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 positive semidefinite rank, and their symmetric analogues: the completely positive rank and the completely positive semidefinite rank. We study convergence properties of our hierarchies, compare them extensively to known lower bounds, and provide some (numerical) examples.Keywords Matrix factorization ranks · Nonnegative rank · Positive semidefinite rank · Completely positive rank · Completely positive semidefinite rank · Noncommutative polynomial optimization Mathematics Subject Classification (2010) 15A48 · 15A23 · 90C22 1 Introduction
Matrix factorization ranksA factorization of a matrix A ∈ R m×n over a sequence {K d } d∈N of cones that are each equipped with an inner product ·, · is a decomposition of the form A = ( X i ,Y j ) with X i ,Y j ∈ K d for all (i, j) ∈ [m] × [n], for some integer d ∈ N. Following [35], the