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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 201–210 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
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