SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 441–450 of 536 papers

TitleStatusHype
PopSGD: Decentralized Stochastic Gradient Descent in the Population Model—0
Asynchronous Decentralized SGD with Quantized and Local Updates—0
Popt4jlib: A Parallel/Distributed Optimization Library for Java—0
SLSGD: Secure and Efficient Distributed On-device Machine Learning—0
Predict Globally, Correct Locally: Parallel-in-Time Optimal Control of Neural Networks—0
Prescribed-time Convergent Distributed Multiobjective Optimization with Dynamic Event-triggered Communication—0
Coordinated Day-ahead Dispatch of Multiple Power Distribution Grids hosting Stochastic Resources: An ADMM-based Framework—0
Privacy-Preserving Distributed Market Mechanism for Active Distribution Networks—0
Privacy-Preserving Distributed Optimization and Learning—0
Privacy-Preserving Peer-to-Peer Energy Trading via Hybrid Secure Computations—0
Show:102550
← PrevPage 45 of 54Next →

No leaderboard results yet.