In this paper we consider approximation algorithms for a class of quadratic optimization problems that contain orthogonality constraints, i.e. constraints of the form $X^TX=I$, where $X \in {\mathbb R}^{m \times n}$ is the optimization variable. Such class of problems, which we denote by (QP-OC), is quite general and captures several well--studied problems in the literature as special cases. In a recent work, Nemirovski [Math. Prog. 109:283--317, 2007] gave the first non--trivial approximation algorithm for (QP-OC). His algorithm is based on semidefinite programming and has an approximation guarantee of $O\left((m+n)^{1/3}\right)$. We improve upon this result by providing the first logarithmic approximation guarantee for (QP-OC). Specifically, we show that (QP-OC) can be approximated to within a factor of $O\left(\ln\left(\max\{m,n\}\right)\right)$. The main technical tool used in the analysis is a concentration inequality for the spectral norm of a sum of certain random matrices, which we develop using tools from functional analysis. Such inequality also has ramifications in the design of so--called safe tractable approximations of chance constrained optimization problems. In particular, we use it to improve a recent result of Ben--Tal and Nemirovski [Manuscript, 2007] on certain chance constrained linear matrix inequality systems.

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View Improved Approximation Bound for Quadratic Optimization Problems with Orthogonality Constraints