Scalable adaptive cubic regularization methods-Reference-Cited by-同舟云学术

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Scalable adaptive cubic regularization methods

Published:2023-10-31 Issue: Volume: Page:
ISSN:0025-5610
Container-title:Mathematical Programming
language:en
Short-container-title:Math. Program.

Author:

Dussault Jean-Pierre,Migot Tangi,Orban Dominique^ORCID

Funder

Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada

Canada First Research Excellence Fund

Publisher

Springer Science and Business Media LLC

Subject

General Mathematics,Software

Link

https://link.springer.com/content/pdf/10.1007/s10107-023-02007-6.pdf

Reference38 articles.

1. Bianconcini, T., Liuzzi, G., Morini, B., Sciandrone, M.: On the use of iterative methods in cubic regularization for unconstrained optimization. Comput. Optim. Appl. 60(1), 35–57 (2014). https://doi.org/10.1007/s10589-014-9672-x

2. Birgin, E.G., Gardenghi, J.L., Martínez, J.M., Santos, S.A., Toint, Ph.L.: Worst-case evaluation complexity for unconstrained nonlinear optimization using high-order regularized models. Math. Progr. 163(1–2), 359–368 (2016). https://doi.org/10.1007/s10107-016-1065-8

3. Carmon, Y., Duchi, J.: Gradient descent finds the cubic-regularized nonconvex Newton step. SIAM J. Optim. 29(3), 2146–2178 (2019). https://doi.org/10.1137/17m1113898

4. Cartis, C., Gould, N.I.M., Toint, Ph.L.: Adaptive cubic regularisation methods for unconstrained optimization. Part I: motivation, convergence and numerical results. Math. Program. 127(2), 245–295 (2011). https://doi.org/10.1007/s10107-009-0286-5

5. Cartis, C., Gould, N.I.M., Toint, Ph.L.: Adaptive cubic regularisation methods for unconstrained optimization. Part II: worst-case function- and derivative-evaluation complexity. Math. Program. 130(2), 295–319 (2011). https://doi.org/10.1007/s10107-009-0337-y

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