New subspace minimization conjugate gradient methods based on regularization model for unconstrained optimization-Reference-Cited by-同舟云学术

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New subspace minimization conjugate gradient methods based on regularization model for unconstrained optimization

Published:2020-10-28 Issue:4 Volume:87 Page:1501-1534
ISSN:1017-1398
Container-title:Numerical Algorithms
language:en
Short-container-title:Numer Algor

Author:

Zhao Ting,Liu Hongwei,Liu Zexian

Funder

National Science Foundation of China

Guangxi Natural Science Foundation

China Postdoctoral Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics

Link

https://link.springer.com/content/pdf/10.1007/s11075-020-01017-1.pdf

Reference52 articles.

1. Andrei, N.: An accelerated subspace minimization three-term conjugate gradient algorithm for unconstrained optimization. Numer. Algorithm. 65, 859–874 (2014)

2. Andrea, C., Tayebeh, D.N., Stefano, L.: On global minimizers of quadratic functions with cubic regularization. Optim. Lett. 13, 1269–1283 (2019)

3. Barzilai, J., Borwein, J.M.: Two-point step size gradient methods. IMA J. Numer. Anal. 8, 141–148 (1988)

4. Bellavia, S., Morini, B., Cartis, C., Gould, N.I.M., Toint, P.L.: Convergence of a regularized euclidean residual algorithm for nonlinear least-squares. SIAM J. Numer. Anal. 48, 1–29 (2010)

5. Bellavia, S., Morini, B.: Strong local convergence properties of adaptive regularized methods for nonlinear least squares. IMA J. Numer. Anal. 35, 947–968 (2014)

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