Approximation and regularization properties of augmented penalty functions in convex programming

The possibilities of using augmented penalty functions for the regularization and optimal correction of convex programming problems are investigated. Convergence conditions are formulated for the corresponding methods and an iteration algorithm for a linear optimization problem is proposed.