Lower bounds on matrix factorization ranks via noncommutative polynomial optimization

Sander Gribling; David de Laat; Monique Laurent

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arXiv · 1708.01573

Lower bounds on matrix factorization ranks via noncommutative polynomial optimization

Sander Gribling·David de Laat·Monique Laurent

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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.

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Sander Gribling, David de Laat, Monique Laurent. 2017-08-04. Lower bounds on matrix factorization ranks via noncommutative polynomial optimization. https://arxiv.org/abs/1708.01573

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