Convergence of min-sum-min message-passing for quadratic optimization

Abstract

We propose a new message-passing algorithm for the quadratic optimization problem. As opposed to the min-sum algorithm, the new algorithm involves two minimizations and one summation at each iteration. The new min-sum-min algorithm exploits feedback from last iteration in generating new messages, resembling the Jacobi- relaxation algorithm. We show that if the feedback signal is large enough, the min-sum-min algorithm is guaranteed to converge to the optimal solution. Experimental results show that the min-sum-min algorithm outperforms two reference methods w.r.t. the convergence speed. © 2014 Springer-Verlag.