Convergence analysis on an accelerated proximal point algorithm for linearly constrained optimization problems

DOI10.1155/2020/8873507zbMath1459.90156OpenAlexW3101819263MaRDI QIDQ2214872

Sha Lu, Zeng-xin Wei

Publication date: 10 December 2020

Published in: Mathematical Problems in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2020/8873507

Mathematics Subject Classification ID

Convex programming (90C25) Nonlinear programming (90C30) Programming in abstract spaces (90C48)

Cites Work

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• A general approach to convergence properties of some methods for nonsmooth convex optimization
• Augmented Lagrangian algorithms for linear programming
• The rate of convergence of proximal method of multipliers for equality constrained optimization problems
• An accelerated augmented Lagrangian method for linearly constrained convex programming with the rate of convergence \(O\left({1/{k^2}} \right)\)
• On the Convergence of the Proximal Point Algorithm for Convex Minimization
• New Proximal Point Algorithms for Convex Minimization
• Monotone Operators and the Proximal Point Algorithm
• Augmented Lagrangians and Applications of the Proximal Point Algorithm in Convex Programming
• Variational Analysis
• Relaxing Tight Frame Condition in Parallel Proximal Methods for Signal Restoration
• Accelerated First-Order Primal-Dual Proximal Methods for Linearly Constrained Composite Convex Programming
• General parameterized proximal point algorithm with applications in statistical learning
• Convex Analysis

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