Classes of Linear Programs Solvable by Coordinate-Wise Minimization

Tomas Werner

Abstract: Coordinate-wise minimization is a simple popular method for large-scale optimization. Unfortunately, for general (non-differentiable and/or constrained) convex problems, its fixed points may not be global minima. We present two classes of linear programs (LPs) that coordinate-wise minimization solves exactly. We show that these classes subsume the dual LP relaxations of several well-known combinatorial optimization problems and the method finds a global minimum with sufficient accuracy in reasonable runtimes. Moreover, we experimentally show that the method frequently yields good suboptima or even optima for sparse LPs where optimality is not guaranteed in theory. Though the presented problems can be solved by more efficient methods, our results are theoretically non-trivial and can lead to new large-scale optimization algorithms in the future.