General Framework for Proximal Point Algorithms on (a, Η)-maximal Monotonicity for Nonlinear Variational Inclusions
نویسنده
چکیده
General framework for proximal point algorithms based on the notion of (A, η)-maximal monotonicity (also referred to as (A, η)monotonicity in literature) is developed. Linear convergence analysis for this class of algorithms to the context of solving a general class of nonlinear variational inclusion problems is successfully achieved along with some results on the generalized resolvent corresponding to (A, η)-monotonicity. The obtained results generalize and unify a wide range of investigations readily available in literature.
منابع مشابه
Super-Relaxed (η)-Proximal Point Algorithms, Relaxed (η)-Proximal Point Algorithms, Linear Convergence Analysis, and Nonlinear Variational Inclusions
We glance at recent advances to the general theory of maximal set-valued monotone mappings and their role demonstrated to examine the convex programming and closely related field of nonlinear variational inequalities. We focus mostly on applications of the super-relaxed η proximal point algorithm to the context of solving a class of nonlinear variational inclusion problems, based on the notion ...
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