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Samuel Burer (samuelbureruiowa.edu) Abstract: The convex cone of $n \times n$ completely positive (CPP) matrices and its dual cone of copositive matrices arise in several areas of applied mathematics, including optimization. Every CPP matrix is doubly nonnegative (DNN), i.e., positive semidefinite and componentwise nonnegative, and it is known that, for $n \le 4$ only, every DNN matrix is CPP. In this paper, we investigate the difference between $5\times 5$ DNN and CPP matrices. Defining a {\em bad\/} matrix to be one which is DNN but not CPP, we: (i) characterize all $5 \times 5$ extreme DNN matrices, in particular bad ones; (ii) design a finite procedure to decompose any $n \times n$ DNN matrix into the sum of a CPP matrix and a bad matrix, which itself cannot be further decomposed; (iii) show that every bad $5 \times 5$ DNN matrix is the sum of a CPP matrix and a single bad extreme matrix; and (iv) demonstrate how to separate bad extreme matrices from the cone of $5 \times 5$ CPP matrices. Keywords: completely positive matrices, doubly nonnegative matrices, copositive matrices Category 1: Convex and Nonsmooth Optimization (Convex Optimization ) Category 2: Linear, Cone and Semidefinite Programming (Semidefinite Programming ) Category 3: Global Optimization (Theory ) Citation: Linear Algebra and its Applications 431 (2009), 15391552. Download: [PDF] Entry Submitted: 05/27/2008 Modify/Update this entry  
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