Complexity bound of a Levenberg–Marquardt algorithm based on probabilistic Jacobian models
Author:
Funder
Science and Technology Innovation Plan Of Shanghai Science and Technology Commission
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11590-024-02140-x.pdf
Reference23 articles.
1. Banderia, A.S., Scheinberg, K., Vicente, L.N.: Convergence of trust-region methods based on probabilistic models. SIAM J. Optim. 24, 1238–1264 (2014)
2. Bergou, E.H., Gratton, S., Vicente, L.N.: Levenberg–Marquardt methods based on probabilistic gradient models and inexact subproblem solution, with application to data assimilation. SIAM/ASA J. Uncertain. Quantif. 4, 924–951 (2016)
3. Bergou, E.H., Diouane, Y., Kungurtsev, V., Royer, C.W.: A stochastic Levenberg–Marquardt method using random models with complexity results. SIAM/ASA J. Uncertain. Quantif. (2022). https://doi.org/10.1137/20M1366253
4. Blanchet, J., Cartis, C., Menickelly, M., Scheinberg, K.: Convergence rate analysis of a stochastic trust region method for nonconvex optimization. INFORMS J. Optim. 1, 92–119 (2019)
5. Cartis, C., Roberts, L.: A derivative-free Gauss–Newton method. Math. Program. Comput. 11, 631–674 (2019)
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