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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 321–330 of 536 papers

TitleStatusHype
Mean Field MARL Based Bandwidth Negotiation Method for Massive Devices Spectrum Sharing—0
A Proximal Gradient Method With Probabilistic Multi-Gossip Communications for Decentralized Composite Optimization—0
Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications to Parallel Machine Learning and Multi-Label Image Segmentation—0
Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications—0
Moreau Envelope ADMM for Decentralized Weakly Convex Optimization—0
Multi-Agent Reinforcement Learning with Graph Convolutional Neural Networks for optimal Bidding Strategies of Generation Units in Electricity Markets—0
Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point—0
Multi-frequency calibration for DOA estimation with distributed sensors—0
Multi-Message Shuffled Privacy in Federated Learning—0
Multi-objective Distributed Optimization for Zonal Distribution System with Multi-Microgrids—0
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