5 research outputs found
Algorithms for Positive Semidefinite Factorization
This paper considers the problem of positive semidefinite factorization (PSD
factorization), a generalization of exact nonnegative matrix factorization.
Given an -by- nonnegative matrix and an integer , the PSD
factorization problem consists in finding, if possible, symmetric -by-
positive semidefinite matrices and such
that for , and . PSD
factorization is NP-hard. In this work, we introduce several local optimization
schemes to tackle this problem: a fast projected gradient method and two
algorithms based on the coordinate descent framework. The main application of
PSD factorization is the computation of semidefinite extensions, that is, the
representations of polyhedrons as projections of spectrahedra, for which the
matrix to be factorized is the slack matrix of the polyhedron. We compare the
performance of our algorithms on this class of problems. In particular, we
compute the PSD extensions of size for the
regular -gons when , and . We also show how to generalize our
algorithms to compute the square root rank (which is the size of the factors in
a PSD factorization where all factor matrices and have rank one)
and completely PSD factorizations (which is the special case where the input
matrix is symmetric and equality is required for all ).Comment: 21 pages, 3 figures, 3 table
Lower bounds on matrix factorization ranks via noncommutative polynomial optimization
We use techniques from (tracial noncommutative) polynomial optimization to formulate hierarchies of semidefinite programming lower bounds on matrix factorization ranks. In particular, we consider the nonnegative rank, the completely positive rank, and their symmetric analogues: the positive semidefinite rank and the completely positive semidefinite rank. We study the convergence properties of our hierarchies, compare them extensively to known lower bounds, and provide some (numerical) examples