Hi, On Mon, Apr 2, 2012 at 5:38 PM, Val Kalatsky <kalat...@gmail.com> wrote: > Both results are correct. > There are 2 factors that make the results look different: > 1) The order: the 2nd eigenvector of the numpy solution corresponds to the > 1st eigenvector of your solution, > note that the vectors are written in columns. > 2) The phase: an eigenvector can be multiplied by an arbitrary phase factor > with absolute value = 1. > As you can see this factor is -1 for the 2nd eigenvector > and -0.99887305445887753-0.047461785427773337j for the other one.
Thanks for this answer; for my own benefit: Definition: A . v = L . v where A is the input matrix, L is an eigenvalue of A and v is an eigenvector of A. http://en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix In [63]: A = [[0.6+0.0j, -1.97537668-0.09386068j],[-1.97537668+0.09386068j, -0.6+0.0j]] In [64]: L, v = np.linalg.eig(A) In [66]: np.allclose(np.dot(A, v), L * v) Out[66]: True Best, Matthew _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
