Stochastic Gradients: Optimization, Simulation, Randomization, and Sensitivity Analysis

被引：0

作者：

Fu, Michael C. ^{[1
,2
]}

Hu, Jiaqiao ^{[3
]}

Scheinberg, Katya ^{[4
]}

机构：

[1] Univ Maryland, Robert H Smith Sch Business, College Pk, MD 20742 USA

[2] Univ Maryland, Inst Syst Res, College Pk, MD 20742 USA

[3] SUNY Stony Brook, Dept Appl Math & Stat, Stony Brook, NY USA

[4] Georgia Inst Technol, H Milton Stewart Sch Ind & Syst Engn, Atlanta, GA USA

来源：

IISE TRANSACTIONS | 2025年

基金：

美国国家科学基金会;

关键词：

Stochastic gradients; perturbation analysis; automatic differentiation; likelihood ratio method; optimization; sensitivity analysis; stochastic gradient descent; stochastic approximation; stochastic optimization; simulation optimization; PERTURBATION ANALYSIS; APPROXIMATION ALGORITHMS; COMPOSITE OPTIMIZATION; SEARCH; CONVERGENCE; COMPLEXITY; RATES;

D O I：

10.1080/24725854.2025.2469839

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Big data and high-dimensional optimization problems in operations research (OR) and artificial intelligence (AI) have brought stochastic gradients to the forefront. This article provides a view of research and applications in stochastic gradient estimation from multiple perspectives, as seminal advances have come from diverse and disparate research fields, including operations research/management science (OR/MS), industrial/systems engineering (ISE), optimal/stochastic control, statistics, and more recently from the computer science (CS) AI machine learning (ML) community.

引用

页数：17

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