List questions from svd

Published on 06 Jun 2016 - 21:16

287

Views

why bother with extra orthonormal vector in Singular value decomposition

Published on 08 Jun 2016 - 9:56

78

Views

Efficient distributed algorithm for Singular Value decomposition

Published on 09 Jun 2016 - 11:26

102

Views

Prove that $A^2=A\iff \Sigma K=I_r$

Published on 18 Mar 2026 - 2:18

738

Views

low rank approximations and diagonalization

Published on 14 Jun 2016 - 17:02

89

Views

invertible subspaces and SVD

Published on 17 Jun 2016 - 19:52

90

Views

Find SVD of $A$

Published on 29 Mar 2026 - 23:43

3.1k

Views

Minimizing $\|Ax\|_2$ subject to $\|x\|_2 = 1$

Published on 20 Jun 2016 - 9:25

38

Views

Given A $m\times n$, $\{u_1,...,u_n\}$ ON basis of $R^n$, prove eigenvalues aren't negative

Published on 29 Mar 2026 - 23:45

817

Views

Compute the SVD of $AB$ from the SVDs of $A$ and $B$

Published on 23 Jun 2016 - 21:51

2.5k

Views

Proof that left singular vectors in SVD are orthogonal, and proof of low-rank approximation

Published on 25 Jun 2016 - 3:46

621

Views

Maximizing the trace of a complex matrix

Published on 05 Jul 2016 - 13:01

1.8k

Views

SVD decomposition to UV decomposition

Published on 02 Apr 2013 - 18:02

1.9k

Views

Least square solution based on the pseudoinverse solved efficiently with singular value decomposition

Published on 04 Apr 2013 - 23:17

166

Views

Detecting a clustering matrix via the SVD

Published on 07 Apr 2013 - 23:58

List Question

The SVD Solution to Linear Least Squares / Linear System of Equations

why bother with extra orthonormal vector in Singular value decomposition

Efficient distributed algorithm for Singular Value decomposition

Prove that $A^2=A\iff \Sigma K=I_r$

low rank approximations and diagonalization

invertible subspaces and SVD

Find SVD of $A$

Minimizing $\|Ax\|_2$ subject to $\|x\|_2 = 1$

Given A $m\times n$, $\{u_1,...,u_n\}$ ON basis of $R^n$, prove eigenvalues aren't negative

Compute the SVD of $AB$ from the SVDs of $A$ and $B$

Proof that left singular vectors in SVD are orthogonal, and proof of low-rank approximation

Maximizing the trace of a complex matrix

SVD decomposition to UV decomposition

Least square solution based on the pseudoinverse solved efficiently with singular value decomposition

Detecting a clustering matrix via the SVD

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