General framework for proximal point algorithms on (A, η)-maximal monotonicity for nonlinear variational inclusions
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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.
Abstract
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.
참고문헌 (17)
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