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 351–360 of 536 papers

TitleStatusHype
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization—0
Goal-Oriented Wireless Communication Resource Allocation for Cyber-Physical Systems—0
GoSGD: Distributed Optimization for Deep Learning with Gossip Exchange—0
Gradient-Consensus: Linearly Convergent Distributed Optimization Algorithm over Directed Graphs—0
Gradient flows and proximal splitting methods: A unified view on accelerated and stochastic optimization—0
Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks—0
Gradient Sparsification for Communication-Efficient Distributed Optimization—0
Gradient-Tracking over Directed Graphs for solving Leaderless Multi-Cluster Games—0
Graph Neural Network-Based Distributed Optimal Control for Linear Networked Systems: An Online Distributed Training Approach—0
Graph Neural Networks Gone Hogwild—0
Show:102550
← PrevPage 36 of 54Next →

No leaderboard results yet.