This book brings together groundbreaking research papers authored by distinguished experts and scholars. Each contribution delves into innovative aspects of splitting optimization ranging from theoretical advancements and methodological developments to a diverse array of applications. The book offers readers a comprehensive and cohesive overview of the current state of splitting optimization, while inspiring continued research and innovation in this dynamic field.
This book brings together groundbreaking research papers authored by distinguished experts and scholars. Each contribution delves into innovative aspects of splitting optimization ranging from theoretical advancements and methodological developments to a diverse array of applications. The book offers readers a comprehensive and cohesive overview of the current state of splitting optimization, while inspiring continued research and innovation in this dynamic field.
Xiangfeng Wang is a full professor at East China Normal University. Xingju Cai is a full professor Nanjing Normal University. Deren Han is a full professor at Beihang University.
Inhaltsangabe
A trust region based splitting method for optimization problems with linear constraints.- Forward reflected backward method with extrapolation and linesearch for monotone inclusion problem.- Fast adaptive ADMM with Gaussian back substitution for multiple block linear constrainted seperable problems.- Inertial alternating direction method of multipliers with logarithmic-quadratic proximal regularization.- A class of augmented Lagrangian-type algorithms for solving generalized Nash equilibrium problems.
A trust region based splitting method for optimization problems with linear constraints.- Forward reflected backward method with extrapolation and linesearch for monotone inclusion problem.- Fast adaptive ADMM with Gaussian back substitution for multiple block linear constrainted seperable problems.- Inertial alternating direction method of multipliers with logarithmic-quadratic proximal regularization.- A class of augmented Lagrangian-type algorithms for solving generalized Nash equilibrium problems.
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