A quadratically convergent global algorithm for the linearly-constrained minimum cross-entropy problem

Abstract

In this paper, we propose a curved-search algorithm for solving the cross-entropy minimization problem with linear equality constrains. The proposed algorithm converges globally to a dual optimal solution with a quadratic rate of convergence. A dual-to-primal conversion formula is provided. We also analyze the computational effort required for the algorithm and report our computational experience.