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Distributed Optimization

The goal of Distributed Optimization is to optimize a certain objective defined over millions of billions of data that is distributed over many machines by utilizing the computational power of these machines.

Source: Analysis of Distributed StochasticDual Coordinate Ascent

Papers

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Showing 451–500 of 536 papers

TitleStatusHype
Privacy-Preserving Push-Pull Method for Decentralized Optimization via State Decomposition—0
Private Learning on Networks—0
Private Learning on Networks: Part II—0
Problem-dependent convergence bounds for randomized linear gradient compression—0
Projected Push-Sum Gradient Descent-Ascent for Convex Optimizationwith Application to Economic Dispatch Problems—0
Provable Privacy Advantages of Decentralized Federated Learning via Distributed Optimization—0
Provably Doubly Accelerated Federated Learning: The First Theoretically Successful Combination of Local Training and Communication Compression—0
Proximal gradient flow and Douglas-Rachford splitting dynamics: global exponential stability via integral quadratic constraints—0
Q-SHED: Distributed Optimization at the Edge via Hessian Eigenvectors Quantization—0
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computations—0
Quantization Avoids Saddle Points in Distributed Optimization—0
Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free—0
Achieving Linear Speedup with ProxSkip in Distributed Stochastic Optimization—0
Rate Analysis of Coupled Distributed Stochastic Approximation for Misspecified Optimization—0
Real-Time Distributed Model Predictive Control with Limited Communication Data Rates—0
Recurrent Averaging Inequalities in Multi-Agent Control and Social Dynamics Modeling—0
Reducing the Communication of Distributed Model Predictive Control: Autoencoders and Formation Control—0
Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning—0
Graph neural networks-based Scheduler for Production planning problems using Reinforcement Learning—0
Residual-Evasive Attacks on ADMM in Distributed Optimization—0
Review of Mathematical Optimization in Federated Learning—0
Revisiting EXTRA for Smooth Distributed Optimization—0
Robust Distributed Optimization With Randomly Corrupted Gradients—0
Robust Optimization, Structure/Control co-design, Distributed Optimization, Monolithic Optimization, Robust Control, Parametric Uncertainty—0
ROML: A Robust Feature Correspondence Approach for Matching Objects in A Set of Images—0
Scalable Centralized Deep Multi-Agent Reinforcement Learning via Policy Gradients—0
Seamless Integration: Sampling Strategies in Federated Learning Systems—0
Secure Architectures Implementing Trusted Coalitions for Blockchained Distributed Learning (TCLearn)—0
Semantics, Representations and Grammars for Deep Learning—0
Short vs. Long-term Coordination of Drones: When Distributed Optimization Meets Deep Reinforcement Learning—0
Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under (L_0, L_1)-Smoothness—0
Simulation-Integrated Distributed Optimal Power Flow for Unbalanced Power Distribution Systems—0
Simultaneous Contact-Rich Grasping and Locomotion via Distributed Optimization Enabling Free-Climbing for Multi-Limbed Robots—0
Single Point-Based Distributed Zeroth-Order Optimization with a Non-Convex Stochastic Objective Function—0
Smoothed Normalization for Efficient Distributed Private Optimization—0
Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques—0
Distributed Optimization using Heterogeneous Compute SystemsCode0
Private Multi-Task Learning: Formulation and Applications to Federated LearningCode0
GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational ComplexityCode0
Sparsified SGD with MemoryCode0
Distributed Optimization with Arbitrary Local SolversCode0
Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of MultipliersCode0
Efficient Randomized Subspace Embeddings for Distributed Optimization under a Communication BudgetCode0
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural NetworksCode0
Distributed Adversarial Training to Robustify Deep Neural Networks at ScaleCode0
Adding vs. Averaging in Distributed Primal-Dual OptimizationCode0
ZOOpt: Toolbox for Derivative-Free OptimizationCode0
Accelerated Primal-Dual Algorithms for Distributed Smooth Convex Optimization over NetworksCode0
Shuffle-QUDIO: accelerate distributed VQE with trainability enhancement and measurement reductionCode0
Differentially Private Distributed Estimation and LearningCode0
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