Ka\unková, Vlasta A note on the differentiability in two-stage stochastic nonlinear programming problems. (English) Zbl 0654.90062 Kybernetika 24, No. 3, 207-215 (1988). The differentiability of the objective function in two-stage stochastic nonlinear programming problems is investigated. Sufficient conditions are presented which assure that the form of the gradient can be derived from the supergradient in the corresponding (deterministic) parametric optimization problem. Reviewer: J.Pinter Cited in 1 ReviewCited in 1 Document MSC: 90C15 Stochastic programming Keywords:differentiability; two-stage stochastic nonlinear programming; supergradient; parametric optimization PDF BibTeX XML Cite \textit{V. Ka\u{n}ková}, Kybernetika 24, No. 3, 207--215 (1988; Zbl 0654.90062) Full Text: Link EuDML References: [1] P. Kall: Stochastic Linear Programming. Springer-Verlag, Berlin–Heidelberg–New York 1976. · Zbl 0317.90042 [2] V. Kaňková: Differentiability of the optimalized function in a two-stage stochastic nonlinear programming problem. Ekonomicko-matematický obzor 14 (1978), 3, 322-330. In Czech. [3] V. Kaňková: Approximating solution of problems of the two-stage stochastic nonlinear programming. Ekonomicko-matematický obzor 16 (1980), 1, 64-76. In Czech. [4] V. Kaňková: Optimization problem with parameter and its application to the problems of two-stage stochastic nonlinear programming. Kybernetika 16 (1980), 5, 411-425. [5] S. Karlin: Mathematical Methods and Theory in Games, Programming, and Economics. Pergamon Press, London–Paris, 1959. · Zbl 0139.12704 [6] В. Н. Пшеничный: Необоходимые условия экстремума. Hauka, Москва 1982. · Zbl 1170.01407 [7] R. T. Rockafellar: Convex Analysis. Princeton Press, New Jersey 1970. · Zbl 0193.18401 This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.