Zobrazeno 1 - 10
of 302
pro vyhledávání: '"I. A. Gould"'
Autor:
A. Bianchini, G. Bangga, I. Baring-Gould, A. Croce, J. I. Cruz, R. Damiani, G. Erfort, C. Simao Ferreira, D. Infield, C. N. Nayeri, G. Pechlivanoglou, M. Runacres, G. Schepers, B. Summerville, D. Wood, A. Orrell
Publikováno v:
Wind Energy Science, Vol 7, Pp 2003-2037 (2022)
While modern wind turbines have become by far the largest rotating machines on Earth with further upscaling planned for the future, a renewed interest in small wind turbines (SWTs) is fostering energy transition and smart grid development. Small mach
Externí odkaz:
https://doaj.org/article/12c7f7f950d740dfaaab888767a4c5cd
Publikováno v:
Wind Energy Science, Vol 7, Pp 455-467 (2022)
In this work, we extend the AeroDyn module of OpenFAST to support arbitrary collections of wings, rotors, and towers. The new standalone AeroDyn driver supports arbitrary motions of the lifting surfaces and complex turbulent inflows. Aerodynamics and
Externí odkaz:
https://doaj.org/article/f6721904d50b47eea910bed6b582d6f5
Autor:
Gould, E. / I. H., 2, E. / I. H. Gould
Publikováno v:
J19: The Journal of Nineteenth-Century Americanists; Spring2023, Vol. 11 Issue 1, p203-212, 10p
Publikováno v:
Optimization Methods and Software. 35:243-256
An adaptive regularization algorithm is proposed that uses Taylor models of the objective of order p, (Formula presented.), of the unconstrained objective function, and that is guaranteed to find a first- and second-order critical point in at most (F
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e19ac682fc9746c0364b889009f43696
https://doi.org/10.1080/10556788.2019.1678033
https://doi.org/10.1080/10556788.2019.1678033
Publikováno v:
Optimization methods & software
35 (2019): 304–328. doi:10.1080/10556788.2019.1670177
info:cnr-pdr/source/autori:N.I. Gould and V. Simoncini/titolo:Error estimates for iterative algorithms for minimizing regularized quadratic subproblems/doi:10.1080%2F10556788.2019.1670177/rivista:Optimization methods & software (Print)/anno:2019/pagina_da:304/pagina_a:328/intervallo_pagine:304–328/volume:35
35 (2019): 304–328. doi:10.1080/10556788.2019.1670177
info:cnr-pdr/source/autori:N.I. Gould and V. Simoncini/titolo:Error estimates for iterative algorithms for minimizing regularized quadratic subproblems/doi:10.1080%2F10556788.2019.1670177/rivista:Optimization methods & software (Print)/anno:2019/pagina_da:304/pagina_a:328/intervallo_pagine:304–328/volume:35
We derive bounds for the objective errors and gradient residuals when finding approximations to the solution of common regularized quadratic optimization problems within evolving Krylov spaces. These provide upper bounds on the number of iterations r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dfb9c1c6208e420f49b607f7ff2d3a17
https://doi.org/10.1080/10556788.2019.1670177
https://doi.org/10.1080/10556788.2019.1670177
Publikováno v:
Computational Optimization and Applications. 73:1-35
Given a twice-continuously differentiable vector-valued function r(x), a local minimizer of $$\Vert r(x)\Vert _2$$ is sought. We propose and analyse tensor-Newton methods, in which r(x) is replaced locally by its second-order Taylor approximation. Co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a80e6707236fc7846b3b9b4c7b2c46c9
https://doi.org/10.1007/s10589-019-00064-2
https://doi.org/10.1007/s10589-019-00064-2
Autor:
Peter Weston, Xiangkun Li, Parangat Shukla, Edward I. Baring-Gould, Arthur Jacquiau-Chamski, Samuel Booth, Sean Esterly, Jon Thacker, Jonathan Clowes
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4735306896c0d8645424db003d9bfb54
https://doi.org/10.2172/1597244
https://doi.org/10.2172/1597244
Publikováno v:
Toint, P 2017, ' Worst-case evaluation complexity of regularization methods for smooth unconstrained optimization using Hölder continuous gradients ', Optimization Methods and Software, vol. 32, no. 6, pp. 1273-1298 . https://doi.org/10.1080/10556788.2016.1268136
The worst-case behaviour of a general class of regularization algorithms is considered in the case where only objective function values and associated gradient vectors are evaluated. Upper bounds are derived on the number of such evaluations that are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a75119731d43a1ec8734ef1a4435852a
https://researchportal.unamur.be/en/publications/bc81bf28-15b6-4fa0-b632-a6fee775d459
https://researchportal.unamur.be/en/publications/bc81bf28-15b6-4fa0-b632-a6fee775d459
Publikováno v:
ACM Transactions on Mathematical Software. 43:1-35
In recent years, a variety of preconditioners have been proposed for use in solving large sparse linear least-squares problems. These include simple diagonal preconditioning, preconditioners based on incomplete factorizations, and stationary inner it
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::24f93e5d9bec19c5076f91a972ef989b
https://doi.org/10.1145/3014057
https://doi.org/10.1145/3014057