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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
Centralised and Distributed Optimization for Aggregated Flexibility Services Provision—0
Cell Zooming with Masked Data for Off-Grid Small Cell Networks: Distributed Optimization Approach—0
A Novel Decentralized Algorithm for Coordinating the Optimal Power and Traffic Flows with EVs based on Variable Inner Loop Selection—0
A Distributed ADMM-based Deep Learning Approach for Thermal Control in Multi-Zone Buildings under Demand Response Events—0
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