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 431–440 of 536 papers

TitleStatusHype
Optimal Gradient Sliding and its Application to Distributed Optimization Under Similarity—0
Optimally Managing the Impacts of Convergence Tolerance for Distributed Optimal Power Flow—0
Optimal Methods for Convex Risk Averse Distributed Optimization—0
Optimization-Based Ramping Reserve Allocation of BESS for AGC Enhancement—0
Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents—0
Optimization in Open Networks via Dual Averaging—0
Parallel Feedforward Compensation for Output Synchronization: Fully Distributed Control and Indefinite Laplacian—0
Partitioning Data on Features or Samples in Communication-Efficient Distributed Optimization?—0
Peer-to-Peer Learning Dynamics of Wide Neural Networks—0
Pixel super-resolved lensless on-chip sensor with scattering multiplexing—0
Show:102550
← PrevPage 44 of 54Next →

No leaderboard results yet.