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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
Optimally Managing the Impacts of Convergence Tolerance for Distributed Optimal Power Flow—0
Discretized Distributed Optimization over Dynamic Digraphs—0
Asynchronous Message-Passing and Zeroth-Order Optimization Based Distributed Learning with a Use-Case in Resource Allocation in Communication Networks—0
EControl: Fast Distributed Optimization with Compression and Error Control—0
Goal-Oriented Wireless Communication Resource Allocation for Cyber-Physical Systems—0
Zeroth-Order Feedback-Based Optimization for Distributed Demand Response—0
Correlation Aware Sparsified Mean Estimation Using Random Projection—0
Distributed Delay-Tolerant Strategies for Equality-Constraint Sum-Preserving Resource Allocation—0
Machine Learning Infused Distributed Optimization for Coordinating Virtual Power Plant Assets—0
Distributed Linear Regression with Compositional Covariates—0
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