Difference between revisions of "SVD Command"

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

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 ;SVD( <Matrix> )
 :Returns the [[w:Singular_value_decomposition | Singular Value Decomposition]] of the matrix (as a list of 3 matrices).
-:{{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>}}
+:{{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>