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The SingularValueDecomposition(x,opts) or SVD(x,opts) commands compute a singular value decomposition of one or more matrices in x

$\mathrm{with}\left(\mathrm{DeepLearning}\right)\:$

$t≔\mathrm{Constant}\left(\mathrm{LinearAlgebra}:-\mathrm{VandermondeMatrix}\left(⟨3.\,5.\,7.⟩\right)\right)$

$t≔\left[\begin{array}{c}\mathrm{DeepLearning\; Tensor}\\ \mathrm{Name:\; Const:0}\\ \mathrm{Shape:\; [3,\; 3]}\\ \mathrm{Data\; Type:\; float[8]}\end{array}\right]$

$\mathrm{svd}≔\mathrm{SingularValueDecomposition}\left(t\right)$

$\mathrm{svd}≔\left[\begin{array}{c}\mathrm{DeepLearning\; Tensor}\\ \mathrm{Name:\; none}\\ \mathrm{Shape:\; undefined}\\ \mathrm{Data\; Type:\; undefined}\end{array}\right]$

$\mathrm{value}\left(\mathrm{svd}\right)$

$\left[\begin{array}{c}56.4641241067444\\ 2.18767256385950\\ 0.129528419652346\end{array}\right],\left[\begin{array}{ccc}0.166183955496311& 0.784470926894502& 0.597484943570030\\ 0.451491366392332& 0.478138549997582& \mathrm{-0.753351891933242}\\ 0.876663241506558& \mathrm{-0.394954290853373}& 0.274722895164790\end{array}\right],\left[\begin{array}{ccc}0.0264652748454962& 0.396611083565445& 0.917605164338342\\ 0.157492062963468& 0.904808849092890& \mathrm{-0.395622543223603}\\ 0.987165558217498& \mathrm{-0.154985789659065}& 0.0385170828710182\end{array}\right]$

The DeepLearning,Tensor,SingularValueDecomposition command was introduced in Maple 2018.

For more information on Maple 2018 changes, see Updates in Maple 2018.