Precompact convergence of the nonconvex primal-dual hybrid gradient algorithm
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Publication:1675933
DOI10.1016/j.cam.2017.07.037zbMath1397.90318OpenAlexW2747285118MaRDI QIDQ1675933
Tao Sun, Roberto Barrio, Hao Jiang, Li-Zhi Cheng
Publication date: 3 November 2017
Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.cam.2017.07.037
convergence analysisnonconvex optimizationKurdyka-Łojasiewic functionsprimal-dual hybrid gradient algorithms
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Cites Work
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- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- Douglas-Rachford splitting for nonconvex optimization with application to nonconvex feasibility problems
- Proximal alternating linearized minimization for nonconvex and nonsmooth problems
- A unified primal-dual algorithm framework based on Bregman iteration
- On the convergence of the proximal algorithm for nonsmooth functions involving analytic features
- On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators
- A dual algorithm for the solution of nonlinear variational problems via finite element approximation
- On gradients of functions definable in o-minimal structures
- Peaceman-Rachford splitting for a class of nonconvex optimization problems
- A first-order primal-dual algorithm for convex problems with applications to imaging
- Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward-backward splitting, and regularized Gauss-Seidel methods
- On the convergence rate improvement of a primal-dual splitting algorithm for solving monotone inclusion problems
- A General Framework for a Class of First Order Primal-Dual Algorithms for Convex Optimization in Imaging Science
- Proximal Alternating Minimization and Projection Methods for Nonconvex Problems: An Approach Based on the Kurdyka-Łojasiewicz Inequality
- The Numerical Solution of Parabolic and Elliptic Differential Equations
- Global Convergence of Splitting Methods for Nonconvex Composite Optimization
- Efficient Schemes for Total Variation Minimization Under Constraints in Image Processing
- On the convergence rate of a forward-backward type primal-dual splitting algorithm for convex optimization problems
- On the Convergence of Primal-Dual Hybrid Gradient Algorithm
- The Primal-Dual Hybrid Gradient Method for Semiconvex Splittings
- A Primal-Dual Splitting Algorithm for Finding Zeros of Sums of Maximal Monotone Operators
- Signal Recovery by Proximal Forward-Backward Splitting
- A Nonlinear Alternating Direction Method
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