Distribution sensitivity in stochastic programming
Mathematical ProgrammingPublished 1 March 1991
Werner Römisch, Rüdiger Schultz
Citations73
SJR quartileQ1
SJR score1.73
SNIP2.20
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TL;DR
In this paper, stochastic programming problems are viewed as parametric programs with respect to the probability distributions of the random coefficients and quantitative continuity results for optimal values and optimal solution sets are proved.
Abstract
In this paper, stochastic programming problems are viewed as parametric programs with respect to the probability distributions of the random coefficients. General results on quantitative stability in parametric optimization are used to study distribution sensitivity of stochastic programs. For recourse and chance constrained models quantitative continuity results for optimal values and optimal solution sets are proved (with respect to suitable metrics on the space of probability distributions). The results are useful to study the effect of approximations and of incomplete information in stochastic programming.
Keywords
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