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Novel update techniques for the revised simplex method

Qi Huangfu (H.Qi-5***at***sms.ed.ac.uk)
J. A. Julian Hall (J.A.J.Hall***at***ed.ac.uk)

Abstract: This paper introduces three novel techniques for updating the invertible representation of the basis matrix when solving practical sparse linear programming (LP) problems using a high performance implementation of the dual revised simplex method, being of particular value when suboptimization is used. Two are variants of the product form update and the other permits multiple Forrest-Tomlin updates to be performed. Computational results show that one of the product form variants is significantly more efficient than the traditional approach, with its performance approaching that of the Forrest-Tomlin update for some problems. The other is less efficient, but valuable in the context of the dual revised simplex method with suboptimization. Results show that the multiple Forrest-Tomlin updates are performed with no loss of serial efficiency.

Keywords: Linear programming; dual revised simplex method; sparse matrices; basis inverse update techniques

Category 1: Linear, Cone and Semidefinite Programming (Linear Programming )

Citation: Technical Report ERGO-13-001, School of Mathematics, University of Edinburgh, January 2013

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Entry Submitted: 02/14/2013
Entry Accepted: 02/14/2013
Entry Last Modified: 02/14/2013

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