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Lower bounds on matrix factorization ranks via noncommutative polynomial optimization

Sander Gribling(gribling***at***cwi.nl)
David de Laat(mail***at***daviddelaat.nl)
Monique Laurent(m.laurent***at***cwi.nl)

Abstract: 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 the convergence properties of our hierarchies, compare them extensively to known lower bounds, and provide some (numerical) examples.

Keywords: Matrix factorization ranks, noncommutative polynomial optimization

Category 1: Linear, Cone and Semidefinite Programming (Semi-definite Programming )

Citation:

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Entry Submitted: 08/31/2017
Entry Accepted: 08/31/2017
Entry Last Modified: 08/31/2017

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