A projection method for optimization problems on the Stiefel manifold

Oscar Dalmau-Cedeño, Harry Oviedo

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9 Citas (Scopus)

Resumen

In this paper we propose a feasible method based on projections using a curvilinear search for solving optimization problems with orthogonality constraints. Our algorithm computes the SVD decomposition in each iteration in order to preserve feasibility. Additionally, we present some convergence results. Finally, we perform numerical experiments with simulated problems; and analyze the performance of the proposed methods compared with state-of-the-art algorithms.

Idioma originalInglés
Título de la publicación alojadaPattern Recognition - 9th Mexican Conference, MCPR 2017, Proceedings
EditoresJesus Ariel Carrasco-Ochoa, Jose Francisco Martinez-Trinidad, Jose Arturo Olvera-Lopez
EditorialSpringer Verlag
Páginas84-93
Número de páginas10
ISBN (versión impresa)9783319592251
DOI
EstadoPublicada - 2017
Publicado de forma externa
Evento9th Mexican Conference on Pattern Recognition, MCPR 2017 - Huatulco, México
Duración: 21 jun. 201724 jun. 2017

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen10267 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia9th Mexican Conference on Pattern Recognition, MCPR 2017
País/TerritorioMéxico
CiudadHuatulco
Período21/06/1724/06/17

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