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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 281–290 of 536 papers

TitleStatusHype
Simple and Scalable Algorithms for Cluster-Aware Precision Medicine—0
High-performance Kernel Machines with Implicit Distributed Optimization and Randomization—0
High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise—0
Hybrid Decentralized Optimization: Leveraging Both First- and Zeroth-Order Optimizers for Faster Convergence—0
Hyperspectral Unmixing Based on Clustered Multitask Networks—0
Impact of Redundancy on Resilience in Distributed Optimization and Learning—0
Improving Rate of Convergence via Gain Adaptation in Multi-Agent Distributed ADMM Framework—0
Improving the Transient Times for Distributed Stochastic Gradient Methods—0
Improving the Worst-Case Bidirectional Communication Complexity for Nonconvex Distributed Optimization under Function Similarity—0
Innovation Compression for Communication-efficient Distributed Optimization with Linear Convergence—0
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