Faster Lagrangian-Based Methods in Convex Optimization
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Publication:5062120
DOI10.1137/20M1375358zbMath1486.90149arXiv2010.14314OpenAlexW4214591022MaRDI QIDQ5062120
Publication date: 15 March 2022
Published in: SIAM Journal on Optimization (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2010.14314
nonsmooth optimizationLagrangian multiplier methodsaugmented Lagrangianalternating direction method of multiplierconvex composite minimizationproximal multiplier algorithmsfast nonergodic global rate of convergence
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Cites Work
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- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
- On the ergodic convergence rates of a first-order primal-dual algorithm
- 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
- A proximal-based deomposition method for compositions method for convex minimization problems
- A first-order primal-dual algorithm for convex problems with applications to imaging
- Accelerated alternating direction method of multipliers: an optimal \(O(1 / K)\) nonergodic analysis
- On non-ergodic convergence rate of Douglas-Rachford alternating direction method of multipliers
- Multiplier and gradient methods
- On the $O(1/n)$ Convergence Rate of the Douglas–Rachford Alternating Direction Method
- Lagrangian methods for composite optimization
- On Full Jacobian Decomposition of the Augmented Lagrangian Method for Separable Convex Programming
- Monotone Operators and the Proximal Point Algorithm
- Augmented Lagrangians and Applications of the Proximal Point Algorithm in Convex Programming
- Accelerated Optimization for Machine Learning
- Rate of Convergence Analysis of Decomposition Methods Based on the Proximal Method of Multipliers for Convex Minimization
- Iteration-Complexity of Block-Decomposition Algorithms and the Alternating Direction Method of Multipliers
- Interior Gradient and Proximal Methods for Convex and Conic Optimization
- Convex Analysis
- An introduction to continuous optimization for imaging
- Iteration-complexity of first-order augmented Lagrangian methods for convex programming
