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. Some weighted Euclidean norms are applied to finding a descent direction for improving the solution. These norms vary with iterations. A theoretical justification of the algorithms with some assumptions (including the nonsingularity of the problem) is presented.