New augmented Lagrangian-based proximal point algorithm for convex optimization with equality constraints
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Publication:727234
DOI10.1007/s10957-016-0991-1zbMath1354.65127OpenAlexW2519079961MaRDI QIDQ727234
Publication date: 6 December 2016
Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10957-016-0991-1
algorithmconvergenceconvex optimizationaugmented Lagrangiannumerical resultproximal point algorithmcompressed sensingsparse optimization
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- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- Smooth minimization of non-smooth functions
- A unified primal-dual algorithm framework based on Bregman iteration
- An extension of Karmarkar's projective algorithm for convex quadratic programming
- On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators
- Multiplier methods: A survey
- A proximal-based deomposition method for compositions method for convex minimization problems
- A hybrid approximate extragradient-proximal point algorithm using the enlargement of a maximal monotone operator
- A first-order primal-dual algorithm for convex problems with applications to imaging
- Exact matrix completion via convex optimization
- A Fast Algorithm for Sparse Reconstruction Based on Shrinkage, Subspace Optimization, and Continuation
- Analysis and Generalizations of the Linearized Bregman Method
- Linearized Bregman iterations for compressed sensing
- Convergence of the linearized Bregman iteration for ℓ₁-norm minimization
- A Singular Value Thresholding Algorithm for Matrix Completion
- Alternating Direction Algorithms for $\ell_1$-Problems in Compressive Sensing
- A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
- The Split Bregman Method for L1-Regularized Problems
- Decoding by Linear Programming
- Probing the Pareto Frontier for Basis Pursuit Solutions
- An Efficient TVL1 Algorithm for Deblurring Multichannel Images Corrupted by Impulsive Noise
- Augmented Lagrangians and Applications of the Proximal Point Algorithm in Convex Programming
- Atomic Decomposition by Basis Pursuit
- An Augmented Lagrangian Approach to the Constrained Optimization Formulation of Imaging Inverse Problems
- Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
- Signal Recovery by Proximal Forward-Backward Splitting
- Robustness of the hybrid extragradient proximal-point algorithm.
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