similar to: Eigenvalue calculation of sparse matrices

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

Dear all, I want to compute the eigenvalues of a complex matrix for some statistics. Comparing it to its matlab/octave sibling, I don't get the same eigenvalues in R computing it from the exact same matrix. In R, I used eigen() and arpack() that give different eigenvalues. In matlab/octave I used eig() and eigs() that give out the same eigenvalues but different to the R ones. For real

Can someone confirm that the EISPACK routines for eigenvalues of symmetric matrix are in base R. They seem to be, but I can't seem to locate where they are in the src tree. Thanks. Chong Gu -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or

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

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

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

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, 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

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

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

Hello, I am trying to construct two matrices, F and V, composed of partial derivatives and then find the eigenvalues of F*Inverse(V). I have the following equations in ryacas notation: > library(Ryacas) > FIh <- Expr("betah*Sh*Iv") > FIv <- Expr("betav*Sv*Ih") > VIh <- Expr("(muh + gamma)*Ih") > VIv <- Expr("muv*Iv") I

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]]

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

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

Some of you may have seen a message on s-news by Stefan Steinhaus regarding his paper on "Comparison of mathematical programs for data analysis". He compares S-PLUS 4.5 with several other programs. He does not include R in the comparisons. On p. 28 of his report he gives the URL the Auckland site along with URL's for two other systems but comments that "I didn't received

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)

Hi, I am having trouble with some code that I am dyn.loading. I am writing an interface to ARPACK. I compile my interface (dssimp.cc), and link it against the ARPACK library (libarpack_SUN4.a): g++ -shared -static -fPIC dssimp.cc -o dssimp.so -larpack_SUN4 -lg2c -lm I can dyn.load the code and it appears OK. However, when I call my function, the call to the function in the ARPACK library