A class of customized proximal point algorithms for linearly constrained convex optimization
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Publication:725706
DOI10.1007/s40314-016-0371-3OpenAlexW2517952166MaRDI QIDQ725706
Publication date: 2 August 2018
Published in: Computational and Applied Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s40314-016-0371-3
Convex programming (90C25) Nonlinear programming (90C30) Numerical optimization and variational techniques (65K10)
Related Items (13)
General parameterized proximal point algorithm with applications in statistical learning ⋮ Inertial accelerated primal-dual methods for linear equality constrained convex optimization problems ⋮ Modified proximal symmetric ADMMs for multi-block separable convex optimization with linear constraints ⋮ An accelerated proximal augmented Lagrangian method and its application in compressive sensing ⋮ Some extensions of the operator splitting schemes based on Lagrangian and primal–dual: a unified proximal point analysis ⋮ Two convergent primal-dual hybrid gradient type methods for convex programming with linear constraints ⋮ A generalized forward-backward splitting operator: degenerate analysis and applications ⋮ Approximate customized proximal point algorithms for separable convex optimization ⋮ The operator splitting schemes revisited: primal-dual gap and degeneracy reduction by a unified analysis ⋮ A prediction-correction-based primal-dual hybrid gradient method for linearly constrained convex minimization ⋮ A parameterized proximal point algorithm for separable convex optimization ⋮ Improved Lagrangian-PPA based prediction correction method for linearly constrained convex optimization ⋮ Convergence results of two-step inertial proximal point algorithm
Cites Work
- A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
- PPA-like contraction methods for convex optimization: a framework using variational inequality approach
- Fixed point and Bregman iterative methods for matrix rank minimization
- Customized proximal point algorithms for linearly constrained convex minimization and saddle-point problems: a unified approach
- A first-order primal-dual algorithm for convex problems with applications to imaging
- Accelerated linearized Bregman method
- On the proximal Jacobian decomposition of ALM for multiple-block separable convex minimization problems and its relationship to ADMM
- Alternating proximal gradient method for convex minimization
- A customized Douglas-Rachford splitting algorithm for separable convex minimization with linear constraints
- Multiplier and gradient methods
- A customized proximal point algorithm for convex minimization with linear constraints
- Convergence Analysis of Primal-Dual Algorithms for a Saddle-Point Problem: From Contraction Perspective
- Linearized Bregman iterations for compressed sensing
- A Singular Value Thresholding Algorithm for Matrix Completion
- The Split Bregman Method for L1-Regularized Problems
- A Proximal Strictly Contractive Peaceman--Rachford Splitting Method for Convex Programming with Applications to Imaging
- Bregmanized Nonlocal Regularization for Deconvolution and Sparse Reconstruction
- Monotone Operators and the Proximal Point Algorithm
- An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
- Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization
- Dual–primal proximal point algorithms for extended convex programming
- Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
- Convex analysis and monotone operator theory in Hilbert spaces
- Sparse Image and Signal Processing
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