Forbidden minor characterizations for low-rank optimal solutions to semidefinite programs over the elliptope

We study a new geometric graph parameter \egd(G), defined as the smallest integer $r\ge 1$ for which any partial symmetric matrix which is completable to a correlation matrix and whose entries are specified at the positions of the edges of $G$, can be completed to a matrix in the convex hull of correlation matrices of \rank at most $r$. This graph parameter is motivated by its relevance to the problem of finding low rank solutions to semidefinite programs over the elliptope, and also by its relevance to the bounded rank Grothendieck constant. Indeed, \egd(G)\le r if and only if the rank-$r$ Grothendieck constant of $G$ is equal to 1. We show that the parameter \egd(G) is minor monotone, we identify several classes of forbidden minors for \egd(G)\le r and we give the full characterization for the case $r=2$. We also show an upper bound for \egd(G) in terms of a new tree-width-like parameter \sla(G), defined as the smallest $r$ for which $G$ is a minor of the strong product of a tree and $K_r$. We show that, for any 2-connected graph $G\ne K_{3,3}$ on at least 6 nodes, \egd(G)\le 2 if and only if \sla(G)\le 2.Comment: 33 pages, 8 Figures. In its second version, the paper has been modified to accommodate the suggestions of the referees. Furthermore, the title has been changed since we feel that the new title reflects more accurately the content and the main results of the pape

Complexity of the positive semidefinite matrix completion problem with a rank constraint.

We consider the decision problem asking whether a partial rational symmetric matrix with an all-ones diagonal can be completed to a full positive semidefinite matrix of rank at most k. We show that this problem is NP-hard for any fixed integer k ≥ 2. Equivalently, for k ≥ 2, it is NP-hard to test membership in the rank constrained elliptope Ek(G), i.e., the set of all partial matrices with off-diagonal entries specified at the edges of G, that can be completed to a positive semidefinite matrix of rank at most k. Additionally, we show that deciding membership in the convex hull of Ek(G) is also NP-hard for any fixed integer k ≥ 2

Symmetry in RLT cuts for the quadratic assignment and standard quadratic optimization problems

The reformulation-linearization technique (RLT), introduced in [W.P. Adams, H.D. Sher-ali, A tight linearization and an algorithm for zero-one quadratic programming problems, Management Science, 32(10):1274{1290, 1986], provides a way to compute linear program-ming bounds on the optimal values of NP-hard combinatorial optimization problems. In this paper we show that, in the presence of suitable algebraic symmetry in the original problem data, it is sometimes possible to compute level two RLT bounds with additional linear matrix inequality constraints. As an illustration of our methodology, we compute the best-known bounds for certain graph partitioning problems on strongly regular graphs

Forbidden minor characterizations for low-rank optimal solutions to semidefinite programs over the elliptope

We study a new geometric graph parameter $egd(G)$, defined as the smallest integer $r\ge 1$ for which any partial symmetric matrix which is completable to a correlation matrix and whose entries are specified at the positions of the edges of $G$, can be completed to a matrix in the convex hull of correlation matrices of \rank at most $r$. This graph parameter is motivated by its relevance to the bounded rank Grothendieck constant: $egd(G) \le r$ if and only if the rank-$r$ Grothendieck constant of $G$ is equal to 1. The parameter $egd(G)$ is minor monotone. We identify several classes of forbidden minors for $egd(G)\le r$ and give the full characterization for the case $r=2$. We show an upper bound for $egd(G)$ in terms of a new tree-width-like parameter \sla(G), defined as the smallest $r$ for which $G$ is a minor of the strong product of a tree and $K_r$. We show that, for $G\ne K_{3,3}$ 2-connected on at least 6 nodes, $egd(G)\le 2$ if and only if \sla(G)\le 2

On bounded rank positive semidefinite matrix completions of extreme partial correlation matrices.

We study a new geometric graph parameter $egd(G)$, defined as the smallest integer $r\ge 1$ for which any partial symmetric matrix which is completable to a correlation matrix and whose entries are specified at the positions of the edges of $G$, can be completed to a matrix in the convex hull of correlation matrices of \rank at most $r$. This graph parameter is motivated by its relevance to the bounded rank Grothendieck constant: $egd(G) \le r$ if and only if the rank-$r$ Grothendieck constant of $G$ is equal to 1. The parameter $egd(G)$ is minor monotone. We identify several classes of forbidden minors for $egd(G)\le r$ and give the full characterization for the case $r=2$. We show an upper bound for $egd(G)$ in terms of a new tree-width-like parameter \sla(G), defined as the smallest $r$ for which $G$ is a minor of the strong product of a tree and $K_r$. We show that, for $G\ne K_{3,3}$ 2-connected on at least 6 nodes, $egd(G)\le 2$ if and only if \sla(G)\le 2

On the complexity of computing the handicap of a sufficient matrix

The class of sufficient matrices is important in the study of the linear complementarity problem (LCP)—some interior point methods (IPM’s) for LCP’s with sufficient data matrices have complexity polynomial in the bit size of the matrix and its handicap.In this paper we show that the handicap of a sufficient matrix may be exponential in its bit size, implying that the known complexity bounds of interior point methods are not polynomial in the input size of the LCP problem. We also introduce a semidefinite programming based heuristic, that provides a finite upper bond on the handicap, for the sub-class of Ρ-matrices (where all principal minors are positive)