Vectorization and multitasking of nonlinear network programming algorithms
type: Item
instance of: scholarly article
title: Vectorization and multitasking of nonlinear network programming algorithms
zbMATH Open document ID: 0665.90095
DOI: 10.1007/BF01589416
author: Stavros A. Zenios
author: John M. Mulvey
published in: Mathematical Programming. Series A. Series B
publication date: 1988-01-01T00:00:00Z
review text: Vector supercomputers are designed with two levels of parallelism in order to achieve computational efficiency: low level parallelism through vector operations and high level pallelism with multiple independent processors. These innovations have a significant impact on the development of algorithms for network optimization. In this paper a framework for the vectorization and multitasking of optimization software is developed. It is then applied on the primal truncated Newton algorithm for nonlinear generalized network problems. The vectorization and multitasking of the algorithm is discussed and illustrated with computational experiments with the software system NLPNETG on the CRAY series of vector multiprocessors.
Mathematics Subject Classification ID: 90C35
Mathematics Subject Classification ID: 90B10
Mathematics Subject Classification ID: 65K05
zbMATH DE Number: 4089352
zbMATH Keywords: Vector supercomputers
zbMATH Keywords: two levels of parallelism
zbMATH Keywords: vector operations
zbMATH Keywords: multiple independent processors
zbMATH Keywords: network optimization
zbMATH Keywords: primal truncated Newton algorithm
zbMATH Keywords: nonlinear generalized network problems
MaRDI profile type: Publication
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full work available at URL: https://doi.org/10.1007/bf01589416
OpenAlex ID: W1975455230
description: scientific article; zbMATH DE number 4089352