Computational study of decomposition algorithms for mean-risk stochastic linear programs

DOI10.1007/s12532-015-0088-0zbMath1329.90091OpenAlexW974410315MaRDI QIDQ903926

Tanisha G. Cotton, Lewis Ntaimo

Publication date: 15 January 2016

Published in: Mathematical Programming Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s12532-015-0088-0

zbMATH Keywords

stochastic programmingdecompositionsubgradient optimizationmean-risk objectives

Mathematics Subject Classification ID

Convex programming (90C25) Large-scale problems in mathematical programming (90C06) Applications of mathematical programming (90C90) Stochastic programming (90C15)

Related Items (4)

The effect of regularization in portfolio selection problems ⋮ Scenario reduction for stochastic programs with conditional value-at-risk ⋮ A mean-risk mixed integer nonlinear program for transportation network protection ⋮ Two-stage absolute semi-deviation mean-risk stochastic programming: an application to the supply chain replenishment problem

Uses Software

• CPLEX
• SUTIL

Cites Work

• Monte Carlo bounding techniques for determinig solution quality in stochastic programs
• Stochastic programming with integer variables
• Decomposition algorithms for stochastic programming on a computational grid
• Deviation measures in linear two-stage stochastic programming
• On deviation measures in stochastic integer programming
• Convexity and decomposition of mean-risk stochastic programs
• The empirical behavior of sampling methods for stochastic programming
• Stochastic decomposition. A statistical method for large scale stochastic linear programming
• Conditional value-at-risk in stochastic programs with mixed-integer recourse
• Coherent Measures of Risk
• Level bundle methods for oracles with on-demand accuracy
• Risk-Averse Optimization in Two-Stage Stochastic Models: Computational Aspects and a Study
• Risk Aversion via Excess Probabilities in Stochastic Programs with Mixed-Integer Recourse
• On a Class of Minimax Stochastic Programs
• Dual Stochastic Dominance and Related Mean-Risk Models
• A New Scenario Decomposition Method for Large-Scale Stochastic Optimization
• Optimization of Convex Risk Functions
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