A limited-memory trust-region method for nonlinear optimization with many equality constraints-Reference-Cited by-同舟云学术

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A limited-memory trust-region method for nonlinear optimization with many equality constraints

Published:2023-03-10 Issue:3 Volume:42 Page:
ISSN:2238-3603
Container-title:Computational and Applied Mathematics
language:en
Short-container-title:Comp. Appl. Math.

Author:

Lee Jae Hwa,Jung Yoon Mo,Yun Sangwoon^ORCID

Funder

National Research Foundation of Korea

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computational Mathematics

Link

https://link.springer.com/content/pdf/10.1007/s40314-023-02251-8.pdf

Reference35 articles.

1. Bottou L, Curtis FE, Nocedal J (2018) Optimization methods for large-scale machine learning. SIAM Rev 60:223–311. https://doi.org/10.1137/16M1080173

2. Brust JJ, Erway JB, Marcia RF (2017) On solving L-SR1 trust-region subproblems. Comput Optim Appl 66:245–266. https://doi.org/10.1007/s10589-016-9868-3

3. Brust JJ, Marcia RF, Petra CG (2019) Large-scale quasi-newton trust-region methods with low-dimensional linear equality constraints. Comput Optim Appl 74:669–701. https://doi.org/10.1007/s10589-019-00127-4

4. Brust JJ, Burdakov O, Erway JB, Marcia RF, Yuan YX (2022) Algorithm 1030: SC-SR1: MATLAB software for limitedmemory SR1 trust-region methods. ACM Trans. Math. Softw. 48, 1–33. https://doi.org/10.1145/3550269

5. Brust JJ, Erway JB, Marcia RF (2022b) Shape-changing trust-region methods using multipoint symmetric secant matrices. arXiv:2209.12057

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