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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 121–130 of 536 papers

TitleStatusHype
Generalized Gradient Descent is a Hypergraph Functor—0
Distributed Maximum Consensus over Noisy Links—0
Network-Aware Value Stacking of Community Battery via Asynchronous Distributed Optimization—0
Quantization Avoids Saddle Points in Distributed Optimization—0
Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction—0
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression—0
MUSIC: Accelerated Convergence for Distributed Optimization With Inexact and Exact Methods—0
Privacy-Preserving Distributed Optimization and Learning—0
Parallel Momentum Methods Under Biased Gradient Estimations—0
FairSync: Ensuring Amortized Group Exposure in Distributed Recommendation RetrievalCode0
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