A projection method for optimization problems on the Stiefel manifold

Oscar Dalmau-Cedeño, Harry Oviedo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations


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.

Original languageEnglish
Title of host publicationPattern Recognition - 9th Mexican Conference, MCPR 2017, Proceedings
EditorsJesus Ariel Carrasco-Ochoa, Jose Francisco Martinez-Trinidad, Jose Arturo Olvera-Lopez
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319592251
StatePublished - 2017
Externally publishedYes
Event9th Mexican Conference on Pattern Recognition, MCPR 2017 - Huatulco, Mexico
Duration: 21 Jun 201724 Jun 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10267 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th Mexican Conference on Pattern Recognition, MCPR 2017


  • Constrained optimization
  • Non-monotone algorithm
  • Optimization on manifolds
  • Orthogonality constraints
  • Stiefel manifold


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