Theoretical justification of interior point algorithms for solving optimization problems with nonlinear constraints

A family of interior point algorithms is considered. These algorithms can be used for solving mathematical programming problems with nonlinear inequality constraints. The weighted Euclidean rates are applied to find a descent direction for improving a solution. These rates are varying in iterations. Theoretical justification of the algorithms with some assumptions (such as non-degeneracy of a problem) is presented.