# Bregman distance

Given a convex closed set with non-empty interior and a Bregman function with zone , the Bregman distance is defined as: Bregman distances were introduced in [a1]. For several examples of Bregman distances for relevant sets , see Bregman function. It follows easily from the properties of Bregman functions that for all and all , that if and only if and that is a convex function (cf. also convex function (of a real variable)) for all . In general, does not satisfy the triangle inequality, it is not symmetric (i.e. it is not true that for all , ) and is not convex. If and either is symmetric or is convex for all , then is a quadratic function and is the square of an elliptic norm. A basic property of Bregman distances, which follows easily from the definition, is the following:  for all , and all . Given a closed convex set such that , the Bregman projection onto , , is defined as The properties of Bregman distances ensure existence and uniqueness of for all . Given closed convex sets such that for all and all (such sets are said to be zones consistent with ), it is interesting to consider a sequence of successive Bregman projections onto the convex sets , i.e. the sequence with and iterative formula given by where is the index of the convex set used in the th iteration (for instance cyclically, i.e. ). This algorithm, called Bregman's method, converges to a point in if is non-empty (see [a1]). It has been proved in [a1] that if all the sets are hyperplanes, then the limit of the sequence is also the unique solution of , subject to . This property also holds for an underrelaxed version of the method, of the type where is a hyperplane parallel to and lying between and (see [a3]). Under suitable modifications in the definition of the hyperplane , the method has been extended to the case of minimization of subject to linear inequalities and linear interval constraints (see [a2], [a5]). The entropy maximization method known as MART (multiplicative algebraic reconstruction technique, see [a4]) is a particular case of Bregman's method with (the non-negative orthant of ) and , under a specific underrelaxation strategy.

Bregman distances have also been used to generate generalized proximal point methods for convex optimization and variational inequalities (cf. Proximal point methods in mathematical programming).