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Fundamentals of matrix computations
David S. Watkins
- 01 Jan 1991
TL;DR: This paper focuses on Gaussian Elimination as a model for Iterative Methods for Linear Systems, and its applications to Singular Value Decomposition and Sparse Eigenvalue Problems.
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Abstract: Gaussian Elimination and its Variants Sensitivity of Linear Systems Effects of Roundoff Errors Orthogonal Matrices and the Least Squares Problem Eigenvalues, Eigenvectors and Invariant Subspaces Other Methods for the Symmetric Eigenvalue Problem The Singular Value Decomposition Appendices Bibliography
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Citations
DCT approximations based on Chen’s factorization
C.J. Tablada,C.J. Tablada,T. L. T. da Silveira,Renato J. Cintra,Renato J. Cintra,Fábio M. Bayer +5 more
TL;DR: Two 8-point multiplication-free DCT approximations based on the Chen’s factorization can outperform traditional transforms and state-of-the-art methods at a very low complexity cost.
9
Efficiency of the QR class estimator in semiparametric regression models to combat multicollinearity
Mahdi Roozbeh,Mohammad Najarian +1 more
TL;DR: In this article, a modified estimator based on the modified estimators for solving the multicollinearity among the predictor variables in statistical literature is proposed, which is a modification of the biased estimator.
9
Kernelised orthonormal random projection on grassmann manifolds with applications to action and gait-based gender recognition
Kun Zhao,Arnold Wiliem,Brian C. Lovell +2 more
- 23 Mar 2015
TL;DR: Experimental results in two biometric applications can achieve better accuracy than the state-of-the-art random projection method for manifold points, and comparisons with kernelised classifiers show that the method achieves nearly 3-fold speed up on average whilst maintaining the accuracy.
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A fast and stable parallel QR algorithm for symmetric tridiagonal matrices
Ilan Bar-On,Bruno Codenotti +1 more
TL;DR: A new divide and conquer parallel algorithm which is fast and numerically stable, work efficient and of low communication overhead, and it can be used to solve very large problems infeasible by sequential methods.
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