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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 191–200 of 536 papers

TitleStatusHype
BALPA: A Balanced Primal-Dual Algorithm for Nonsmooth Optimization with Application to Distributed Optimization—0
Continual Learning with Distributed Optimization: Does CoCoA Forget?—0
Simple and Scalable Algorithms for Cluster-Aware Precision Medicine—0
Distributed Optimization with Quantized Gradient Descent—0
Impact of Redundancy on Resilience in Distributed Optimization and Learning—0
Fast Adaptive Federated Bilevel Optimization—0
GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational ComplexityCode0
Provably Doubly Accelerated Federated Learning: The First Theoretically Successful Combination of Local Training and Communication Compression—0
Distributed MPC for Self-Organized Cooperation of Multiagent Systems -- Extended Version—0
Hybrid Decentralized Optimization: Leveraging Both First- and Zeroth-Order Optimizers for Faster Convergence—0
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