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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 511–520 of 536 papers

TitleStatusHype
Asynchronous Forward Bounding for Distributed COPs—0
Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees—0
Asynchronous Stochastic Optimization Robust to Arbitrary Delays—0
A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updates—0
AttentionX: Exploiting Consensus Discrepancy In Attention from A Distributed Optimization Perspective—0
Auction-based and Distributed Optimization Approaches for Scheduling Observations in Satellite Constellations with Exclusive Orbit Portions—0
Information-Geometric Barycenters for Bayesian Federated Learning—0
BALPA: A Balanced Primal-Dual Algorithm for Nonsmooth Optimization with Application to Distributed Optimization—0
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning—0
Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs—0
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