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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 291–300 of 536 papers

TitleStatusHype
Asynchronous Stochastic Optimization Robust to Arbitrary Delays—0
Secure Distributed Training at ScaleCode1
DeepLM: Large-Scale Nonlinear Least Squares on Deep Learning Frameworks Using Stochastic Domain DecompositionCode1
Learning Autonomy in Management of Wireless Random Networks—0
Decentralized Personalized Federated Learning for Min-Max Problems—0
Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques—0
Auction-based and Distributed Optimization Approaches for Scheduling Observations in Satellite Constellations with Exclusive Orbit Portions—0
Assessing the Impacts of Nonideal Communications on Distributed Optimal Power Flow Algorithms—0
Pixel super-resolved lensless on-chip sensor with scattering multiplexing—0
Optimization in Open Networks via Dual Averaging—0
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