Robust Stochastic Optimization With Convex Risk Measures: A Discretized Subgradient Scheme

Robust Stochastic Optimization With Convex Risk Measures: A Discretized Subgradient Scheme

We study the distributionally robust stochastic optimization problem within a general framework of risk measures, in which the ambiguity set is described by a spectrum of practically used probability distribution constraints such as bounds on mean-deviation and entropic value-at-risk. We show that a...

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Bibliographic Details
Main Authors: Yu, H., Sun, Jie
Format: Journal Article
Language:English
Published: AMER INST MATHEMATICAL SCIENCES-AIMS 2021
Subjects:
Science & Technology
Technology
Physical Sciences
Engineering, Multidisciplinary
Operations Research & Management Science
Mathematics, Interdisciplinary Applications
Engineering
Mathematics
Stochastic optimization
distributionally robust
convex risk measure
subgradient method
two-stage optimization problem
LINEAR OPTIMIZATION
PROGRAMS
MODELS
Online Access:http://purl.org/au-research/grants/arc/DP160102819
http://hdl.handle.net/20.500.11937/90790

Internet

http://purl.org/au-research/grants/arc/DP160102819
http://hdl.handle.net/20.500.11937/90790

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