Linearization methods for optimization of functionals which depend on probability measures

Gaivoronski, A. (1986). Linearization methods for optimization of functionals which depend on probability measures. In: Stochastic Programming 84 Part II. Eds. Prekopa, A. & Wets, R., pp. 157-181 Springer. ISBN 978-3-642-00926-6 10.1007/BFb0121130.

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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-place technique.

Item Type:	Book Section
Depositing User:	Luke Kirwan
Date Deposited:	09 Aug 2016 14:06
Last Modified:	27 Aug 2021 17:41
URI:	https://pure.iiasa.ac.at/13650

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