Linear programming with nonparametric penalty programs and iterated thresholding

It is known [Mangasarian, A Newton method for linear programming, J. Optim. Theory Appl. 121 (2004), pp. 1–18] that every linear program can be solved exactly by minimizing an unconstrained quadratic penalty program. The penalty program is parameterized by a scalar t>0, and one is able to solve the original linear program in this manner when t is selected larger than a finite, but unknown . In this paper, we show that every linear program can be solved using the solution to a parameter-free penalty program. We also characterize the solutions to the quadratic penalty programs using fixed points of certain nonexpansive maps. This leads to an iterative thresholding algorithm that converges to a desired limit point. We show in numerical experiments that this iterative method can outperform a variety of standard quadratic program solvers. Finally, we show that for every , the solution one obtains by solving a parameterized penalty program is guaranteed to lie in the feasible set of the original linear program.