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Experimental investigation of an interior search method within a simplex framework

Experimental investigation of an interior search method within a simplex framework A feasible direction method for solving Linear Programming (LP) problems, followed by a procedure for purifying a non-basic solution to an improved extreme point solution have been embedded within an otherwise simplex based optimizer. The algorithm is designed to be hybrid in nature and exploits many aspects of sparse matrix and revised simplex technology. The interior search step terminates at a boundary point which is usually non-basic. This is followed by a series of minor pivotal steps which lead to a basic feasible solution with a superior objective function value. It is concluded that the procedures discussed in this article are likely to have three possible applications, which are (i) improving a non-basic feasible solution to a superior extreme point solution, (ii) an improved starting point for the revised simplex method, and (iii) an efficient implementation of the multiple price strategy of the revised simplex method. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Communications of the ACM Association for Computing Machinery

Experimental investigation of an interior search method within a simplex framework

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Publisher
Association for Computing Machinery
Copyright
Copyright © 1988 by ACM Inc.
ISSN
0001-0782
DOI
10.1145/53580.214953
Publisher site
See Article on Publisher Site

Abstract

A feasible direction method for solving Linear Programming (LP) problems, followed by a procedure for purifying a non-basic solution to an improved extreme point solution have been embedded within an otherwise simplex based optimizer. The algorithm is designed to be hybrid in nature and exploits many aspects of sparse matrix and revised simplex technology. The interior search step terminates at a boundary point which is usually non-basic. This is followed by a series of minor pivotal steps which lead to a basic feasible solution with a superior objective function value. It is concluded that the procedures discussed in this article are likely to have three possible applications, which are (i) improving a non-basic feasible solution to a superior extreme point solution, (ii) an improved starting point for the revised simplex method, and (iii) an efficient implementation of the multiple price strategy of the revised simplex method.

Journal

Communications of the ACMAssociation for Computing Machinery

Published: Dec 1, 1988

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