eigenvalue

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

Until now I used numpy.linalg.eigvals to calculate the eigenvalues of quadratic matrices with at least 1000 rows/columns and, for most cases, about a fifth of its entries non-zero (I don't know if that should be considered a sparse matrix). I found another topic indicating that scipy can possibly do a better job. However, since I have to calculate the eigenvalues for hundreds of thousands of large matrices of increasing size (possibly up to 20000 rows/columns and yes, I need ALL of their eigenvalues), this will always take awfully long. If I can speed things up, even just the tiniest bit, it

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,

Is there an distinct and effective way of finding eigenvalues and eigenvectors of a real, symmetrical, very large, let's say 10000x10000, sparse matrix in Eigen3 ? There is an eigenvalue solver for dense matrices but that doesn't make use of the property of the matrix e.g. it's symmetry. Furthermore I don't want to store the matrix in dense. Or (alternative) is there a better (+better documented) library to do that? Armadillo will do this using eigs_sym Note that computing all the eigenvalues is a very expensive operation whatever you do, usually what is done is to find only the k largest, or

Here's my code: def topK(dataMat,sensitivity): meanVals = np.mean(dataMat, axis=0) meanRemoved = dataMat - meanVals covMat = np.cov(meanRemoved, rowvar=0) eigVals,eigVects = np.linalg.eig(np.mat(covMat)) I get the error in the title on the last line above. I suspect has something to do with the datatype, so, here's an image of the variable and datatype from the Variable Explorer in Spyder: I've tried changing np.linalg.eig(np.mat(covMat)) to np.linalg.eig(np.array(np.mat(covMat))) and to np.linalg.eig(np.array(covMat)) , nothing works. Any ideas? (an example would be great!) Your array has a