Parallel algorithms for nonlinear programming problems
type: Item
instance of: scholarly article
title: Parallel algorithms for nonlinear programming problems
zbMATH Open document ID: 0642.90083
DOI: 10.1007/BF00940841
author: Michel J. Daydé
published in: Journal of Optimization Theory and Applications
publication date: 1989-01-01T00:00:00Z
review text: This paper describes several parallel algorithms for solving nonlinear programming problems. Two approaches where parallelism can successfully be introduced have been explored: a quadratic approximation method based on penalty function and a dual method. These methods are improved by using two algorithms originally proposed for solving unconstrained problems: the parallel variable metric algorithm and the parallel Jacobson-Oksman algorithm. Even though general problems are dealt with, particular emphasis is placed on the potential of these parallel methods for separable programming problems. The numerical effectiveness of the algorithms is demonstrated on a set of test problems using a CRAY-1S vector computer and serial computers (with respect to sequential versions of the same methods).
Mathematics Subject Classification ID: 90C30
Mathematics Subject Classification ID: 65K05
Mathematics Subject Classification ID: 49M30
zbMATH DE Number: 4047450
zbMATH Keywords: parallel algorithms
zbMATH Keywords: quadratic approximation
zbMATH Keywords: penalty function
zbMATH Keywords: dual method
zbMATH Keywords: parallel variable metric algorithm
zbMATH Keywords: parallel Jacobson-Oksman algorithm
zbMATH Keywords: separable programming
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 4047450