Alternating Direction Method of Multipliers

Also known as ADMM

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1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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SPADE-DFL: Communication-Efficient Decentralized Federated Learning via Derivative-Free Linearized ADMM
Sep 24, 2026Mengli Wei, Mengkai Zhu, Jiawen Chen +2Communication-Efficient Distributed Training Black-Box Optimization
School of Mathematics, Southeast University, Nanjing 210096, China
Learning-enabled Acceleration of Scenario-based Model Predictive Control
Jul 14, 2026Trinh Tran, Binh Nguyen, Truong X. NghiemAlternating Direction Method of Multipliers Model Predictive Control
Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USA
WarpMPC: Large-Batch MPC on GPU via ADMM with Unrolled $LDL^\top$ Factorization
Jul 13, 2026Henrik Hose, Se Hwan Jeon, Charles Khazoom +2GPU Kernel Optimization GPU Acceleration
Institute for Data Science in Mechanical Engineering (DSME), RWTH Aachen University, Germany · Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge MA, USA.
Second-Order KKT Guarantees for Bregman ADMM in Nonconvex and Non-Lipschitz Optimization
Jun 26, 2026Shuang Li, Zhihui Zhu, Qiuwei LiOptimization Convergence Analysis Distributed Optimization
Department of Electrical and Computer Engineering, Iowa State University, Ames, Iowa 50014, USA · Department of Computer Science and Engineering, Ohio State University, Columbus, Ohio 43210, USA · DAMO Academy, Alibaba Group US, Bellevue, Washington 98004, USA
Deep-Unfolded Coordination
Jun 18, 2026Hunter Kuperman, Minchan Jung, Rahul V. Ghosh +2Trajectory Optimization Multi-Agent System Optimization
Autonomous Control and Decision Systems Laboratory Georgia Institute of Technology United States
LEAF: A Learning-Enabled ADMM Framework for Accelerated Convex Optimization
Jun 8, 2026Binh Nguyen, Trinh Tran, Truong X. NghiemLearning to Optimize Alternating Direction Method of Multipliers
Department of Electrical and Computer Engineering, College of Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, USA
Physics-Aware Linearized ADMM and Its Unrolling
Jun 1, 2026Satoshi Takabe, Shunta Arai, Tadashi WadayamaAlternating Direction Method of Multipliers Algorithm Unrolling
Department of Computer Science Institute of Science Tokyo Tokyo, Japan · Center for Advanced Intelligence Project RIKEN Tokyo, Japan · Department of Engineering Nagoya Institute of Technology Nagoya, Japan
Learning Multi-Agent Coordination via Sheaf-ADMM
May 29, 2026Jeffrey Seely, Bartłomiej Cupiał, Llion JonesMulti-Agent System Optimization Multi-Agent Coordination
Sakana AI, Tokyo, Japan · University of Warsaw · AKCES NCBR
A Unified Framework for Structure-Aware Clustering and Heterogeneous Causal Graph Learning
May 19, 2026Honglin Du, Muxuan Liang, Xiang ZhongStructural Causal Models Clustering
Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611, USA · Department of Biostatistics, MD Anderson Cancer Center, Houston, TX 77030, USA
ADMM-Q: An Improved Hessian-based Weight Quantizer for Post-Training Quantization of Large Language Models
May 11, 2026Ryan Lucas, Mehdi Makni, Xiang Meng +2LLM Quantization Alternating Direction Method of Multipliers
MIT Operations Research Center · MIT Sloan School of Management · MIT Center for Statistics
Function-Space ADMM for Decentralized Federated Learning: A Control Theoretic Perspective
May 10, 2026Akihito Taya, Yuuki Nishiyama, Kaoru SezakiAlternating Direction Method of Multipliers Non-IID Federated Learning
Institute of Industrial Science, The University of Tokyo, Meguro-ku, Tokyo 153-8505, Japan · Center for Spatial Information Science, The University of Tokyo, Kashiwa-shi, Chiba 277-8568, Japan
FlowADMM: Plug-and-play ADMM with Flow-based Renoise-Denoise Priors
May 9, 2026Hendrik Sommerhoff, Michael MoellerAlternating Direction Method of Multipliers Image Restoration
Computer Vision Group, University of Siegen Hölderlinstraße 3, 57076 Siegen, Germany
Low Rank Tensor Completion via Adaptive ADMM
May 5, 2026Niclas Führling, Getuar Rexhepi, Giuseppe Thadeu Freitas de AbreuTensor Completion Nuclear Norm Minimization
School of Computer Science and Engineering, Constructor University, Bremen, Germany
Learning Over-Relaxation Policies for ADMM with Convergence Guarantees
Apr 29, 2026Junan Lin, Paul J. Goulart, Luca FurieriLearning to Optimize Alternating Direction Method of Multipliers
Department of Engineering Science, University of Oxford, OX1 3PJ Oxford, UK
ATRS: Adaptive Trajectory Re-splitting via a Shared Neural Policy for Parallel Optimization
Apr 24, 2026Jiajun Yu, Guodong Liu, Li Wang +6Trajectory Optimization Alternating Direction Method of Multipliers
HUANet: Hard-Constrained Unrolled ADMM for Constrained Convex Optimization
Apr 14, 2026Trinh Tran, Binh Nguyen, Truong X. NghiemLearning to Optimize Alternating Direction Method of Multipliers
Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USA
ADMM-based Continuous Trajectory Optimization in Graphs of Convex Sets
Mar 11, 2026Lukas Pries, Jon Arrizabalaga, Zachary Manchester +1Trajectory Optimization Alternating Direction Method of Multipliers
Autonomous Aerial Systems Lab, Dep. of Aerospace and Geodesy, TU Munich, Germany · Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, USA
Learning to Optimize by Differentiable Programming
Jan 23, 2026Liping Tao, Xindi Tong, Chee Wei TanPrimal-Dual Optimization Learning to Optimize
Nanyang Technological University, Singapore · Nanyang Technological University, Singapore, Singapore
Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming
Jun 2, 2025Haruki Yokota, Hiroshi Higashi, Yuichi Tanaka +1Graph Structure Learning Graph Representation Learning
On the Relationship Between CoCoA and ADMM for Distributed Empirical Risk Minimization
Feb 1, 2025Runxiong Wu, Andi WangPrimal-Dual Optimization Distributed Optimization
Department of Industrial & Systems Engineering University of Wisconsin–Madison

