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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 451–460 of 536 papers

TitleStatusHype
A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updates—0
Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization—0
A Distributed Second-Order Algorithm You Can Trust—0
Distributed learning with compressed gradients—0
Distributed Optimization Strategy for Multi Area Economic Dispatch Based on Electro Search Optimization Algorithm—0
Double Quantization for Communication-Efficient Distributed Optimization—0
Tie-Line Characteristics based Partitioning for Distributed Optimization of Power Systems—0
Scalable Centralized Deep Multi-Agent Reinforcement Learning via Policy Gradients—0
Communication-Efficient Projection-Free Algorithm for Distributed Optimization—0
Model Aggregation via Good-Enough Model Spaces—0
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