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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 161–170 of 536 papers

TitleStatusHype
Correlation Aware Sparsified Mean Estimation Using Random Projection—0
Differentially-Private Distributed Model Predictive Control of Linear Discrete-Time Systems with Global Constraints—0
Asynchronous Forward Bounding for Distributed COPs—0
DiLoCo: Distributed Low-Communication Training of Language Models—0
Discretized Distributed Optimization over Dynamic Digraphs—0
Distributed Continuous-Time Optimization with Uncertain Time-Varying Quadratic Cost Functions—0
Correlated Quantization for Faster Nonconvex Distributed Optimization—0
Distributed Algorithms for Feature Extraction Off-loading in Multi-Camera Visual Sensor Networks—0
Distributed and time-varying primal-dual dynamics via contraction analysis—0
Correlated quantization for distributed mean estimation and optimization—0
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