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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: | 27 Aug 2021 17:12 |
| URI: | https://pure.iiasa.ac.at/2562 |
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