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 331–340 of 536 papers

TitleStatusHype
Design of heterogeneous multi-agent system for distributed computation—0
Convergent Adaptive Gradient Methods in Decentralized Optimization—0
Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning—0
Byzantine-Resilient Non-Convex Stochastic Gradient Descent—0
Wyner-Ziv Estimators for Distributed Mean Estimation with Side Information and OptimizationCode0
Linear Convergence of Distributed Mirror Descent with Integral Feedback for Strongly Convex Problems—0
Sparse sketches with small inversion bias—0
Distributed Stochastic Consensus Optimization with Momentum for Nonconvex Nonsmooth Problems—0
Widely-distributed Radar Imaging Based on Consensus ADMM—0
Distributed Saddle-Point Problems: Lower Bounds, Near-Optimal and Robust Algorithms—0
Show:102550
← PrevPage 34 of 54Next →

No leaderboard results yet.