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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 221–230 of 536 papers

TitleStatusHype
Provably Doubly Accelerated Federated Learning: The First Theoretically Successful Combination of Local Training and Communication Compression—0
Distributed MPC for Self-Organized Cooperation of Multiagent Systems -- Extended Version—0
Hybrid Decentralized Optimization: Leveraging Both First- and Zeroth-Order Optimizers for Faster Convergence—0
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
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