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ADMM

Alternating Direction Method of Multipliers

GeneralIntroduced 2000398 papers

Description

The alternating direction method of multipliers (ADMM) is an algorithm that solves convex optimization problems by breaking them into smaller pieces, each of which are then easier to handle. It takes the form of a decomposition-coordination procedure, in which the solutions to small local subproblems are coordinated to find a solution to a large global problem. ADMM can be viewed as an attempt to blend the benefits of dual decomposition and augmented Lagrangian methods for constrained optimization. It turns out to be equivalent or closely related to many other algorithms as well, such as Douglas-Rachford splitting from numerical analysis, Spingarn’s method of partial inverses, Dykstra’s alternating projections method, Bregman iterative algorithms for l1 problems in signal processing, proximal methods, and many others.

Text Source: https://stanford.edu/~boyd/papers/pdf/admm_distr_stats.pdf

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Papers Using This Method

Federated ADMM from Bayesian Duality2025-06-16 Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming2025-06-02 Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction Networks for Single-Pixel Imaging2025-05-29 Linear Convergence of Plug-and-Play Algorithms with Kernel Denoisers2025-05-21 Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows2025-05-07 Enhancing Variable Selection in Large-scale Logistic Regression: Leveraging Manual Labeling with Beneficial Noise2025-04-23 Residual-Evasive Attacks on ADMM in Distributed Optimization2025-04-22 Plug and Play Distributed Control of Clustered Energy Hub Networks2025-04-08 Low-Complexity SDP-ADMM for Physical-Layer Multicasting in Massive MIMO Systems2025-04-08 Distributed Mixed-Integer Quadratic Programming for Mixed-Traffic Intersection Control2025-04-06 Sparsity-Promoting Reachability Analysis and Optimization of Constrained Zonotopes2025-04-04 Modular Distributed Nonconvex Learning with Error Feedback2025-03-18 Federated Smoothing ADMM for Localization2025-03-12 Efficient Distributed Learning over Decentralized Networks with Convoluted Support Vector Machine2025-03-10 An Adaptive Multiparameter Penalty Selection Method for Multiconstraint and Multiblock ADMM2025-02-28 Distributed Primal-Dual Algorithms: Unification, Connections, and Insights2025-02-01 Three-Dimensional Diffusion-Weighted Multi-Slab MRI With Slice Profile Compensation Using Deep Energy Model2025-01-28 Connecting Federated ADMM to Bayes2025-01-28 Asynchronous distributed collision avoidance with intention consensus for inland autonomous ships2025-01-27 Radio Map Estimation via Latent Domain Plug-and-Play Denoising2025-01-23