Article2008

Discrepancy distances and scenario reduction in two-stage stochastic mixed-integer programming

René Henrion · Christian Küchler · Werner Römisch · ,Humboldt-Universität zu Berlin, Institut für Mathematik, 10099 Berlin

39 citations0/0 files storedJournal of Industrial and Management Optimization

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Risk and Portfolio Optimization Electric Power System Optimization Advanced Optimization Algorithms Research

Abstract

Polyhedral discrepancies are relevant for the quantitative stability of mixed-integer two-stage and chance constrained stochastic programs. We study the problem of optimal scenario reduction for a discrete probability distribution with respect to certain polyhedral discrepancies and develop algorithms for determining the optimally reduced distribution approximately. Encouraging numerical experience for optimal scenario reduction is provided.

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References

2003Scenario Reduction Algorithms in Stochastic Programming 2000Scenario reduction in stochastic programming: An approach using probability metrics 2003Stability of Stochastic Programming Problems 2003Stochastic programming with integer variables 2007A note on scenario reduction for two-stage stochastic programs 2007Scenario reduction in stochastic programming with respect to discrepancy distances 1996Rates of Convergence in Stochastic Programs with Complete Integer Recourse

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