Complexity of the positive semidefinite matrix completion problem with a rank constraint
نویسندگان
چکیده
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.
منابع مشابه
The Positive Semidefinite Grothendieck Problem with Rank Constraint
Given a positive integer n and a positive semidefinite matrix A = (Aij ) ∈ R m×m the positive semidefinite Grothendieck problem with rank-nconstraint is (SDPn) maximize m
متن کاملComputational Limits for Matrix Completion
Matrix Completion is the problem of recovering an unknown real-valued low-rank matrix from a subsample of its entries. Important recent results show that the problem can be solved efficiently under the assumption that the unknown matrix is incoherent and the subsample is drawn uniformly at random. Are these assumptions necessary? It is well known that Matrix Completion in its full generality is...
متن کاملPositive Semidefinite Matrix Completions on Chordal Graphs and Constraint Nondegeneracy in Semidefinite Programming
Let G = (V, E) be a graph. In matrix completion theory, it is known that the following two conditions are equivalent: (i) G is a chordal graph; (ii) Every G-partial positive semidefinite matrix has a positive semidefinite matrix completion. In this paper, we relate these two conditions to constraint nondegeneracy condition in semidefinite programming and prove that they are each equivalent to (...
متن کاملPositive Semidefinite Metric Learning with Boosting
The learning of appropriate distance metrics is a critical problem in image classification and retrieval. In this work, we propose a boosting-based technique, termed BOOSTMETRIC, for learning a Mahalanobis distance metric. One of the primary difficulties in learning such a metric is to ensure that the Mahalanobis matrix remains positive semidefinite. Semidefinite programming is sometimes used t...
متن کاملExploiting sparsity in linear and nonlinear matrix inequalities via positive semidefinite matrix completion
Abstract A basic framework for exploiting sparsity via positive semidefinite matrix completion is presented for an optimization problem with linear and nonlinear matrix inequalities. The sparsity, characterized with a chordal graph structure, can be detected in the variable matrix or in a linear or nonlinear matrix-inequality constraint of the problem. We classify the sparsity in two types, the...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2012