Generalized Gradients in Dynamic Optimization, Optimal Control, and Machine Learning Problems*-Reference-Cited by-同舟云学术

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Generalized Gradients in Dynamic Optimization, Optimal Control, and Machine Learning Problems*

Published:2020-03 Issue:2 Volume:56 Page:243-258
ISSN:1060-0396
Container-title:Cybernetics and Systems Analysis
language:en
Short-container-title:Cybern Syst Anal

Author:

Norkin V. I.

Publisher

Springer Science and Business Media LLC

Subject

General Computer Science

Link

http://link.springer.com/content/pdf/10.1007/s10559-020-00240-x.pdf

Reference59 articles.

1. A. E. Bryson and Ju-Shi Ho, Applied Optimal Control. Optimization, Estimation, and Control, CRC Press (2017).

2. Yu. M. Ermoliev, Methods of Stochastic Programming [in Russian], Nauka Moscow (1976).

3. Yu. M. Ermoliev, V. P. Gulenko and T. I. Tsarenko, Finite-Difference Method in Optimal Control Problems [in Russian], Naukova Dumka, Kyiv (1978).

4. F. P. Vasil’ev, Methods of to Solve Extremum Problems [in Russian], Nauka, Moscow (1981).

5. Yu. G. Evtushenko, Optimization and Fast Automatic Differentiation [in Russian], VTs im. A. A. Dorodnitsyna RAN, Moscow (2013). http://dx.doi.org/https://doi.org/10.1016/j.neunet.2014.09.003.

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Fast and Robust Constrained Optimization via Evolutionary and Quadratic Programming;Lecture Notes in Computer Science;2023

2. Stochastic Generalized Gradient Methods for Training Nonconvex Nonsmooth Neural Networks;Cybernetics and Systems Analysis;2021-09

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