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

René Henrion | Zendy; Christian Küchler | Zendy; Werner Römisch | Zendy

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

Author(s) -

René Henrion,

Christian Küchler,

Werner Römisch

Publication year - 2008

Publication title -

journal of industrial and management optimization

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.325

H-Index - 32

eISSN - 1553-166X

pISSN - 1547-5816

DOI - 10.3934/jimo.2008.4.363

Subject(s) - reduction (mathematics) , integer programming , integer (computer science) , mathematical optimization , stochastic programming , stability (learning theory) , probability distribution , computer science , distribution (mathematics) , linear programming , mathematics , statistics , mathematical analysis , geometry , machine learning , programming language

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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