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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 351–360 of 536 papers

TitleStatusHype
Optimal Methods for Convex Risk Averse Distributed Optimization—0
Optimization-Based Ramping Reserve Allocation of BESS for AGC Enhancement—0
Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents—0
Optimization in Open Networks via Dual Averaging—0
Parallel Feedforward Compensation for Output Synchronization: Fully Distributed Control and Indefinite Laplacian—0
Partitioning Data on Features or Samples in Communication-Efficient Distributed Optimization?—0
Peer-to-Peer Learning Dynamics of Wide Neural Networks—0
Pixel super-resolved lensless on-chip sensor with scattering multiplexing—0
PopSGD: Decentralized Stochastic Gradient Descent in the Population Model—0
Asynchronous Decentralized SGD with Quantized and Local Updates—0
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