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Article
Optimality of Balanced Proper Orthogonal Decomposition for Data Reconstruction II: Further Approximation Results
Journal of Mathematical Analysis and Applications
  • John R Singler, Missouri University of Science and Technology
Abstract
In our earlier paper Singler (2010), we showed two separate data sets can be optimally approximated using balanced proper orthogonal decomposition (POD) modes derived from the data. In this work, we prove new results concerning the approximation capability of the balanced POD modes. We give exact computable expressions for the errors between the individual data sets and the low order balanced POD data reconstructions. We also consider approximating elements of the Hilbert space using various projections onto the balanced POD modes. We discuss the relevance of these results to balanced POD model reduction of nonlinear partial differential equations.
Department(s)
Mathematics and Statistics
Research Center/Lab(s)
Center for High Performance Computing Research
Keywords and Phrases
  • Balanced Proper Orthogonal Decomposition,
  • Data Approximation,
  • Hilbert-Schmidt Operators,
  • Proper Orthogonal Decomposition
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2015 Elsevier, All rights reserved.
Publication Date
1-1-2015
Citation Information
John R Singler. "Optimality of Balanced Proper Orthogonal Decomposition for Data Reconstruction II: Further Approximation Results" Journal of Mathematical Analysis and Applications Vol. 421 Iss. 2 (2015) p. 1006 - 1020 ISSN: 0022247X
Available at: http://works.bepress.com/john-singler/36/