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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 521–530 of 536 papers

TitleStatusHype
A Hierarchical Approach for Joint Multi-view Object Pose Estimation and Categorization—0
Adding vs. Averaging in Distributed Primal-Dual OptimizationCode0
Communication-Efficient Distributed Optimization of Self-Concordant Empirical Loss—0
Online Distributed Optimization on Dynamic Networks—0
Communication-Efficient Distributed Dual Coordinate Ascent—0
High-performance Kernel Machines with Implicit Distributed Optimization and Randomization—0
ROML: A Robust Feature Correspondence Approach for Matching Objects in A Set of Images—0
Asynchronous Forward Bounding for Distributed COPs—0
Communication Efficient Distributed Optimization using an Approximate Newton-type MethodCode0
Asynchronous Adaptation and Learning over Networks - Part II: Performance Analysis—0
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