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.
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收藏
页数：17
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