Approximating the little Grothendieck problem over the orthogonal and unitary groups
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10107_2016_Article_993.pdf
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Author(s)
Kennedy, Christopher
Singer, Amit
Sousa Bandeira, Afonso Jose
Date Issued
March 2016
Journal
Mathematical Programming
Publisher
Springer Berlin Heidelberg
Citation
Bandeira, Afonso S., Christopher Kennedy, and Amit Singer. “Approximating the Little Grothendieck Problem over the Orthogonal and Unitary Groups.” Mathematical Programming (2016): n. pag.
Version
Author's final manuscript
Abstract
The little Grothendieck problem consists of maximizing ∑[subscript ij]C[subscript ij]x[subscript i]x[subscript j] for a positive semidefinite matrix C, over binary variables x[subscript i]∈{±1}. In this paper we focus on a natural generalization of this problem, the little Grothendieck problem over the orthogonal group. Given C∈R[superscript dn×dn] a positive semidefinite matrix, the objective is to maximize ∑[subscript ij]tr(C[superscript T][subscript ij]O[subscript i]O[superscript T][subscript j] restricting O[subscript i] to take values in the group of orthogonal matrices O[subscript d], where C[subscript ij] denotes the (ij)-th d×d block of C. We propose an approximation algorithm, which we refer to as Orthogonal-Cut, to solve the little Grothendieck problem over the group of orthogonal matrices O[subscript d] and show a constant approximation ratio. Our method is based on semidefinite programming. For a given d≥1, we show a constant approximation ratio of α[subscript R](d)[superscript 2], where α[subscript R](d) is the expected average singular value of a d×d matrix with random Gaussian N(0,1/d) i.i.d. entries. For d=1 we recover the known α[subscript R](1)[superscript 2]=2/π approximation guarantee for the classical little Grothendieck problem. Our algorithm and analysis naturally extends to the complex valued case also providing a constant approximation ratio for the analogous little Grothendieck problem over the Unitary Group U[subscript d]. Orthogonal-Cut also serves as an approximation algorithm for several applications, including the Procrustes problem where it improves over the best previously known approximation ratio of 1/2√2 . The little Grothendieck problem falls under the larger class of problems approximated by a recent algorithm proposed in the context of the non-commutative Grothendieck inequality. Nonetheless, our approach is simpler and provides better approximation with matching integrality gaps. Finally, we also provide an improved approximation algorithm for the more general little Grothendieck problem over the orthogonal (or unitary) group with rank constraints, recovering, when d=1, the sharp, known ratios.
MIT Department
Massachusetts Institute of Technology. Department of Mathematics
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DOI of Published Version
https://doi.org/10.1007/s10107-016-0993-7
