svd_powers


svd_powers, a Python code which applies singular value decomposition (SVD) analysis to a set of powers x(i)^(j-1).

Licensing:

The information on this web page is distributed under the MIT license.

Languages:

svd_powers is available in a MATLAB version and an Octave version and a Python version.

Related Data and Programs:

svd_circle, a Python code which analyzes a linear map of the unit circle caused by an arbitrary 2x2 matrix a, using the singular value decomposition (SVD).

svd_lls, a Python code which uses the singular value decomposition (SVD) to construct and plot the best affine and linear relationships in the sense of least square, between two vectors of data.

svd_snowfall, a Python code which reads a file containing historical snowfall data and analyzes the data with the Singular Value Decomposition (SVD).

svd_sphere, a Python code which analyzes a linear map of the unit sphere caused by an arbitrary 3x3 matrix A, using the singular value decomposition (SVD).

svd_test, a Python code which demonstrates the singular value decomposition (SVD) for a simple example.

svd_truncated_test, a Python code which demonstrates the computation of the reduced or truncated Singular Value Decomposition (SVD) that is useful for cases when one dimension of the matrix is much smaller than the other.

Reference:

Source Code:

Plots of the SVD vectors:

Plots of the projection of an arbitrary data vector onto the SVD vectors:


Last revised on 31 March 2022.