svd

MapdlMath.svd(mat, thresh='', sig='', v='', **kwargs)

Apply an SVD Algorithm on a matrix.

The SVD algorithm is only applicable to dense matrices. Columns that are linearly dependent on others are removed, leaving the independent or basis vectors. The matrix is resized according to the new size determined by the algorithm.

For the SVD algorithm, the singular value decomposition of an input matrix is a factorization of the form:

M = U*SIGMA*V.T

For more details, see: https://en.wikipedia.org/wiki/Singular_value_decomposition

Parameters
  • mat (ansys.AnsMat) – The array to compress.

  • thresh (float, optional) – Numerical threshold value used to manage the compression. Default is 1E-7.

  • sig (str, optional) – Name of the vector used to store the SIGMA values.

  • v (str, optional) – Name of the vector used to store the values from v. See the equation above.

Examples

Apply SVD on an existing Dense Rectangular Matrix, using default threshold. The matrix is modified in-place.

>>> mm.svd(mat)