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Computational Experience and the Explanatory Value of Condition Numbers for Linear Optimization

Ordonez Fernando (fordon***at***mit.edu)
Robert M. Freund (rfreund***at***mit.edu)

Abstract: The goal of this paper is to develop some computational experience and test the practical relevance of the theory of condition numbers C(d) for linear optimization, as applied to problem instances that one might encounter in practice. We used the NETLIB suite of linear optimization problems as a test bed for condition number computation and analysis. Our computational results indicate that 72% of the NETLIB suite problem instances are ill-conditioned. However, after pre-processing heuristics are applied, only 19% of the post- processed problem instances are ill-conditioned, and log C(d) of the finitely-conditioned post- processed problems is fairly nicely distributed. We also show that the number of IPM iterations needed to solve the problems in the NETLIB suite varies roughly linearly (and monotonically) with log C(d) of the post-processed problem instances. Empirical evidence yields a positive linear relationship between IPM iterations and log C(d) for the post- processed problem instances, significant at the 95% confidence level. Furthermore, 42% of the variation in IPM iterations among the NETLIB suite problem instances is accounted for by log C(d) of the problem instances after pre- processing.

Keywords: linear program, condition number, interior-point method

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

Category 2: Optimization Software and Modeling Systems

Citation: MIT Operations Research Center Working Paper OR361-02

Download: [Postscript][PDF]

Entry Submitted: 01/31/2002
Entry Accepted: 01/31/2002
Entry Last Modified: 01/31/2002

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