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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Mathematical programming 70 (1995), S. 73-89 
    ISSN: 1436-4646
    Keywords: Stochastic integer programming ; Parametric integer programming ; Continuity ; Stability ; Weak convergence of probability measures
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract For two-stage stochastic programs with integrality constraints in the second stage, we study continuity properties of the expected recourse as a function both of the first-stage policy and the integrating probability measure. Sufficient conditions for lower semicontinuity, continuity and Lipschitz continuity with respect to the first-stage policy are presented. Furthermore, joint continuity in the policy and the probability measure is established. This leads to conclusions on the stability of optimal values and optimal solutions to the two-stage stochastic program when subjecting the underlying probability measure to perturbations.
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Mathematical programming 50 (1991), S. 197-226 
    ISSN: 1436-4646
    Keywords: 90C15 ; 90C31 ; Stochastic programming ; quantitative stability ; recourse problem ; chance constrained problem ; probability metric
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: 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.
    Type of Medium: Electronic Resource
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