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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 401–410 of 536 papers

TitleStatusHype
Accelerated Primal-Dual Algorithms for Distributed Smooth Convex Optimization over NetworksCode0
Sparsification as a Remedy for Staleness in Distributed Asynchronous SGD—0
SlowMo: Improving Communication-Efficient Distributed SGD with Slow MomentumCode0
PopSGD: Decentralized Stochastic Gradient Descent in the Population Model—0
Gradient-Consensus: Linearly Convergent Distributed Optimization Algorithm over Directed Graphs—0
Convex Set Disjointness, Distributed Learning of Halfspaces, and LP Feasibility—0
Proximal gradient flow and Douglas-Rachford splitting dynamics: global exponential stability via integral quadratic constraints—0
Federated Learning: Challenges, Methods, and Future DirectionsCode0
Gradient flows and proximal splitting methods: A unified view on accelerated and stochastic optimization—0
Popt4jlib: A Parallel/Distributed Optimization Library for Java—0
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