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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 181–190 of 536 papers

TitleStatusHype
Deep Reinforcement Learning for QoS-Constrained Resource Allocation in Multiservice Networks—0
Distributed Dynamic Safe Screening Algorithms for Sparse Regularization—0
Distributed Energy Trading Management for Renewable Prosumers with HVAC and Energy Storage—0
Distributed estimation of the inverse Hessian by determinantal averaging—0
Distributed Experiment Design and Control for Multi-agent Systems with Gaussian Processes—0
Distributed Fractional Bayesian Learning for Adaptive Optimization—0
Distributed gradient-based optimization in the presence of dependent aperiodic communication—0
Distributed gradient methods under heavy-tailed communication noise—0
Distributed Training of Graph Convolutional Networks—0
BALPA: A Balanced Primal-Dual Algorithm for Nonsmooth Optimization with Application to Distributed Optimization—0
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