Memoryless Quasi-Newton Methods Based on the Spectral-Scaling Broyden Family for Riemannian Optimization

Autor: Yasushi Narushima, Shummin Nakayama, Masashi Takemura, Hiroshi Yabe

Publikováno v: Journal of Optimization Theory and Applications. 197:639-664

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::dc8a6bbf9198cd17e43067e498ed8a22
https://doi.org/10.1007/s10957-023-02183-7

Inexact proximal memoryless quasi-Newton methods based on the Broyden family for minimizing composite functions

Autor: Shummin Nakayama, Yasushi Narushima, Hiroshi Yabe

Publikováno v: Computational Optimization and Applications. 79:127-154

This study considers a proximal Newton-type method to solve the minimization of a composite function that is the sum of a smooth nonconvex function and a nonsmooth convex function. In general, the method uses the Hessian matrix of the smooth portion

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::397a5a6ffb1f0b7a6fe5fe18dae67859
https://doi.org/10.1007/s10589-021-00264-9

Shaking Table Tests on Level Ground Model Simulating Construction of Sand Compaction Piles

Autor: Hiroshi Yabe, Junichi Koseki, Kenji Harada, Keiichi Tanaka

Publikováno v: Proceedings of the 4th International Conference on Performance Based Design in Earthquake Geotechnical Engineering (Beijing 2022) ISBN: 9783031118975

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::e2d7b48cc61902764fc32b92b3766bd7
https://doi.org/10.1007/978-3-031-11898-2_103

Memoryless quasi-Newton methods based on spectral-scaling Broyden family for unconstrained optimization

Autor: Yasushi Narushima, Hiroshi Yabe, Shummin Nakayama

Publikováno v: Journal of Industrial & Management Optimization. 15:1773-1793

Memoryless quasi-Newton methods are studied for solving large-scale unconstrained optimization problems. Recently, memoryless quasi-Newton methods based on several kinds of updating formulas were proposed. Since the methods closely related to the con

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d9724c8d85eb46fabeb0ecb37f472741
https://doi.org/10.3934/jimo.2018122

An active-set memoryless quasi-Newton method based on a spectral-scaling Broyden family for bound constrained optimization

Autor: Hiroshi Yabe, Yasushi Narushima, Hiroaki Nishio, Shummin Nakayama

Publikováno v: Results in Control and Optimization, Vol 3, Iss, Pp 100012-(2021)

In this paper, we consider an active-set algorithm for solving large-scale bound constrained optimization problems. First, by incorporating a restart technique, we modify the active-set strategy by Yuan and Lu (2011) and combine it with the memoryles

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d8c13958c481b541cdebeee74832abbb
https://doi.org/10.1016/j.rico.2021.100012