Convergence rate of inexact proximal point methods with relative error criteria for convex optimization
نویسندگان
چکیده
In this paper, we consider a framework of inexact proximal point methods for convex optimization that allows a relative error tolerance in the approximate solution of each proximal subproblem and establish its convergence rate. We then show that the well-known forward-backward splitting algorithm for convex optimization belongs to this framework. Finally, we propose and establish the iteration-complexity of an inexact forward-backward splitting algorithm for solving optimization problems whose objective functions are obtained by maximizing convex-concave saddle functions.
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