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

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/2622 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/2622 http://eprints.org/relation/isCoversheetVersionOf https://pure.iiasa.ac.at/id/document/2622 archive 351 disk0/00/00/25/62 2016-01-15 01:55:29 2021-08-27 17:12:10 2016-01-15 01:55:29 monograph show 2 Kiwiel K. A Descent Algorithm for Large-Scale Linearly Constrained Convex Nonsmooth Minimization pub iis_met prog_ado 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. 1984-04 published CP-84-015 CP-84-015 public collaborative_paper IIASA, Laxenburg, Austria 21 FALSE info:eu-repo/semantics/book