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

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Author Info
Ordónez, Fernando
Freund, Robert M.
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-processin

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File URL: http://hdl.handle.net/1721.1/3547
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Paper provided by Massachusetts Institute of Technology (MIT), Sloan School of Management in its series Working papers with number 4337-02.

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Date of creation: 25 Sep 2003
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Handle: RePEc:mit:sloanp:3547

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Postal: MASSACHUSETTS INSTITUTE OF TECHNOLOGY (MIT), SLOAN SCHOOL OF MANAGEMENT, 50 MEMORIAL DRIVE CAMBRIDGE MASSACHUSETTS 02142 USA

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Keywords: Convex Optimization; Complexity; Interior-Point Method; Barrier Method;

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  1. Freund, Robert Michael. & Todd, Michael J., 1947-, 1992. "Barrier functions and interior-point algorithms for linear programming with zero-, one-, or two-sided bounds on the variables," Working papers 3454-92., Massachusetts Institute of Technology (MIT), Sloan School of Management. [Downloadable!]
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