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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 526–536 of 536 papers

TitleStatusHype
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
Asynchronous Adaptation and Learning over Networks --- Part I: Modeling and Stability Analysis—0
Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent—0
Communication/Computation Tradeoffs in Consensus-Based Distributed Optimization—0
Diffusion Adaptation over Networks—0
Distributed Delayed Stochastic Optimization—0
Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling—0
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