The singular value decomposition of a matrix A is a factorization A=USQT, where U and Q are orthogonal and S is a diagonal matrix. The svd command finds the singular value decomposition of a matrix.
You can get the diagonal matrix S from s with S=diag(s) (see Section 6.44.2).
Examples.
⎡ ⎢ ⎣ |
| ⎤ ⎥ ⎦ | , | ⎡ ⎣ | 5.46498570422,0.365966190626 | ⎤ ⎦ | , | ⎡ ⎢ ⎣ |
| ⎤ ⎥ ⎦ |
⎡ ⎢ ⎣ |
| ⎤ ⎥ ⎦ | , | ⎡ ⎣ | 8.6409011028,0.578643354497 | ⎤ ⎦ | , | ⎡ ⎢ ⎣ |
| ⎤ ⎥ ⎦ |
|