simulation Optimization: Optimize then Predict with Inexact Solutions](https://mlsanity.com/paper/2512.06270/) (2025)
- [TESO Tabu Enhanced Simulation Optimization for Noisy Black Box Problems](https://mlsanity.com/paper/2512.24007/) (2025)
- [Regular Tree Search for Simulation Optimization](https://mlsanity.com/paper/2506.17696/) (2025)

## Abstract (arXiv)

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.