New augmented Lagrangian-based proximal point algorithm for convex optimization with equality constraints
type: Item
instance of: scholarly article
title: New augmented Lagrangian-based proximal point algorithm for convex optimization with equality constraints
zbMATH Open document ID: 1354.65127
author: Yuan Shen
author: Hong-yong Wang
published in: Journal of Optimization Theory and Applications
publication date: 2016-12-06T00:00:00Z
Mathematics Subject Classification ID: 65K05
Mathematics Subject Classification ID: 90C25
zbMATH DE Number: 6661398
zbMATH Keywords: convex optimization
zbMATH Keywords: proximal point algorithm
zbMATH Keywords: augmented Lagrangian
zbMATH Keywords: sparse optimization
zbMATH Keywords: compressed sensing
zbMATH Keywords: algorithm
zbMATH Keywords: convergence
zbMATH Keywords: numerical result
describes a project that uses: FPC_AS
describes a project that uses: SPGL1
describes a project that uses: RecPF
describes a project that uses: FTVd
describes a project that uses: PDCO
MaRDI profile type: Publication
full work available at URL: s10957-016-0991-1
OpenAlex ID: W2519079961
cites work: Q5491042
cites work: Atomic Decomposition by Basis Pursuit
cites work: Decoding by Linear Programming
cites work: A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
cites work: An Efficient TVL1 Algorithm for Deblurring Multichannel Images Corrupted by Impulsive Noise
cites work: A Singular Value Thresholding Algorithm for Matrix Completion
cites work: Exact matrix completion via convex optimization
cites work: Robustness of the hybrid extragradient proximal-point algorithm.
cites work: An extension of Karmarkar's projective algorithm for convex quadratic programming
cites work: Q3690580
cites work: Q3038523
cites work: Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
cites work: Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
cites work: Probing the Pareto Frontier for Basis Pursuit Solutions
cites work: An Augmented Lagrangian Approach to the Constrained Optimization Formulation of Imaging Inverse Problems
cites work: The Split Bregman Method for L1-Regularized Problems
cites work: On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators
cites work: Multiplier methods: A survey
cites work: A proximal-based deomposition method for compositions method for convex minimization problems
cites work: Alternating Direction Algorithms for $\ell_1$-Problems in Compressive Sensing
cites work: Augmented Lagrangians and Applications of the Proximal Point Algorithm in Convex Programming
cites work: A hybrid approximate extragradient-proximal point algorithm using the enlargement of a maximal monotone operator
cites work: Signal Recovery by Proximal Forward-Backward Splitting
cites work: A first-order primal-dual algorithm for convex problems with applications to imaging
cites work: A unified primal-dual algorithm framework based on Bregman iteration
cites work: Linearized Bregman iterations for compressed sensing
cites work: Convergence of the linearized Bregman iteration for ℓ₁-norm minimization
cites work: Analysis and Generalizations of the Linearized Bregman Method
cites work: Smooth minimization of non-smooth functions
cites work: A Fast Algorithm for Sparse Reconstruction Based on Shrinkage, Subspace Optimization, and Continuation
DOI: 10.1007/S10957-016-0991-1
description: scientific article; zbMATH DE number 6661398