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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 271–280 of 536 papers

TitleStatusHype
Variance Reduction in Deep Learning: More Momentum is All You Need—0
vqSGD: Vector Quantized Stochastic Gradient Descent—0
When Evolutionary Computation Meets Privacy—0
Widely-distributed Radar Imaging Based on Consensus ADMM—0
Without-Replacement Sampling for Stochastic Gradient Methods: Convergence Results and Application to Distributed Optimization—0
Without-Replacement Sampling for Stochastic Gradient Methods—0
Asynchronous Message-Passing and Zeroth-Order Optimization Based Distributed Learning with a Use-Case in Resource Allocation in Communication Networks—0
Zeroth-Order Feedback-Based Optimization for Distributed Demand Response—0
Zeroth Order Nonconvex Multi-Agent Optimization over Networks—0
Distributed Optimization via Energy Conservation Laws in Dilated Coordinates—0
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