similar to: EISPACK symmetric matrix eigenvalue routines

I have been looking at the "eigen" function and have reintroduced the ability to compute (right) eigenvalues and vectors for non-symmetric matrices. I've also made "eigen" complex capable. The code is based on the eispack entry points RS, RG, CH, CG (which is what S appears to use too). The problem with both the S and R implementations is that they consume huge amounts

Hi all, Recently our statistics students noticed that their Gibbs samplers were crashing due to some NaNs in some parameters. The NaNs came from mvrnorm (Ripley & Venables' MASS package multivariate normal sampling function) and with some more investigation it turned out that they were generated by function eigen, the eigenvalue computing function. The problem did not seem to happen

Dear all, I am currently working on the calculation of eigenvalues (and -vectors) of large matrices. Since these are mostly sparse matrices and I remember some specific functionalities in MATLAB for sparse matrices, I started a research how to optimize the calculation of eigenvalues of a sparse matrix. The function eigen itself works with the LAPACK library which has no special handling for

Hi, I am using eigen to get an eigen decomposition of a square, symmetric matrix. For some reason, I am getting a column in my eigen vectors (the 52nd column out of 601) that is a column of all NAs. I am using the option, symmetric=T for eigen. I just discovered that I do not get this behavior when I use the option EISPACK=T. With EISPACK=T, the 52nd eigenvector is (up to rounding error) a

Dear subscribers of R-devel I am experiencing that R crashes (further details are given below) in some complex matrix calculations when EISPACK=TRUE has been specified in eigen(). I discovered the behaviour some months ago just after the release of R-2.2.0, and it has been lying on my desk since. I apologise for not having nailed the problem down to a simple function call, but I thought I

Hi all, Hi, When I want to compute the eigenvalues & eigenvectors of a specific matrix, R crashes (i.e. it stops responding to any input). I've tried it with different versions of R (2.1.1, 2.2.0, 2.2.1) - all with crashing as result. What I did before the crash was: M <- as.matrix(read.table("thematrix",header=T)) eigen(M) If, instead of eigen(M), I use eigen(M,

Dear all I am so glad the R can provide the efficient calculate about eigenvector and eigenvalue. However, i have some puzzle about the procedure of eigen. Fristly, what kind of procedue does the R utilize such that the eigen are obtained? For example, A=matrix(c(1,2,4,3),2,2) we can define the eigenvalue lamda, such as det | 1-lamda 4 | =0 | 2 3-lamda | then

I'm using R version 2.0.1 on a Windows 2000 operating system. Here is some actual code I executed: > test [,1] [,2] [1,] 1000 500 [2,] 500 250 > eigen(test, symmetric=T)$values [1] 1.250000e+03 -3.153033e-15 > eigen(test, symmetric=T)$values[2] >= 0 [1] FALSE > eigen(test, symmetric=T, only.values=T)$values [1] 1250 0 > eigen(test, symmetric=T,

Hi, It is my understanding that the eigenvectors of a circulant matrix are given as follows: 1,omega,omega^2,....,omega^{p-1} where the matrix has dimension given by p x p and omega is one of p complex roots of unity. (See Bellman for an excellent discussion on this). The matrix created by the attached row and obtained using the following commands indicates no imaginary parts for the

Hi all, when testing the new improvements in the new 1.7.0-version I stumbled over the following: >eigen(matrix(c(0,.3,2,.9),2,2)) Error in eigen(matrix(c(0,.3,2,.9),2,2)) : LAPACK routine DGEEV gave error code -13 >eigen(matrix(c(0,.3,2,.9),2,2),EISPACK=TRUE) $values [1] 1.3458236 -0.4458236 $vectors [,1] [,2] [1,] -1.1436890 -0.9760443 [2,] -0.7696018

Dear all, I encounter some covariance matrix with quite small eigenvalues (around 1e-18), which are smaller than the machine precision. The dimension of my matrix is 17. Here I just fake some small matrix for illustration. a<-diag(c(rep(3,4),1e-18)) # a matrix with small eigenvalues b<-matrix(1:25,ncol=5) # define b to get an orthogonal matrix b<-b+t(b) bb<-eigen(b,symmetric=T)

It was a real surprise, but a student in my class found that the function eigen is buggy. He traced to the problem from his inability of getting principal component analysis to work on his data. Chong Gu Here is a matrix I generated through X'X, where X is 2x3. > jj [,1] [,2] [,3] [1,] 0.8288469 -1.269783 -0.7533517 [2,] -1.2697829 2.162132 2.0262917 [3,]

Batch: Putting q(save=F) at the end of my file does not work in my context because I can no longer source the file without quitting. I have that quit statement in my .First so that I always quit that way interactively. The problem is that it is ignored in batch. eigen: The crash occurs on my 586 running Red Hat Linux 2.0.27 but not on my son's 486 running SLackware Linix 2.0.29. We both

Hi, Given a positive integer N, and a real number \lambda such that 0 < \lambda < 1, I would like to generate an N by N stochastic matrix (a matrix with all the rows summing to 1), such that it has the second largest eigenvalue equal to \lambda (Note: the dominant eigenvalue of a stochastic matrix is 1). I don't care what the other eigenvalues are. The second eigenvalue is

I'm trying to test if a correlation matrix is positive semidefinite. My understanding is that a matrix is positive semidefinite if it is Hermitian and all its eigenvalues are positive. The values in my correlation matrix are real and the layout means that it is symmetric. This seems to satisfy the Hermitian criterion so I figure that my real challenge is to check if the eigenvalues are all

Hi, If I use the eigen() function to find the eigenvalues of a matrix, how can I find the eigenvector corresponding to the largest eigen value? Thanks! [[alternative HTML version deleted]]

This thread reveals that R has some holes in the solution of some of the linear algebra problems that may arise. It looks like Jim Ramsay used a quick and dirty approach to the generalized eigenproblem by using B^(-1) %*% A, which is usually not too successful due to issues with condition of B and making a symmetric/Hermitian problem unsymmetric. In short, the problem is stated as follows:

Dear R users, even if this question is not related to an issue about R, probably some of you will be able to help me. I have a square matrix of dimension k by k with alpha on the diagonal and beta everywhee else. This symmetric matrix is called symmetric compound matrix and has the form a( I + cJ), where I is the k by k identity matrix J is the k by k matrix of all ones a = alpha - beta c =

DeaR list, I happily use eigen() to compute the eigenvalues and eigenvectors of a fairly large matrix (200x200, say), but it seems over-killed as its rank is limited to typically 2 or 3. I sort of remember being taught that numerical techniques can find iteratively decreasing eigenvalues and corresponding orthogonal eigenvectors, which would provide a nice alternative (once I have the

Folks: I'm trying to port some code from python over to R, and I'm running into a wall finding R code that can solve a generalized eigenvalue problem following this function model: http://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.eig.html Any ideas? I don't want to call python from within R for various reasons, I'd prefer a "native" R solution if one