eigenvector

First, don't be fooled by the long post, there is not a lot of code just an observation of results so there are few example matrices. This is a bit related to this question: Matlab Codegen Eig Function - Is this a Bug? I know that mex/C/C++ translated eig() function may not return the same eigenvectors when using the same function in MATLAB and that's fine, but i am puzzled with results I'm getting. First this simple example: Output % c = diagonal matrix of eigenvalues % b = matrix whose columns are the corresponding right eigenvectors function [ b, c ] = eig_test(a) [b, c] = eig(a); end

I am trying to find a program in C code that will allow me to compute a eigenvalue (spectral) decomposition for a square matrix. I am specifically trying to find code where the highest eigenvalue (and therefore its associated eigenvalue) are located int the first column. The reason I need the output to be in this order is because I am trying to compute eigenvector centrality and therefore I only really need to calculate the eigenvector associated with the highest eigenvalue. Thanks in advance! In any case I would recommend to use a dedicated linear algebra package like Lapack (Fortran but can

Scipy and Numpy returns eigenvectors normalized. I am trying to use the vectors for a physical application and I need them to not be normalized. For example a = np.matrix('-3, 2; -1, 0') W,V = spl.eig(a) scipy returns eigenvalues (W) of [-2,-1] and the modal matrix (V) (eigenvalues as columns) [[ 0.89442719 0.70710678][ 0.4472136 0.70710678]] I need the original modal matrix [[2 1][1 1]] According to various related threads (1) (2) (3) , there is no such thing as a "non normalized" eigenvector. Indeed, an eigenvector v corresponding to the eigenvalue l of the matrix A is defined by, A*v = l*v

Following up on this question about how to find the Markov steady state, I'm now running into the problem that it works perfectly on my lab computer, but it doesn't work on any other computer. Specifically, it always finds the correct number of near-one eigenvalues, and thus which nodes are attractor nodes, but it doesn't consistently find all of them and they aren't grouped properly. For example, using the 64x64 transition matrix below, the computers in which it doesn't work it always produces one of three different wrong collections of attractors at random. On the smaller matrix M1 below,

I'm newbie in C++ programming , but I have a task to compute eigenvalues and eigenvectors (standard eigenproblem Ax=lx ) for symmetric matrices (and hermitian)) for very large matrix of size: binomial(L,L/2) where L is about 18-22. Now I'm testing it on machine which has about 7.7 GB ram available, but at final I'll have access to PC with 64GB RAM. I've started with Lapack++ . At the beginning my project assume to solve this problem only for symmetrical real matrices. This library was great. Very quick and small RAM consuming. It has an option to compute eigenvectors and place into input