Generalized distances give rise to generalized projections into convex sets. An important
question is whether or not one can use within the same projection algorithm different types …

This monograph provides both a unified account of the development of models and methods
for the problem of estimating equilibrium traffic flows in urban areas and a survey of the …

An alternative convergence proof of a proximal-like minimization algorithm using Bregman
functions, recently proposed by Censor and Zenios, is presented. The analysis allows the …

The coordinate descent method enjoys a long history in convex differentiable minimization.
Surprisingly, very little is known about the convergence of the iterates generated by this …

The convex feasibility problem, that is, nding a point in the intersection of nitely many closed
convex sets in Euclidean space, arises in various areas of mathematics and physical …

The original proximal minimization algorithm employs quadratic additive terms in the
objectives of the subproblems. In this paper, we replace these quadratic additive terms by …

A Bregman function is a strictly convex, differentiable function that induces a well-behaved
distance measure or D-function on Euclidean space. This paper shows that, for every …

A broad class of optimization algorithms based on Bregman distances in Banach spaces is
unified around the notion of Bregman monotonicity. A systematic investigation of this notion …

Since I started working in the area of nonlinear programming and, later on, variational
inequality problems, I have frequently been surprised to find that many algorithms, however …

We introduce a family of new transforms based on imitating the proximal map** of Moreau
and the associated Moreau-Yosida proximal approximation of a function. The transforms are …