Momentum-inspired low-rank coordinate descent for diagonally constrained SDPs

Abstract

We present a novel, practical, and provable approach for solving diagonally constrained semi-definite programming (SDP) problems at scale using accelerated non-convex programming. Our algorithm non-trivially combines acceleration motions from convex optimization with coordinate power iteration and matrix factorization techniques. The algorithm is extremely simple to implement, and adds only a single extra hyperparameter – momentum. We prove that our method admits local linear convergence in the neighborhood of the optimum and always converges to a first-order critical point. Experimentally, we showcase the merits of our method on three major application domains$:$ MaxCut, MaxSAT, and MIMO signal detection. In all cases, our methodology provides significant speedups over non-convex and convex SDP solvers – 5X faster than state-of-the-art non-convex solvers, and 9 to 10^3 X faster than convex SDP solvers – with comparable or improved solution quality.

Publication
Under review