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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 421–430 of 536 papers

TitleStatusHype
Structured Reinforcement Learning for Incentivized Stochastic Covert Optimization—0
SUCAG: Stochastic Unbiased Curvature-aided Gradient Method for Distributed Optimization—0
Supervised MPC control of large-scale electricity networks via clustering methods—0
Survey of Distributed Algorithms for Resource Allocation over Multi-Agent Systems—0
TAMUNA: Doubly Accelerated Distributed Optimization with Local Training, Compression, and Partial Participation—0
TernaryVote: Differentially Private, Communication Efficient, and Byzantine Resilient Distributed Optimization on Heterogeneous Data—0
The Communication Complexity of Optimization—0
The Geometry of Sign Gradient Descent—0
The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication—0
The Minimax Complexity of Distributed Optimization—0
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