Convex approximations in stochastic programming by semidefinite programming
type: Item
instance of: scholarly article
title: Convex approximations in stochastic programming by semidefinite programming
zbMATH Open document ID: 1254.90137
author: I. Deák
author: Imre Pólik
author: András Prékopa
author: Tamás Terlaky
published in: Annals of Operations Research
publication date: 2013-01-15T00:00:00Z
Mathematics Subject Classification ID: 90C15
Mathematics Subject Classification ID: 90C22
zbMATH DE Number: 6125693
zbMATH Keywords: convex approximation
zbMATH Keywords: stochastic optimization
zbMATH Keywords: successive regression approximations
zbMATH Keywords: semidefinite optimization
describes a project that uses: SeDuMi
describes a project that uses: YALMIP
MaRDI profile type: Publication
full work available at URL: s10479-011-0986-0
OpenAlex ID: W2082637242
cites work: Q4415379
cites work: Q5439474
cites work: Two-stage stochastic problems with correlated normal variables: computational experiences
cites work: Testing successive regression approximations by large-scale two-stage problems
cites work: Solving two-stage stochastic programming problems with level decomposition
cites work: Q4309474
cites work: Aspects of semidefinite programming. Interior point algorithms and selected applications
cites work: Monte Carlo bounding techniques for determinig solution quality in stochastic programs
cites work: Q4225025
cites work: Q4838490
cites work: Using SeDuMi 1.02, A Matlab toolbox for optimization over symmetric cones
cites work: Computing Maximum Likelihood Estimators of Convex Density Functions
cites work: Semidefinite Programming
DOI: 10.1007/S10479-011-0986-0
description: scientific article; zbMATH DE number 6125693