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Separating Doubly Nonnegative and Completely Positive Matrices

Hongbo Dong (hongbo-dong***at***uiowa.edu)
Kurt M. Anstreicher (kurt-anstreicher***at***uiowa.edu)

Abstract: The cone of Completely Positive (CP) matrices can be used to exactly formulate a variety of NP-Hard optimization problems. A tractable relaxation for CP matrices is provided by the cone of Doubly Nonnegative (DNN) matrices; that is, matrices that are both positive semidefinite and componentwise nonnegative. A natural problem in the optimization setting is then to separate a given DNN but non-CP matrix from the cone of CP matrices. We describe two different constructions for such a separation that apply to 5x5 matrices that are DNN but non-CP. We also describe a generalization that applies to larger DNN but non-CP matrices having block structure. Computational results illustrate the applicability of these separation procedures to generate improved bounds on difficult problems.

Keywords: Completely positive matrices, copositive programming, semidefinite programming

Category 1: Global Optimization (Theory )

Category 2: Integer Programming ((Mixed) Integer Nonlinear Programming )

Category 3: Linear, Cone and Semidefinite Programming (Other )

Citation: Working paper, Dept. of Management Sciences, University of Iowa, Iowa City IA, March 2010.

Download: [PDF]

Entry Submitted: 03/04/2010
Entry Accepted: 03/05/2010
Entry Last Modified: 03/16/2011

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