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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 376–400 of 536 papers

TitleStatusHype
New Bounds For Distributed Mean Estimation and Variance Reduction—0
Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets—0
The Geometry of Sign Gradient Descent—0
Is Local SGD Better than Minibatch SGD?—0
Distributed Optimization over Block-Cyclic Data—0
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
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local ComputationsCode0
Layer-wise Adaptive Gradient Sparsification for Distributed Deep Learning with Convergence Guarantees—0
vqSGD: Vector Quantized Stochastic Gradient Descent—0
Learning-Accelerated ADMM for Distributed Optimal Power Flow—0
On the Convergence of Local Descent Methods in Federated Learning—0
Local SGD with Periodic Averaging: Tighter Analysis and Adaptive SynchronizationCode0
Asynchronous Decentralized SGD with Quantized and Local Updates—0
Accelerated Primal-Dual Algorithms for Distributed Smooth Convex Optimization over NetworksCode0
Sparsification as a Remedy for Staleness in Distributed Asynchronous SGD—0
SCAFFOLD: Stochastic Controlled Averaging for Federated LearningCode1
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