Deterministic guarantees for Burer-Monteiro factorizations of smooth semidefinite programs
This provides deterministic guarantees for a widely-used heuristic in SDP relaxations, benefiting optimization practitioners in machine learning and signal processing.
The paper proves that for smooth semidefinite programs with equality constraints, the non-convex Burer-Monteiro factorization yields global optimality under first- and second-order necessary conditions when the constraint set is a smooth manifold and the factor size is sufficiently large. For smaller factor sizes, a similar result holds for almost all linear cost functions.
We consider semidefinite programs (SDPs) with equality constraints. The variable to be optimized is a positive semidefinite matrix $X$ of size $n$. Following the Burer--Monteiro approach, we optimize a factor $Y$ of size $n \times p$ instead, such that $X = YY^T$. This ensures positive semidefiniteness at no cost and can reduce the dimension of the problem if $p$ is small, but results in a non-convex optimization problem with a quadratic cost function and quadratic equality constraints in $Y$. In this paper, we show that if the set of constraints on $Y$ regularly defines a smooth manifold, then, despite non-convexity, first- and second-order necessary optimality conditions are also sufficient, provided $p$ is large enough. For smaller values of $p$, we show a similar result holds for almost all (linear) cost functions. Under those conditions, a global optimum $Y$ maps to a global optimum $X = YY^T$ of the SDP. We deduce old and new consequences for SDP relaxations of the generalized eigenvector problem, the trust-region subproblem and quadratic optimization over several spheres, as well as for the Max-Cut and Orthogonal-Cut SDPs which are common relaxations in stochastic block modeling and synchronization of rotations.