Zobrazeno 1 - 10
of 129
pro vyhledávání: '"Philippe L. Toint"'
Publikováno v:
EURO Journal on Computational Optimization, Vol 3, Iss 1, Pp 31-51 (2015)
We propose to use an observation-thinning method for the efficient numerical solution of large-scale incremental four- dimensional (4D-Var) data assimilation problems. This decomposition is based on exploiting an adaptive hierarchy of the observation
Externí odkaz:
https://doaj.org/article/62a298c003384283901627d22f1dae46
Publikováno v:
Quarterly Journal of the Royal Meteorological Society. 147:1949-1963
Publikováno v:
Frontiers of Mathematics in China. 15:367-384
This paper develops the Bernstein tensor concentration inequality for random tensors of general order, based on the use of Einstein products for tensors. This establishes a strong link between these and matrices, which in turn allows exploitation of
Publikováno v:
Gratton, S, Simon, E & Toint, P 2020, ' An algorithm for the minimization of nonsmooth nonconvex functions using inexact evaluations and its worst-case complexity ', Mathematical Programming, vol. 187, no. 1-2, pp. 1-24 . https://doi.org/10.1007/s10107-020-01466-5
Mathematical Programming, Series A
Mathematical Programming, Series A, Springer, 2020, ⟨10.1007/s10107-020-01466-5⟩
Mathematical Programming, Series A
Mathematical Programming, Series A, Springer, 2020, ⟨10.1007/s10107-020-01466-5⟩
An adaptive regularization algorithm using inexact function and derivatives evaluations is proposed for the solution of composite nonsmooth nonconvex optimization. It is shown that this algorithm needs at most $$O(|\log (\epsilon )|\,\epsilon ^{-2})$
Autor:
Margherita Porcelli, Philippe L. Toint
Publikováno v:
ACM transactions on mathematical software (Online) 48 (2022). doi:10.1145/3474054
info:cnr-pdr/source/autori:Porcelli M.; Toint P.L./titolo:Exploiting problem structure in derivative free optimization/doi:10.1145%2F3474054/rivista:ACM transactions on mathematical software (Online)/anno:2022/pagina_da:/pagina_a:/intervallo_pagine:/volume:48
Porcelli, M & Toint, P 2022, ' Exploiting problem structure in Derivative-Free Optimization ', ACM Transactions on Mathematical Software, vol. 48, no. 1, 6 . https://doi.org/10.1145/3474054
info:cnr-pdr/source/autori:Porcelli M.; Toint P.L./titolo:Exploiting problem structure in derivative free optimization/doi:10.1145%2F3474054/rivista:ACM transactions on mathematical software (Online)/anno:2022/pagina_da:/pagina_a:/intervallo_pagine:/volume:48
Porcelli, M & Toint, P 2022, ' Exploiting problem structure in Derivative-Free Optimization ', ACM Transactions on Mathematical Software, vol. 48, no. 1, 6 . https://doi.org/10.1145/3474054
A structured version of derivative-free random pattern search optimization algorithms is introduced, which is able to exploit coordinate partially separable structure (typically associated with sparsity) often present in unconstrained and bound-const
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f6086b262d90c40489020bb21732cb4f
We show to what extent the accuracy of the inner products computed in the GMRES iterative solver can be reduced as the iterations proceed without affecting the convergence rate or final accuracy achieved by the iterates. We bound the loss of orthogon
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5f3cf4d7bc5f288015ef8857eaa2a298
https://ut3-toulouseinp.hal.science/hal-03637231
https://ut3-toulouseinp.hal.science/hal-03637231
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
Publikováno v:
Chen, X, Toint, P & Wang, H 2019, ' Complexity of partially separable convexly constrained optimization with non-Lipschitzian singularities ', SIAM Journal on Optimization, vol. 29, no. 1, pp. 874-903 . https://doi.org/10.1137/18m1166511
An adaptive regularization algorithm using high-order models is proposed for solving partially separable convexly constrained nonlinear optimization problems whose objective function contains non-Lipschitzian 'q-norm regularization terms for q ϵ (0;
Publikováno v:
Gratton, S, Selime, G, Simon, E & Toint, P 2018, ' A note on preconditioning weighted linear least-squares with consequences for weakly constrained variational data assimilation ', Quarterly Journal of the Royal Meteorological Society, vol. 144, no. 712, pp. 934-940 . https://doi.org/10.1002/qj.3262
Quarterly Journal of the Royal Meteorological Society
Quarterly Journal of the Royal Meteorological Society, Wiley, 2018, 144 (712), pp.934-940. ⟨10.1002/qj.3262⟩
Quarterly Journal of the Royal Meteorological Society
Quarterly Journal of the Royal Meteorological Society, Wiley, 2018, 144 (712), pp.934-940. ⟨10.1002/qj.3262⟩
The effect of preconditioning linear weighted least-squares using an approximation of the model matrix is analyzed, showing the interplay of the eigenstructures of both the model and weighting matrices. A small example is given illustrating the resul