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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 341–350 of 536 papers

TitleStatusHype
Online distributed optimization for spatio-temporally constrained real-time peer-to-peer energy trading—0
Online Distributed Optimization on Dynamic Networks—0
On Maintaining Linear Convergence of Distributed Learning and Optimization under Limited Communication—0
On the Convergence of Decentralized Adaptive Gradient Methods—0
On the Convergence of Local Descent Methods in Federated Learning—0
On the Finite-Time Behavior of Suboptimal Linear Model Predictive Control—0
Optimal Algorithms for Distributed Optimization—0
Optimal Data Splitting in Distributed Optimization for Machine Learning—0
Optimal Gradient Sliding and its Application to Distributed Optimization Under Similarity—0
Optimally Managing the Impacts of Convergence Tolerance for Distributed Optimal Power Flow—0
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