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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
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
Semantics, Representations and Grammars for Deep Learning—0
99% of Distributed Optimization is a Waste of Time: The Issue and How to Fix it—0
Accelerated consensus via Min-Sum Splitting—0
Accelerated Distributed Dual Averaging over Evolving Networks of Growing Connectivity—0
Accelerated Distributed Optimization with Compression and Error Feedback—0
Accelerated Methods with Compressed Communications for Distributed Optimization Problems under Data Similarity—0
Accelerated Sparsified SGD with Error Feedback—0
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