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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 471–480 of 536 papers

TitleStatusHype
CSWA: Aggregation-Free Spatial-Temporal Community Sensing—0
An Accelerated Communication-Efficient Primal-Dual Optimization Framework for Structured Machine LearningCode0
Straggler Mitigation in Distributed Optimization Through Data Encoding—0
Distributed Unmixing of Hyperspectral Data With Sparsity Constraint—0
Zeroth Order Nonconvex Multi-Agent Optimization over Networks—0
Gradient Sparsification for Communication-Efficient Distributed Optimization—0
A Sequential Approximation Framework for Coded Distributed Optimization—0
DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization—0
Distributed Very Large Scale Bundle Adjustment by Global Camera Consensus—0
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization—0
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