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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 111–120 of 536 papers

TitleStatusHype
Algorithm Unrolling-Based Distributed Optimization for RIS-Assisted Cell-Free Networks—0
Communication-Efficient Distributed Kalman Filtering using ADMM—0
A Sequential Approximation Framework for Coded Distributed Optimization—0
Communication-Efficient Distributed SGD with Compressed Sensing—0
Communication Efficient Federated Learning via Ordered ADMM in a Fully Decentralized Setting—0
Communication Efficient Federated Learning with Linear Convergence on Heterogeneous Data—0
Communication-Efficient Projection-Free Algorithm for Distributed Optimization—0
Communication-efficient Variance-reduced Stochastic Gradient Descent—0
Adaptive Consensus ADMM for Distributed Optimization—0
Auction-based and Distributed Optimization Approaches for Scheduling Observations in Satellite Constellations with Exclusive Orbit Portions—0
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