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Computational experience and the explanatory value of condition numbers for linear optimization

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dc.contributor Ordóñez, Fernando, 1970-
dc.contributor Freund, Robert M.
dc.date 2004-06-01T16:43:10Z
dc.date 2004-06-01T16:43:10Z
dc.date 2002
dc.date 2002
dc.date.accessioned 2013-10-09T02:39:41Z
dc.date.available 2013-10-09T02:39:41Z
dc.date.issued 2013-10-09
dc.identifier 4337-02
dc.identifier http://papers2.ssrn.com/paper.taf?ABSTRACT%5FID=299326
dc.identifier http://hdl.handle.net/1721.1/5408
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description 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: Convex Optimization, Complexity, Interior-Point Method, Barrier Method.
dc.description Fernando Ordonez [and] Robert M. Freund.
dc.description Abstract in HTML and working paper for download in PDF available via World Wide Web at the Social Science Research Network.
dc.description Title from cover. "January 2002."
dc.description Includes bibliographical references (leaves 32-34).
dc.format 34 leaves
dc.format 2013959 bytes
dc.format application/pdf
dc.language eng
dc.publisher Massachusetts Institute of Technology, Operations Research Center
dc.relation Operations Research Center Working Paper;OR 361-02
dc.rights http://papers2.ssrn.com/paper.taf?ABSTRACT%5FID=299326
dc.title Computational experience and the explanatory value of condition numbers for linear optimization


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