Matrix decomposition

Results: 1432



#Item
21Computing the Complete CS Decomposition Brian D. Sutton∗ May 20, 2008 Abstract An algorithm for computing the complete CS decomposition of a partitioned unitary matrix is developed. Although the existence of the CS dec

Computing the Complete CS Decomposition Brian D. Sutton∗ May 20, 2008 Abstract An algorithm for computing the complete CS decomposition of a partitioned unitary matrix is developed. Although the existence of the CS dec

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Source URL: faculty.rmc.edu

Language: English - Date: 2008-05-20 14:06:05
22Distributed Community Detection on Edge-labeled Graphs using Spark San-Chuan Hung Miguel Araujo

Distributed Community Detection on Edge-labeled Graphs using Spark San-Chuan Hung Miguel Araujo

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Source URL: www.mlgworkshop.org

Language: English - Date: 2016-08-14 01:22:56
23Computer Vision Exercise Session 2 Institute of Visual Computing

Computer Vision Exercise Session 2 Institute of Visual Computing

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Source URL: www.cvg.ethz.ch

Language: English - Date: 2015-10-06 15:10:50
24CS168: The Modern Algorithmic Toolbox Lecture #8: How PCA Works Tim Roughgarden & Gregory Valiant∗ April 20,

CS168: The Modern Algorithmic Toolbox Lecture #8: How PCA Works Tim Roughgarden & Gregory Valiant∗ April 20,

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Source URL: theory.stanford.edu

Language: English - Date: 2016-06-04 09:49:44
25Appendix A: Face subclasses In Fig. 3, we showed an example of features versus context. In that example, we illustrated the idea with the class “eyes.” In Fig. S1 we show the subclasses for the class “face” and t

Appendix A: Face subclasses In Fig. 3, we showed an example of features versus context. In that example, we illustrated the idea with the class “eyes.” In Fig. S1 we show the subclasses for the class “face” and t

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Source URL: www2.ece.ohio-state.edu

Language: English - Date: 2010-02-11 05:58:43
261 Factorization-based Sparse Solvers and Preconditioners (4th Gene Golub SIAM Summer School, 2013)

1 Factorization-based Sparse Solvers and Preconditioners (4th Gene Golub SIAM Summer School, 2013)

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Source URL: crd-legacy.lbl.gov

Language: English - Date: 2015-07-29 21:36:24
27BIT 39(1), pp. 143–151, 1999 ILL-CONDITIONEDNESS NEEDS NOT BE COMPONENTWISE NEAR TO ILL-POSEDNESS FOR LEAST SQUARES PROBLEMS SIEGFRIED M. RUMP∗ Abstract. The condition number of a problem measures the sensitivity of

BIT 39(1), pp. 143–151, 1999 ILL-CONDITIONEDNESS NEEDS NOT BE COMPONENTWISE NEAR TO ILL-POSEDNESS FOR LEAST SQUARES PROBLEMS SIEGFRIED M. RUMP∗ Abstract. The condition number of a problem measures the sensitivity of

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Source URL: www.ti3.tu-harburg.de

Language: English - Date: 2005-11-23 05:51:43
28The dynamic mode decomposition (DMD) is a data-driven matrix decomposition for spatio-temporal data grids. Originally introduced in the fluid mechanics community, DMD has been applied to several new domains, including ne

The dynamic mode decomposition (DMD) is a data-driven matrix decomposition for spatio-temporal data grids. Originally introduced in the fluid mechanics community, DMD has been applied to several new domains, including ne

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Source URL: mmds-data.org

- Date: 2016-06-23 15:50:48
    29Parallel Iterative Refinement in Polynomial Eigenvalue Problems∗ Carmen Campos Jose E. Roman

    Parallel Iterative Refinement in Polynomial Eigenvalue Problems∗ Carmen Campos Jose E. Roman

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    Source URL: users.dsic.upv.es

    Language: English - Date: 2016-03-04 13:07:36