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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 501–510 of 536 papers

TitleStatusHype
Distributed optimization for nonrigid nano-tomographyCode0
Accelerating Exact and Approximate Inference for (Distributed) Discrete Optimization with GPUsCode0
Local SGD with Periodic Averaging: Tighter Analysis and Adaptive SynchronizationCode0
A primal-dual perspective for distributed TD-learningCode0
PowerSGD: Practical Low-Rank Gradient Compression for Distributed OptimizationCode0
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated LearningCode0
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local ComputationsCode0
On the Convergence of Decentralized Federated Learning Under Imperfect Information SharingCode0
Cooperative Tuning of Multi-Agent Optimal Control SystemsCode0
Federated Learning: Challenges, Methods, and Future DirectionsCode0
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