Approximating the little Grothendieck problem over the orthogonal and unitary groups

• 成果类型: Article
• 署名作者: Bandeira, Afonso S.; Kennedy, Christopher; Singer, Amit
• 署名单位: Massachusetts Institute of Technology (MIT); University of Texas System; University of Texas Austin; Princeton University; Princeton University
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-016-0993-7
• 发表日期: 2016
• 页码: 433-475
• 关键词: quadratic optimization; RANDOM MATRICES; semidefinite; eigenvectors; inequalities; algorithms; sums; cut

摘要

The little Grothendieck problem consists of maximizing for a positive semidefinite matrix C, over binary variables . In this paper we focus on a natural generalization of this problem, the little Grothendieck problem over the orthogonal group. Given a positive semidefinite matrix, the objective is to maximize restricting to take values in the group of orthogonal matrices , where denotes the (ij)-th 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 and show a constant approximation ratio. Our method is based on semidefinite programming. For a given , we show a constant approximation ratio of , where is the expected average singular value of a matrix with random Gaussian i.i.d. entries. For we recover the known 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 . 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 . 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 , the sharp, known ratios.