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 ...

Oct 1, 2009 · Every CP matrix is doubly nonnegative (DNN), i.e., positive semidefinite and component-wise nonnegative, and it is known that, for n ⩽ 4 only, ...

This work describes two different constructions for such a separation that apply to 5 × 5 matrices that are DNN but non-CP, and a generalization that ...

For X ∈ Sn let G(X) denote the undirected graph on vertices. {1,...,n} with edges {{i 6= j}|Xij. 6= 0}. Definition 1. Let G be an undirected graph on n ...

Mar 4, 2010 · 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 ...

Mar 8, 2010 · Abstract. The cone of Completely Positive (CP) matrices can be used to exactly formulate a variety of NP-Hard optimization problems.

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 ...

Abstract: The cone of Completely Positive (CP) matrices can be used to exactly formulate a variety of NP-Hard optimization problems.

Clearly, each CP matrix is nonnegative and positive semidefinite, i.e., doubly nonnegative (DNN), but the reverse is not necessarily the case. Indeed ...

Aug 16, 2019 · A doubly nonnegative matrix is a real positive semidefinite square matrix with nonnega- tive entries. A completely positive matrix is a doubly ...