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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 201–225 of 536 papers

TitleStatusHype
Shuffle-QUDIO: accelerate distributed VQE with trainability enhancement and measurement reductionCode0
Cooperative Tuning of Multi-Agent Optimal Control SystemsCode0
Distributed CPU Scheduling Subject to Nonlinear Constraints—0
Real-Time Distributed Model Predictive Control with Limited Communication Data Rates—0
Decentralized Optimization with Distributed Features and Non-Smooth Objective Functions—0
Consensus optimization approach for distributed Kalman filtering: performance recovery of centralized filtering with proofs—0
Multi-Agent Reinforcement Learning with Graph Convolutional Neural Networks for optimal Bidding Strategies of Generation Units in Electricity Markets—0
Coordinating Flexible Ramping Products with Dynamics of the Natural Gas Network—0
Convergence Theory of Generalized Distributed Subgradient Method with Random Quantization—0
Online Computation of Terminal Ingredients in Distributed Model Predictive Control for Reference Tracking—0
Distributed Learning of Neural Lyapunov Functions for Large-Scale Networked Dissipative Systems—0
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated LearningCode0
Can Competition Outperform Collaboration? The Role of Misbehaving Agents—0
Simultaneous Contact-Rich Grasping and Locomotion via Distributed Optimization Enabling Free-Climbing for Multi-Limbed Robots—0
On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network—0
Distributed Adversarial Training to Robustify Deep Neural Networks at ScaleCode0
Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression—0
Beyond spectral gap: The role of the topology in decentralized learningCode1
A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting—0
Optimal Gradient Sliding and its Application to Distributed Optimization Under Similarity—0
Byzantine Machine Learning Made Easy by Resilient Averaging of Momentums—0
Distributed Optimization in Distribution Systems with Grid-Forming and Grid-Supporting Inverters—0
On Distributed Adaptive Optimization with Gradient Compression—0
EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed OptimizationCode0
Understanding A Class of Decentralized and Federated Optimization Algorithms: A Multi-Rate Feedback Control Perspective—0
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