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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 61–70 of 536 papers

TitleStatusHype
Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEs—0
Smoothed Normalization for Efficient Distributed Private Optimization—0
Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newton—0
Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under (L_0, L_1)-Smoothness—0
Efficient Distributed Optimization under Heavy-Tailed Noise—0
A Survey of Optimization Methods for Training DL Models: Theoretical Perspective on Convergence and Generalization—0
Communication-Efficient Distributed Kalman Filtering using ADMM—0
Distributed Model Predictive Control Design for Multi-agent Systems via Bayesian Optimization—0
Distributed Convex Optimization with State-Dependent (Social) Interactions over Random Networks—0
Accelerated Methods with Compressed Communications for Distributed Optimization Problems under Data Similarity—0
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