Bregman Proximal Relaxation of Large-Scale 0–1 Problems
Computational Optimization and Applications
Dual convergence of the proximal point method with Bregman distances for linear programming
Optimization Methods & Software
A Continuous Dynamical Newton-Like Approach to Solving Monotone Inclusions
SIAM Journal on Control and Optimization
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We study the relationships between three concepts which arise in connection with variational inequality problems: central paths defined by arbitrary barriers, generalized proximal point methods (where a Bregman distance substitutes for the Euclidean one), and Cauchy trajectory in Riemannian manifolds. First we prove that under rather general hypotheses the central path defined by a general barrier for a monotone variational inequality problem is well defined, bounded, and continuous and converges to the analytic center of the solution set (with respect to the given barrier), thus generalizing results which deal only with complementarity problems and with the logarithmic barrier. Next we prove that a sequence generated by the proximal point method with the Bregman distance naturally induced by the barrier function converges precisely to the same point. Furthermore, for a certain class of problems (including linear programming), such a sequence is contained in the central path, making the concepts of central path and generalized proximal point sequence virtually equivalent. Finally we prove that for this class of problems the central path also coincides with the Cauchy trajectory in the Riemannian manifold defined on the positive orthant by a metric given by the Hessian of the barrier (i.e., a curve whose direction at each point is the negative gradient of the objective function at that point in the Riemannian metric).