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  • 1
    Digitale Medien
    Digitale Medien
    Stability in multistage stochastic programming (1995)
    Springer
    Annals of operations research 56 (1995), S. 79-93 
    Details
    ISSN: 1572-9338
    Schlagwort(e): Multistage stochastic programs ; optimization in Banach spaces ; stability ; approximation
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Mathematik , Wirtschaftswissenschaften
    Notizen: Abstract Multistage stochastic programs are regarded as mathematical programs in a Banach spaceX of summable functions. Relying on a result for parametric programs in Banach spaces, the paper presents conditions under which linearly constrained convex multistage problems behave stably when the (input) data process is subjected to (small) perturbations. In particular, we show the persistence of optimal solutions, the local Lipschitz continuity of the optimal value and the upper semicontinuity of optimal sets with respect to the weak topology inX. The linear case with deterministic first-stage decisions is studied in more detail.
    Materialart: Digitale Medien
    URL: http://dx.doi.org/10.1007/BF02031701
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 2
    Digitale Medien
    Digitale Medien
    Distribution sensitivity in stochastic programming (1991)
    Springer
    Mathematical programming 50 (1991), S. 197-226 
    Details
    ISSN: 1436-4646
    Schlagwort(e): 90C15 ; 90C31 ; Stochastic programming ; quantitative stability ; recourse problem ; chance constrained problem ; probability metric
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Mathematik
    Notizen: 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.
    Materialart: Digitale Medien
    URL: http://dx.doi.org/10.1007/BF01594935
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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