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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 271–280 of 536 papers

TitleStatusHype
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
Graphon Particle Systems, Part II: Dynamics of Distributed Stochastic Continuum Optimization—0
Hemingway: Modeling Distributed Optimization Algorithms—0
Hessian Riemannian Flow For Multi-Population Wardrop Equilibrium—0
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