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General framework for proximal point algorithms based on the notion of (A; )-maximal monotonicity (also referred to as (A; )- monotonicity in literature) is developed. Linear convergence analysis for this class of algorithms to the context of solving a general class of nonlin- ear variational inclusion problems is successfully achieved along with some results on the generalized resolvent corresponding to (A; )-monotonicity. The obtained results generalize and unify a wide range of investigations readily available in literature.

GENERAL FRAMEWORK FOR PROXIMAL POINT ALGORITHMS ON (A, η)-MAXIMAL MONOTONICIT FOR NONLINEAR VARIATIONAL INCLUSIONS