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 461–470 of 536 papers

TitleStatusHype
Accelerating Distributed Optimization: A Primal-Dual Perspective on Local Steps—0
Accelerating variational quantum algorithms with multiple quantum processors—0
Acceleration for Compressed Gradient Descent in Distributed Optimization—0
Acceleration in Distributed Optimization under Similarity—0
Differentially Quantized Gradient Methods—0
A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting—0
A continuous-time analysis of distributed stochastic gradient—0
Adaptive Consensus ADMM for Distributed Optimization—0
Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets—0
Debiased distributed learning for sparse partial linear models in high dimensions—0
Show:102550
← PrevPage 47 of 54Next →

No leaderboard results yet.