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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 121–130 of 536 papers

TitleStatusHype
Consensus optimization approach for distributed Kalman filtering: performance recovery of centralized filtering with proofs—0
Continual Learning with Distributed Optimization: Does CoCoA Forget?—0
Convergence rate of sign stochastic gradient descent for non-convex functions—0
Convergence Rates of Two-Time-Scale Gradient Descent-Ascent Dynamics for Solving Nonconvex Min-Max Problems—0
Convergence Theory of Flexible ALADIN for Distributed Optimization—0
Convergence Theory of Generalized Distributed Subgradient Method with Random Quantization—0
Auction-based and Distributed Optimization Approaches for Scheduling Observations in Satellite Constellations with Exclusive Orbit Portions—0
AttentionX: Exploiting Consensus Discrepancy In Attention from A Distributed Optimization Perspective—0
ALADIN-β: A Distributed Optimization Algorithm for Solving MPCC Problems—0
A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updates—0
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