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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
Graph Learning Under Partial Observability—0
Is Local SGD Better than Minibatch SGD?—0
Iterative Pre-Conditioning to Expedite the Gradient-Descent Method—0
KKT Conditions, First-Order and Second-Order Optimization, and Distributed Optimization: Tutorial and Survey—0
Model Aggregation via Good-Enough Model Spaces—0
LAGO: Few-shot Crosslingual Embedding Inversion Attacks via Language Similarity-Aware Graph Optimization—0
LASER: Linear Compression in Wireless Distributed Optimization—0
Layer-wise Adaptive Gradient Sparsification for Distributed Deep Learning with Convergence Guarantees—0
Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models: Extension—0
Learning-Accelerated ADMM for Distributed Optimal Power Flow—0
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