Decentralized Optimization with Cross-Coupled Mixed Affine Constraints

arXiv:2609.33021 · math.OC, cs.LG · Submitted 2026-09-26 · Read on arXiv

math.OC, cs.LG

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• Accuracy — 33× in this paper · explained in 1 episode
• Trace — 8× in this paper · explained in 2 episodes
• Core — 6× in this paper · explained in 2 episodes
• Robustness — 6× in this paper · explained in 5 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Federated Learning — 5× in this paper · explained in 15 episodes
• Generative AI — 4× in this paper · explained in 2 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• International Conference on Machine Learning — 3× in this paper
• Aggregation — 2× in this paper · explained in 1 episode
• Calibration — 2× in this paper · explained in 7 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Admissibility — 1× in this paper · explained in 2 episodes
• Chebyshev Polynomials — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Hilbert Space — 1× in this paper · explained in 2 episodes
• Minimax optimality — 1× in this paper · explained in 2 episodes
• Spectral Gap — 1× in this paper · explained in 5 episodes

Sources

• A Nesterov-Accelerated Primal-Dual Splitting Algorithm for Convex Nonsmooth Optimization
• Decentralized Proximal Method of Multipliers for Convex Optimization with Coupled Constraints
• Federated Learning of a Mixture of Global and Local Models
• Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
• Think Locally, Act Globally: Federated Learning with Local and Global Representations
• Sliding Methods for H\"older-Smooth Convex--Concave Minimax Optimization with Bilinear Coupling
• Optimality in Decentralized Optimization under Bandwidth Constraints
• BlueFog: Make Decentralized Algorithms Practical for Optimization and Deep Learning
• Solving Convex Smooth Function Constrained Optimization Is Almost As Easy As Unconstrained Optimization

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