On large-scale unconstrained optimization and arbitrary regularization-Reference-Cited by-同舟云学术

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On large-scale unconstrained optimization and arbitrary regularization

Published:2021-10-25 Issue:1 Volume:81 Page:1-30
ISSN:0926-6003
Container-title:Computational Optimization and Applications
language:en
Short-container-title:Comput Optim Appl

Author:

Martínez J. M.,Santos L. T.^ORCID

Funder

Fundação de Amparo à Pesquisa do Estado de São Paulo

PRONEX-CNPq/FAPERJ

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computational Mathematics,Control and Optimization

Link

https://link.springer.com/content/pdf/10.1007/s10589-021-00322-2.pdf

Reference24 articles.

1. Bergou, E., Diouane, Y., Gratton, S.: On the use of the energy norm in trust-region and adaptive cubic regularization subproblems. Comput. Optim. Appl. 68, 533–554 (2017)

2. Bergou, E., Diouane, Y., Gratton, S.: A line-search algorithm inspired by the adaptive cubic regularization framework and complexity analysis. J. Optim. Theory Appl. 178, 885–913 (2018)

3. Birgin, E., Martínez, J.: A spectral conjugate gradient method for unconstrained optimization. Appl. Math. Optim. 43, 117–128 (2001)

4. Birgin, E., Martínez, J., Raydan, M.: Nonmonotone spectral projected gradient methods on convex sets. SIAM J. Optim. 10, 1196–1121 (2000)

5. Birgin, E., Martínez, J., Raydan, M.: Inexact spectral projected gradient methods on convex sets. IMA J. Numer. Anal. 23, 539–559 (2003)

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