- Title
- Comparative study of RPSALG algorithm for convex semi-infinite programming
- Creator
- Auslender, Alfred; Ferrer, Albert; Goberna, Miguel; López, Marco
- Date
- 2014
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/76652
- Identifier
- vital:7573
- Identifier
-
https://doi.org/10.1007/s10589-014-9667-7
- Identifier
- ISSN:0926-6003
- Abstract
- The Remez penalty and smoothing algorithm (RPSALG) is a unified framework for penalty and smoothing methods for solving min-max convex semi-infinite programing problems, whose convergence was analyzed in a previous paper of three of the authors. In this paper we consider a partial implementation of RPSALG for solving ordinary convex semi-infinite programming problems. Each iteration of RPSALG involves two types of auxiliary optimization problems: the first one consists of obtaining an approximate solution of some discretized convex problem, while the second one requires to solve a non-convex optimization problem involving the parametric constraints as objective function with the parameter as variable. In this paper we tackle the latter problem with a variant of the cutting angle method called ECAM, a global optimization procedure for solving Lipschitz programming problems. We implement different variants of RPSALG which are compared with the unique publicly available SIP solver, NSIPS, on a battery of test problems.
- Publisher
- Kluwer Academic Publishers
- Relation
- Computational Optimization and Applications Vol. 60, no. 1 (2014), p. 59-87
- Rights
- Copyright Kluwer Academic
- Rights
- This metadata is freely available under a CCO license
- Subject
- Convex semi-infinite programming; Cutting angle method; Penalty methods; Remez-type methods; Smoothing methods; Algorithms; Constrained optimization; Convex optimization; Global optimization; Iterative methods; Mathematical programming; Optimization; Semi infinite programming; Type methods; Problem solving; 0102 Applied Mathematics; 0103 Numerical and Computational Mathematics
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