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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 451–475 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
GoSGD: Distributed Optimization for Deep Learning with Gossip Exchange—0
Fundamental Resource Trade-offs for Encoded Distributed Optimization—0
A Stochastic Large-scale Machine Learning Algorithm for Distributed Features and Observations—0
SUCAG: Stochastic Unbiased Curvature-aided Gradient Method for Distributed Optimization—0
Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning—0
A Distributed Quasi-Newton Algorithm for Empirical Risk Minimization with Nonsmooth RegularizationCode0
Convergence rate of sign stochastic gradient descent for non-convex functions—0
ZOOpt: Toolbox for Derivative-Free OptimizationCode0
Optimal Algorithms for Distributed Optimization—0
Accelerated consensus via Min-Sum Splitting—0
CSWA: Aggregation-Free Spatial-Temporal Community Sensing—0
Straggler Mitigation in Distributed Optimization Through Data Encoding—0
An Accelerated Communication-Efficient Primal-Dual Optimization Framework for Structured Machine LearningCode0
Distributed Unmixing of Hyperspectral Data With Sparsity Constraint—0
Zeroth Order Nonconvex Multi-Agent Optimization over Networks—0
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