A unifying approach to entropy-like multiplier methods in convex programming

Entropy-like multiplier methods solve the convex constrained minimization problem by solving its dual. To regularize optimization process a distance function parametrized by the current estimate of the Lagrangian multipliers is added to the Lagrangian of the problem, to enforce positivity of these multipliers the chosen function is a so-called entropy-like function which is defined on the positive orthant. In this paper we are concerned with externing available convergence results without giving some specific form to this entropy-like function. Several examples of applicable entropy-like functions are presented.