Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Aryan Eftekhari"'
Autor:
Doris Folini, Pratyuksh Bansal, Aryan Eftekhari, Felix Kübler, Aleksandra Malova, Simon Scheidegger
Simple climate models or climate emulators (CEs) are indispensable in the context of climate economics, where the lion share of compute resources is bound to the economic part of the problem. Associated applications traditionally focused on scenarios
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ee32eca7144b679edafdb08577f82be9
https://doi.org/10.5194/egusphere-egu23-3471
https://doi.org/10.5194/egusphere-egu23-3471
Autor:
Simon Scheidegger, Aryan Eftekhari
Publikováno v:
SIAM Journal on Scientific Computing. 44:C210-C236
We propose a generic and scalable method for computing global solutions of nonlinear, high-dimensional dynamic stochastic economic models. First, within an MPI–TBB parallel time-iteration framework, we approximate economic policy functions using an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e5bd6b9c1259db1fe71936ae5ab1ff04
https://doi.org/10.1137/21m1392231
https://doi.org/10.1137/21m1392231
Publikováno v:
SIAM Journal on Scientific Computing. 41:A380-A401
The estimation of large sparse inverse covariance matrices is a ubiquitous statistical problem in many application areas such as mathematical finance, geology, health, and many others. The $\ell_1$...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8c9e57013c3bec0f82c7d45e02051c98
https://doi.org/10.1137/17m1147615
https://doi.org/10.1137/17m1147615
Autor:
Aryan Eftekhari, Matthias Bollhoefer, Simon Scheidegger, Olaf Schenk, Dimosthenis Pasadakis, Lisa Gaedke-Merzhaeuser
Publikováno v:
SSRN Electronic Journal.
High-dimensional sparse precision matrix estimation is a ubiquitous task in multivariate analysis with applications that cross many disciplines. In this paper, we introduce the SQUIC package, which benefits from superior runtime performance and scala
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::86df2db75ff4c5519611d26f56238638
https://doi.org/10.2139/ssrn.3904001
https://doi.org/10.2139/ssrn.3904001
Publikováno v:
Journal of Computational Science. 53:101389
The l1-regularized Gaussian maximum likelihood method is a common approach for sparse precision matrix estimation, but one that poses a computational challenge for high-dimensional datasets. We present a novel l1-regularized maximum likelihood method
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2698a5212907758ece2fc06657487a77
https://doi.org/10.1016/j.jocs.2021.101389
https://doi.org/10.1016/j.jocs.2021.101389
Publikováno v:
SC
We consider the problem of estimating sparse inverse covariance matrices for high-dimensional datasets using the l1-regularized Gaussian maximum likelihood method. This task is particularly challenging as the required computational resources increase
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::44826cbdb10acd8273084d4414966602
https://doi.org/10.1109/sc.2018.00023
https://doi.org/10.1109/sc.2018.00023
Publikováno v:
PASC
We introduce and deploy a generic, highly scalable computational method to solve high-dimensional dynamic stochastic economic models on high-performance computing platforms. Within an MPI---TBB parallel, nonlinear time iteration framework, we approxi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f1defb60c025e49b9bab36d38c9ecdee
https://doi.org/10.5167/uzh-169142
https://doi.org/10.5167/uzh-169142