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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
Deep Reinforcement Learning for QoS-Constrained Resource Allocation in Multiservice Networks—0
Decentralized gradient methods: does topology matter?—0
Improving Rate of Convergence via Gain Adaptation in Multi-Agent Distributed ADMM Framework—0
Multi-frequency calibration for DOA estimation with distributed sensors—0
Revisiting EXTRA for Smooth Distributed Optimization—0
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
Distributed Optimization over Block-Cyclic Data—0
Is Local SGD Better than Minibatch SGD?—0
Distributed Averaging Methods for Randomized Second Order Optimization—0
Differentially Quantized Gradient Methods—0
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
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local ComputationsCode0
Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents—0
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
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