THE EXTRAGRADIENT METHOD FOR CONVEX OPTIMIZATION IN THE PRESENCE OF COMPUTATIONAL ERRORS

In this article, we study convergence of the extragradient method for constrained convex minimization problems in a Hilbert space. Our goal is to obtain an epsilon-approximate solution of the problem in the presence of computational errors, where epsilon is a given positive number. Most results known in the literature establish convergence of optimization algorithms, when computational errors are summable. In this article, the convergence of the extragradient method for solving convex minimization problems is established for nonsummable computational errors. We show that the the extragradient method generates a good approximate solution, if the sequence of computational errors is bounded from above by a constant.