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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 351–400 of 536 papers

TitleStatusHype
Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents—0
Optimization in Open Networks via Dual Averaging—0
Parallel Feedforward Compensation for Output Synchronization: Fully Distributed Control and Indefinite Laplacian—0
Partitioning Data on Features or Samples in Communication-Efficient Distributed Optimization?—0
Peer-to-Peer Learning Dynamics of Wide Neural Networks—0
Pixel super-resolved lensless on-chip sensor with scattering multiplexing—0
PopSGD: Decentralized Stochastic Gradient Descent in the Population Model—0
Asynchronous Decentralized SGD with Quantized and Local Updates—0
Popt4jlib: A Parallel/Distributed Optimization Library for Java—0
SLSGD: Secure and Efficient Distributed On-device Machine Learning—0
Predict Globally, Correct Locally: Parallel-in-Time Optimal Control of Neural Networks—0
Prescribed-time Convergent Distributed Multiobjective Optimization with Dynamic Event-triggered Communication—0
Coordinated Day-ahead Dispatch of Multiple Power Distribution Grids hosting Stochastic Resources: An ADMM-based Framework—0
Privacy-Preserving Distributed Market Mechanism for Active Distribution Networks—0
Privacy-Preserving Distributed Optimization and Learning—0
Privacy-Preserving Peer-to-Peer Energy Trading via Hybrid Secure Computations—0
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
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
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