Nonlinear functional analysis and its applications
Nonlinear functional analysis and its applications
SIAM Journal on Control and Optimization
Mathematical Programming: Series A and B
Nonlinear proximal point algorithms using Bregman functions, with applications to convex programming
Mathematics of Operations Research
Approximate iterations in Bregman-function-based proximal algorithms
Mathematical Programming: Series A and B
A Modified Forward-Backward Splitting Method for Maximal Monotone Mappings
SIAM Journal on Control and Optimization
Mathematics of Operations Research
Alternating Projection-Proximal Methods for Convex Programming and Variational Inequalities
SIAM Journal on Optimization
Inexact Variants of the Proximal Point Algorithm without Monotonicity
SIAM Journal on Optimization
Local Convergence of the Proximal Point Algorithm and Multiplier Methods Without Monotonicity
Mathematics of Operations Research
The over-relaxed proximal point algorithm based on H-maximal monotonicity design and applications
Computers & Mathematics with Applications
A new system of variational inclusions with (H, η)-monotone operators in hilbert spaces
Computers & Mathematics with Applications
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A general design for the Eckstein-Bertsekas proximal point algorithm, using the notion of the A-maximal monotonicity, is developed. Convergence analysis for the generalized Eckstein-Bertsekas proximal point algorithm in the context of solving a class of nonlinear inclusion problems is explored. Some auxiliary results of interest involving A-maximal monotone mappings are also included. The obtained results generalize investigations on general maximal monotonicity and beyond.