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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 426–450 of 536 papers

TitleStatusHype
On Maintaining Linear Convergence of Distributed Learning and Optimization under Limited Communication—0
On the Convergence of Decentralized Adaptive Gradient Methods—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
Optimal Methods for Convex Risk Averse Distributed Optimization—0
Optimization-Based Ramping Reserve Allocation of BESS for AGC Enhancement—0
Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents—0
Optimization in Open Networks via Dual Averaging—0
Parallel Feedforward Compensation for Output Synchronization: Fully Distributed Control and Indefinite Laplacian—0
Partitioning Data on Features or Samples in Communication-Efficient Distributed Optimization?—0
Peer-to-Peer Learning Dynamics of Wide Neural Networks—0
Pixel super-resolved lensless on-chip sensor with scattering multiplexing—0
PopSGD: Decentralized Stochastic Gradient Descent in the Population Model—0
Asynchronous Decentralized SGD with Quantized and Local Updates—0
Popt4jlib: A Parallel/Distributed Optimization Library for Java—0
SLSGD: Secure and Efficient Distributed On-device Machine Learning—0
Predict Globally, Correct Locally: Parallel-in-Time Optimal Control of Neural Networks—0
Prescribed-time Convergent Distributed Multiobjective Optimization with Dynamic Event-triggered Communication—0
Coordinated Day-ahead Dispatch of Multiple Power Distribution Grids hosting Stochastic Resources: An ADMM-based Framework—0
Privacy-Preserving Distributed Market Mechanism for Active Distribution Networks—0
Privacy-Preserving Distributed Optimization and Learning—0
Privacy-Preserving Peer-to-Peer Energy Trading via Hybrid Secure Computations—0
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