Scaling up stochastic gradient descent for non-convex optimisation

Stochastic gradient descent (SGD) is a widely adopted iterative method for optimizing differentiable objective functions. In this paper, we propose and discuss a novel approach to scale up SGD in applications involving non-convex functions and large datasets. We address the bottleneck problem arisin...

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Veröffentlicht in:	Machine learning Jg. 111; H. 11; S. 4039 - 4079
Hauptverfasser:	Mohamad, Saad, Alamri, Hamad, Bouchachia, Abdelhamid
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.11.2022 Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Comparative studies Computation Computer Science Control Convergence Deep learning Dirichlet problem Distributed memory Empirical analysis Iterative methods Machine Learning Mechatronics Natural Language Processing (NLP) Optimization Robotics Scaling up Simulation and Modeling Special Issue: Foundations of Data Science Statistical models Deep reinforcement learning Distributed and parallel computation Stochastic gradient descent Variational inference Large scale non-convex optimisation
ISSN:	0885-6125, 1573-0565
Online-Zugang:	Volltext
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