![]() ![]() Scipy may have some alternative code to do what you want but it wasn't obvious to me if it does. The Scipy manual was not very helpful, it just lists the input and output for the lapack function. TimWescott at 17:12 I have already did this. Then instead of going by some dubious authority on the Internet, you're actually giving a mathematical proof. I know the eigenvalues are 1 and 2 because matrix A A is a triangular matrix. ![]() You would need to install Python, Scipy, and xlwings to run it. 1 I would suggest taking the FFT of sinusoids of various frequencies, and showing that the resulting FFT has a single bin with the same amplitude as the input sinusoid. Ask Question Asked 2 years, 5 months ago Modified 2 years, 5 months ago Viewed 348 times 2 We have a matrix A A as below 1 0 0 2 1 0 2 2 2. Let me know if you want a copy of the required VBA and Python code to run the function. MATLAB MATLAB IFFT doesnt match the analytical one. By default eig does not always return the eigenvalues and eigenvectors in sorted order. In the attached file, on sheet1, I have copied all the Scipy function results to column 2, and I have converted the function call to text in cell A1. Discussion and support for Matlab, Mathematica, Stata, Maple, Mathcad, LaTeX and more. Also the following results are approximately equal to the Matlab results, but diverge after the 3rd significant figure. I wrote a VBA routine to copy the upper triangle to the lower triangle, to ensure that they were symmetrical, and after doing that only the first result was negative. ![]() With the matrices taken from your files, and using the Scipy front-end to dsygv, the first two results are negative as you found. I called that from Excel with xlwings, using the two Excel files you posted in another forum (the ones you linked to here seem to be text files, and had some problems with the numbers being corrupted when I imported them). I had a look at this using the Python Scipy library, which includes a front end to the Lapack functions. Eigenvalues are a special set of scalars associated with a linear system of equations (i.e., a matrix equation) that are sometimes also known as characteristic roots, characteristic values (Hoffman and Kunze 1971), proper values, or latent roots (Marcus and Minc 1988, p. ![]()
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