Convex approximations in stochastic programming by semidefinite programming

DOI10.1007/s10479-011-0986-0zbMath1254.90137OpenAlexW2082637242MaRDI QIDQ1931652

Imre Pólik, István Deák, Prékopa, András, Tamás Terlaky

Publication date: 15 January 2013

Published in: Annals of Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10479-011-0986-0

zbMATH Keywords

stochastic optimizationsemidefinite optimizationconvex approximationsuccessive regression approximations

Mathematics Subject Classification ID

Semidefinite programming (90C22) Stochastic programming (90C15)

Related Items

A stochastic semidefinite programming approach for bounds on option pricing under regime switching

Uses Software

• SeDuMi
• YALMIP

Cites Work

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• Solving two-stage stochastic programming problems with level decomposition
• Two-stage stochastic problems with correlated normal variables: computational experiences
• Computing Maximum Likelihood Estimators of Convex Density Functions
• Using SeDuMi 1.02, A Matlab toolbox for optimization over symmetric cones
• Semidefinite Programming