Question

Can we use the singular value decomposition to compute the matrix exponential for a non-diagonalisable matrix?

score 533 · Answer 1 · 2026-03-27 14:28:34

533

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Can we use the singular value decomposition to compute the matrix exponential for a non-diagonalisable matrix?

Published on 27 Mar 2026 - 14:28

score 21 · Answer 2 · 2023-03-08 07:27:20

21

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SVD of matrix of eigenvectors

Published on 08 Mar 2023 - 7:27

score 64 · Answer 3 · 2023-03-15 01:49:58

64

Views

Does SVD work for non-square matrices?

Published on 15 Mar 2023 - 1:49

score 52 · Answer 4 · 2026-03-26 16:03:51

52

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singular value decomposition of sum

Published on 26 Mar 2026 - 16:03

score 47 · Answer 5 · 2023-03-22 18:14:17

47

Views

Proof check that $\|A\|_{F}^{2} = \sigma_{1}^{2} + \sigma_{2}^{2} + \dots + \sigma_{k}^{2}$

Published on 22 Mar 2023 - 18:14

score 899 · Answer 6 · 2026-03-26 20:38:48

899

Views

SVD decomposition of block matrix

Published on 26 Mar 2026 - 20:38

score 409 · Answer 7 · 2023-03-30 07:51:17

409

Views

If $M\in M_{m \times n}(\mathbb{R})$ is a rank $r \geq 1$ matrix then it can be written as a sum of exactly $r$ rank 1 matrices

Published on 30 Mar 2023 - 7:51

score 26 · Answer 8 · 2023-03-31 21:55:03

26

Views

Calculating Randomized Singular Values has two main steps

Published on 31 Mar 2023 - 21:55

score 264 · Answer 9 · 2023-04-03 18:19:37

264

Views

How we can proof SVD : $M = U\sigma V^T = \sum_{i=1}^r \sigma_i u_i v_i^T $

Published on 03 Apr 2023 - 18:19

score 146 · Answer 10 · 2023-04-04 03:25:17

146

Views

Bound of $\lVert \mathbf{Ax} \rVert$ with $\mathbf{AA}^\top=\mathbf{I}$

Published on 04 Apr 2023 - 3:25

score 53 · Answer 11 · 2023-04-05 17:46:32

53

Views

Fast algorithm for incremental randomized SVD

Published on 05 Apr 2023 - 17:46

score 34 · Answer 12 · 2023-04-06 05:38:42

34

Views

Show that a non invertible matrix $B$ cannot exist for $||A\vec x - B\vec x || \le 1$

Published on 06 Apr 2023 - 5:38

score 32 · Answer 13 · 2023-04-08 10:30:42

32

Views

Eigenvalue decompositin equal to singular value decomposition

Published on 08 Apr 2023 - 10:30

score 36 · Answer 14 · 2026-03-27 03:40:59

36

Views

Given Gaussian "noise" matrix $G$ and matrix products $AG$, and $A^\intercal AG$, solve for $A$

Published on 27 Mar 2026 - 3:40

score 274 · Answer 15 · 2023-04-16 04:50:18

274

Views

SVDs of the matrix $\begin{bmatrix} 1 & 1 \\ 0 & 0\end{bmatrix}$

Published on 16 Apr 2023 - 4:50

List Question

Can we use the singular value decomposition to compute the matrix exponential for a non-diagonalisable matrix?

SVD of matrix of eigenvectors

Does SVD work for non-square matrices?

singular value decomposition of sum

Proof check that $\|A\|_{F}^{2} = \sigma_{1}^{2} + \sigma_{2}^{2} + \dots + \sigma_{k}^{2}$

SVD decomposition of block matrix

If $M\in M_{m \times n}(\mathbb{R})$ is a rank $r \geq 1$ matrix then it can be written as a sum of exactly $r$ rank 1 matrices

Calculating Randomized Singular Values has two main steps

How we can proof SVD : $M = U\sigma V^T = \sum_{i=1}^r \sigma_i u_i v_i^T $

Bound of $\lVert \mathbf{Ax} \rVert$ with $\mathbf{AA}^\top=\mathbf{I}$

Fast algorithm for incremental randomized SVD

Show that a non invertible matrix $B$ cannot exist for $||A\vec x - B\vec x || \le 1$

Eigenvalue decompositin equal to singular value decomposition

Given Gaussian "noise" matrix $G$ and matrix products $AG$, and $A^\intercal AG$, solve for $A$

SVDs of the matrix $\begin{bmatrix} 1 & 1 \\ 0 & 0\end{bmatrix}$

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