Sep 24, 2026, cs.LGJ/K move · Enter open · S save

SPADE-DFL: Communication-Efficient Decentralized Federated Learning via Derivative-Free Linearized ADMM

Mengli Wei, Mengkai Zhu, Jiawen Chen, Wenwu Yu+1

School of Mathematics, Southeast University, Nanjing 210096, China

Communication-Efficient Distributed Training Black-Box Optimization Alternating Direction Method of Multipliers Decentralized Learning Differentially Private Federated Learning Derivative-Free Optimization+1

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Reducing communication in derivative-free decentralized learning requires controlling the disagreement accumulated over multiple local updates. This paper develops SPADE-DFL, a primal--dual method that allows the number of local function-value updates between neighbor exchanges to grow with the computation budget while preserving the nonprivate convergence order. For smooth nonconvex objectives under uniform query-moment bounds, the prescribed nonprivate schedule achieves a time-averaged stationarity and consensus bound of

\mathcal{O}(T^{-1/3})

using only

Θ(T^{2/3})

communication rounds, where

T

is the number of local updates per client. For private training, the accumulated data-dependent increment is isolated from the graph correction, allowing one protected state per client and round to generate all outgoing messages. We prove client-level differential privacy for the full interactive transcript and quantify the resulting optimization error over a finite horizon. Experiments on four classification tasks show that SPADE-DFL achieves higher mean test accuracy than existing decentralized learning methods.

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