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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 151–160 of 536 papers

TitleStatusHype
DEED: A General Quantization Scheme for Communication Efficiency in Bits—0
Deep Distributed Optimization for Large-Scale Quadratic Programming—0
Asynchronous Forward Bounding for Distributed COPs—0
Correlated Quantization for Faster Nonconvex Distributed Optimization—0
Deep Reinforcement Learning for QoS-Constrained Resource Allocation in Multiservice Networks—0
Delayed Projection Techniques for Linearly Constrained Problems: Convergence Rates, Acceleration, and Applications—0
Design of heterogeneous multi-agent system for distributed computation—0
Detecting Shared Data Manipulation in Distributed Optimization Algorithms—0
Differentially Private Consensus-Based Distributed Optimization—0
Correlated quantization for distributed mean estimation and optimization—0
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