Optimization Algorithms, Distributed Computing and Intelligence
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".
Deadline for manuscript submissions: 10 July 2025 | Viewed by 367
Special Issue Editor
Special Issue Information
Dear Colleagues,
The study of optimization algorithms such as gradient descent has a long history. Nowadays, they are widely applied in machine learning, especially in deep learning, where their analysis encounters new challenges. Theoretically, new research topics include understanding the optimization algorithms’ performance on non-linear models such as deep neural networks and the implicit bias of different optimizers, related to generalization and intelligence. In practice, issues facing deep learning, especially large models, that are worth studying include how to scale hyperparameters efficiently and design robust model optimizers.
We seek papers on insightful approaches to analyzing optimization algorithms, distributed computing intelligence in deep learning and large language models, and any other novel method for advancing these models.
Dr. Qi Meng
Guest Editor
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Keywords
- non-convex optimization
- optimization algorithms in deep learning
- implicit bias
- large-scale optimization
- stochastic methods in optimization
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