Question

Easier way to show that $\text{Re} \{\text{trace}(X^* A)\} \leq \sum_{i =1}^{n}\sigma_{i}(A) $?

score 293 · Answer 1 · 2016-08-19 20:47:10

293

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Easier way to show that $\text{Re} \{\text{trace}(X^* A)\} \leq \sum_{i =1}^{n}\sigma_{i}(A) $?

Published on 19 Aug 2016 - 20:47

score 2k · Answer 2 · 2016-08-20 15:47:31

2k

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Is Singular Value Decomposition (SVD) a linear function?

Published on 20 Aug 2016 - 15:47

score 513 · Answer 3 · 2016-08-23 09:54:39

513

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stuck following proof that the rank r SVD approximation of a matrix D, D_r minimizes the Froebenius norm D-D_r

Published on 23 Aug 2016 - 9:54

score 4k · Answer 4 · 2026-03-25 09:53:49

4k

Views

Sensitivity of the least squares method and matrix condition number

Published on 25 Mar 2026 - 9:53

score 382 · Answer 5 · 2026-03-26 17:13:14

382

Views

If $X = U\Sigma V^T$ and $\|Y\|=1$, why does $Y = UV^T$ maximize $\langle X,Y\rangle$?

Published on 26 Mar 2026 - 17:13

score 1.7k · Answer 6 · 2016-09-10 12:56:49

1.7k

Views

Singular value decomposition of a matrix multiplication

Published on 10 Sep 2016 - 12:56

score 300 · Answer 7 · 2026-03-27 14:10:31

300

Views

How to impose a lower bound to the smallest singular value?

Published on 27 Mar 2026 - 14:10

score 192 · Answer 8 · 2016-09-15 00:34:13

192

Views

interpretation of singular value as stretching of a vector

Published on 15 Sep 2016 - 0:34

score 114 · Answer 9 · 2016-09-15 09:09:12

114

Views

How to fast get the SVD of $A-xx^T$ based on the SVD results of $A$?

Published on 15 Sep 2016 - 9:09

score 17.6k · Answer 10 · 2026-03-27 02:37:45

17.6k

Views

Is U=V in the SVD of a symmetric positive semidefinite matrix?

Published on 27 Mar 2026 - 2:37

score 570 · Answer 11 · 2016-09-20 15:17:08

570

Views

Zeroing rows in the Golub-Kahan SVD algorithm

Published on 20 Sep 2016 - 15:17

score 620 · Answer 12 · 2026-03-25 11:53:20

620

Views

SVD for theoretically singular matrix -- any way to improve solution?

Published on 25 Mar 2026 - 11:53

score 1.1k · Answer 13 · 2016-09-29 19:03:03

1.1k

Views

How to show that the low rank SVD preserves the $2$-norm?

Published on 29 Sep 2016 - 19:03

score 108 · Answer 14 · 2016-10-01 14:18:17

108

Views

Singular values of product of three rectangular matrices

Published on 01 Oct 2016 - 14:18

score 1.5k · Answer 15 · 2016-10-02 06:34:52

1.5k

Views

Understanding low-rank approximation, from the SVD

Published on 02 Oct 2016 - 6:34

List Question

Easier way to show that $\text{Re} \{\text{trace}(X^* A)\} \leq \sum_{i =1}^{n}\sigma_{i}(A) $?

Is Singular Value Decomposition (SVD) a linear function?

stuck following proof that the rank r SVD approximation of a matrix D, D_r minimizes the Froebenius norm D-D_r

Sensitivity of the least squares method and matrix condition number

If $X = U\Sigma V^T$ and $\|Y\|=1$, why does $Y = UV^T$ maximize $\langle X,Y\rangle$?

Singular value decomposition of a matrix multiplication

How to impose a lower bound to the smallest singular value?

interpretation of singular value as stretching of a vector

How to fast get the SVD of $A-xx^T$ based on the SVD results of $A$?

Is U=V in the SVD of a symmetric positive semidefinite matrix?

Zeroing rows in the Golub-Kahan SVD algorithm

SVD for theoretically singular matrix -- any way to improve solution?

How to show that the low rank SVD preserves the $2$-norm?

Singular values of product of three rectangular matrices

Understanding low-rank approximation, from the SVD

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