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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 381–390 of 536 papers

TitleStatusHype
Distributed Averaging Methods for Randomized Second Order Optimization—0
Training Large Neural Networks with Constant Memory using a New Execution AlgorithmCode1
Differentially Quantized Gradient Methods—0
FedDANE: A Federated Newton-Type MethodCode1
Estimating the Error of Randomized Newton Methods: A Bootstrap Approach—0
Acceleration for Compressed Gradient Descent in Distributed Optimization—0
Manifold Identification for Ultimately Communication-Efficient Distributed OptimizationCode0
Graph Learning Under Partial Observability—0
A Distributed Quasi-Newton Algorithm for Primal and Dual Regularized Empirical Risk MinimizationCode0
Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents—0
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