A Descent Algorithm for Large-Scale Linearly Constrained Convex Nonsmooth Minimization

Kiwiel K (1984). A Descent Algorithm for Large-Scale Linearly Constrained Convex Nonsmooth Minimization. IIASA Collaborative Paper. IIASA, Laxenburg, Austria: CP-84-015

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Abstract

A descent algorithm is given for solving a large convex program obtained by augmenting the objective of a linear program with a (possibly nondifferentiable) convex function depending on relatively few variables. Such problems often arise in practice as deterministic equivalents of stochastic programming problem. The algorithm s search direction finding subproblems can be solved efficiently by the existing software for large-scale smooth optimization. The algorithm is both readily implementable and globally convergent.

Item Type: Monograph (IIASA Collaborative Paper)
Research Programs: Adaption and Optimization (ADO)
Depositing User: IIASA Import
Date Deposited: 15 Jan 2016 01:55
Last Modified: 19 Jul 2016 07:47
URI: http://pure.iiasa.ac.at/2562

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