Difference between revisions of "SVD Command"
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;SVD( <Matrix> ) | ;SVD( <Matrix> ) | ||
:Returns the [[w:Singular_value_decomposition | Singular Value Decomposition]] of the matrix (as a list of 3 matrices). | :Returns the [[w:Singular_value_decomposition | Singular Value Decomposition]] of the matrix (as a list of 3 matrices). | ||
− | :{{example|1=<div><code><nowiki>SVD | + | :{{example|1=<div><code><nowiki>SVD({{3, 1, 1}, {-1, 3, 1}})</nowiki></code> yields a list containing <math> \left(\begin{array}{}-0.71&0.71\\0.71&0.71\\\end{array}\right) </math>, <math> \left(\begin{array}{}3.16&0\\0&3.46\\\end{array}\right)</math>, <math>\left(\begin{array}{}-0.89&0.41\\0.45&0.82\\0&0.41\\\end{array}\right)</math>.</div>}} |
{{note|1=<div> | {{note|1=<div> |
Latest revision as of 17:58, 8 August 2019
- SVD( <Matrix> )
- Returns the Singular Value Decomposition of the matrix (as a list of 3 matrices).
- Example:
SVD({{3, 1, 1}, {-1, 3, 1}})
yields a list containing \left(\begin{array}{}-0.71&0.71\\0.71&0.71\\\end{array}\right) , \left(\begin{array}{}3.16&0\\0&3.46\\\end{array}\right), \left(\begin{array}{}-0.89&0.41\\0.45&0.82\\0&0.41\\\end{array}\right).
Note:
- This command is also supported in the CAS View. The numbers in the answer may vary in order between the Algebra View and CAS View.
- See also Eigenvalues Command, Eigenvectors Command, Invert Command, Transpose Command, JordanDiagonalization Command