Geodesic
Convergence results on greedy algorithms for high-dimensional eigenvalue problems
Virginie Ehrlacher1
1 Université Paris Est, CERMICS, Projet MICMAC, Ecole des Ponts ParisTech - INRIA, 6 & 8 avenue Blaise Pascal, 77455 Marne-la-Vallée Cedex 2, France
ESAIM. Proceedings, Tome 45 (2014), pp. 148-157 Cet article a éte moissonné depuis la source EDP Sciences

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In this paper, we present two greedy algorithms for the computation of the lowest eigenvalue (and an associated eigenvector) of a high-dimensional eigenvalue problem, which have been introduced and analyzed recently in a joint work with Eric Cancès and Tony Lelièvre [1]. The performance of our algorithms is illustrated on toy numerical test cases, and compared with that of another greedy algorithm for eigenvalue problems introduced by Ammar and Chinesta [13].
DOI : 10.1051/proc/201445015

Virginie Ehrlacher 1

1 Université Paris Est, CERMICS, Projet MICMAC, Ecole des Ponts ParisTech - INRIA, 6 & 8 avenue Blaise Pascal, 77455 Marne-la-Vallée Cedex 2, France 
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     author = {Virginie Ehrlacher},
     title = {Convergence results on greedy algorithms for high-dimensional eigenvalue problems},
     journal = {ESAIM. Proceedings},
     pages = {148--157},
     year = {2014},
     volume = {45},
     doi = {10.1051/proc/201445015},
     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.1051/proc/201445015/}
}
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Virginie Ehrlacher. Convergence results on greedy algorithms for high-dimensional eigenvalue problems. ESAIM. Proceedings, Tome 45 (2014), pp. 148-157. doi: 10.1051/proc/201445015

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