An Algorithm for Maximizing a Convex Function Based on Its Minimum

In this paper, an algorithm for maximizing a convex function over a convex feasible set is proposed. The algorithm, called CoMax, consists of two phases: in phase 1, a feasible starting point is obtained that is used in a gradient ascent algorithm in phase 2. The main contribution of the paper is connected to phase 1; five different methods are used to approximate the original NP-hard problem of maximizing a convex function (MCF) by a tractable convex optimization problem. All the methods use the minimizer of the convex objective function in their construction. In phase 2, the gradient ascent algorithm yields stationary points to the MCF problem. The performance of CoMax is tested on a wide variety of MCF problems, demonstrating its efficiency.