Entropy-like proximal methods in convex programming

Several entropy-like proximal mapping methods for convex programming are investigated. Such an approach replaces quadratic regularization kernels with convex statistical distances known as phi-divergences. To this end, the convergence characteristics of the iterative algorithms generated by the use of such schemes for convex programming are studied. This is followed by the application of the resulting algorithm to the dual of a convex program. The results show a wide variety of nonquadratic multiplier techniques and confirm their convergence.