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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

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Showing 41–50 of 536 papers

TitleStatusHype
Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs—0
LAGO: Few-shot Crosslingual Embedding Inversion Attacks via Language Similarity-Aware Graph Optimization—0
Distributed Optimization with Efficient Communication, Event-Triggered Solution Enhancement, and Operation Stopping—0
Hessian Riemannian Flow For Multi-Population Wardrop Equilibrium—0
Residual-Evasive Attacks on ADMM in Distributed Optimization—0
Distributed model predictive control without terminal cost under inexact distributed optimization—0
Distributed Optimization with Gradient Tracking over Heterogeneous Delay-Prone Directed Networks—0
Graph Neural Network-Based Distributed Optimal Control for Linear Networked Systems: An Online Distributed Training Approach—0
Reducing the Communication of Distributed Model Predictive Control: Autoencoders and Formation Control—0
Dynamic Incentive Strategies for Smart EV Charging Stations: An LLM-Driven User Digital Twin Approach—0
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