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Linearization methods for optimization of functionals which depend on probability measures

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Stochastic Programming 84 Part II

Part of the book series: Mathematical Programming Studies ((MATHPROGRAMM,volume 28))

Abstract

The main purpose of this paper is to discuss numerical optimization procedures for problems in which both the objective function and the constraints depend on distribution functions. The objective function and constraints are assumed to be nonlinear and to have directional derivatives. The proposed algorithm is based on duality relations between the linearized problem and some special finite-dimensional minimax problem and is of the feasible-direction type. The resulting minimax problem is solved using the cutting-plane technique.

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Authors and Affiliations

  1. International Institute for Applied Systems Analysis, Laxenburg, Austria

    A. Gaivoronski

  2. Glushkov Institute of Cybernetics, Kiev, USSR

    A. Gaivoronski

Authors
  1. A. Gaivoronski
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Andras Prékopa Roger J.- B. Wets

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© 1986 The Mathematical Programming Society, Inc.

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Gaivoronski, A. (1986). Linearization methods for optimization of functionals which depend on probability measures. In: Prékopa, A., Wets, R.J.B. (eds) Stochastic Programming 84 Part II. Mathematical Programming Studies, vol 28. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0121130

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  • DOI: https://doi.org/10.1007/BFb0121130

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00926-6

  • Online ISBN: 978-3-642-00927-3

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