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| Indexado |
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| DOI | 10.1137/130916710 | ||||
| Año | 2014 | ||||
| Tipo | artículo de investigación |
Citas Totales
Autores Afiliación Chile
Instituciones Chile
% Participación
Internacional
Autores
Afiliación Extranjera
Instituciones
Extranjeras
Matrix variables are ubiquitous in modern optimization, in part because variational properties of useful matrix functions often expedite standard optimization algorithms. Convexity is one important such property: permutation-invariant convex functions of the eigenvalues of a symmetric matrix are convex, leading to the wide applicability of semidefinite programming algorithms. We prove the analogous result for the property of "identifiability," a notion central to many active-set-type optimization algorithms.
| Ord. | Autor | Género | Institución - País |
|---|---|---|---|
| 1 | Daniilidis, Aris | Hombre |
Universidad de Chile - Chile
Universitat Autònoma de Barcelona - España |
| 2 | Drusvyatskiy, Dmitriy | Hombre |
Univ Waterloo - Canadá
University of Waterloo - Canadá |
| 3 | Lewis, A. S. | - |
CORNELL UNIV - Estados Unidos
Cornell University - Estados Unidos |
| Fuente |
|---|
| National Science Foundation |
| FONDECYT (Chile) |
| NDSEG grant from the Department of Defense |
| Direct For Mathematical & Physical Scien; Division Of Mathematical Sciences |