search for: eigendecompositions

General goal: Write R code to find the inverse matrix of an nxn positive definite symmetric matrix. Use solve() to verify your code works. Started with a 3x3 matrix example to build the code, but something dosen't seem to be working. I just don't know where I am going wrong. ##Example matrix I found online A<-c(4,1,-1,1,2,1,-1,1,2) m<-matrix(A,nrow=3,ncol=3) ##Caculate the eigen

Hello I've stumbled upon a problem for inversion of a matrix with large values, and I haven't found a solution yet... I wondered if someone could give a hand. (It is about automatic optimisation of a calibration process, which involves the inverse of the information matrix) code: ********************* > macht=0.8698965 > coeff=1.106836*10^(-8) >

hello, i want to compute the top k eigenvalues+eigenvectors of a (large) real symmetric matrix. since it doesn't look like any top-level R function does this, i'll call LAPACK from a C shlib and then use .Call. the only LAPACK function i see to do this in R_ext/Lapack.h is dsyevx. however, i know that in LAPACK dsyevr can also return a partial eigendecomposition. why is dsyevr not

hello, i want to compute the top k eigenvalues+eigenvectors of a (large) real symmetric matrix. since it doesn't look like any top-level R function does this, i'll call LAPACK from a C shlib and then use .Call. the only LAPACK function i see to do this in R_ext/Lapack.h is dsyevx. however, i know that in LAPACK dsyevr can also return a partial eigendecomposition. why is dsyevr not

Hi, Suppose I have a matrix, A. Is there an easy way to find A^{n}? I mean, I can do something like: A %*% A %*% A %*% A for A^4, but if I want A^{10} it would be kind of annoying... Cheers, Kevin ------------------------------------------------------------------------------ Ko-Kang Kevin Wang Postgraduate PGDipSci Student Department of Statistics University of Auckland New Zealand

x=rmvnorm(2000, rep(0, 6), diag(c(5, rep(1,5)))) x=scale(x, center=T, scale=F) pc <- princomp(x) biplot(pc) There are a bunch of red arrows plotted, what do they mean? I knew that the first arrow labelled with "Var1" should be pointing the most varying direction of the data-set (if we think them as 2000 data points, each being a vector of size 6). I also read from

...eral reasons: 'External BLAS implementations often make less use of extended-precision floating-point registers and will almost certainly re-order computations. This can result in less accuracy than using the internal BLAS, and may result in different solutions, e.g. different signs in SVD and eigendecompositions.' https://cran.r-project.org/doc/manuals/r-devel/R-admin.html#BLAS I'm not sure how pervasive a problem this is, though. Keith On Sat, Dec 16, 2017 at 4:01 PM, Dirk Eddelbuettel <edd at debian.org> wrote: > > Kenny, > > On 17 December 2017 at 09:28, Kenny Bell wrote:...

...External BLAS implementations often make less use of extended-precision > floating-point registers and will almost certainly re-order computations. > This can result in less accuracy than using the internal BLAS, and may > result in different solutions, e.g. different signs in SVD and > eigendecompositions.' > > https://cran.r-project.org/doc/manuals/r-devel/R-admin.html#BLAS > > I'm not sure how pervasive a problem this is, though. > > Keith > > > On Sat, Dec 16, 2017 at 4:01 PM, Dirk Eddelbuettel <edd at debian.org> wrote: > >> >> Kenny, >&...

I have a problem with finding the inverse of a matrix. I have a square 9x9 matrix, A, and when I do solve(A) to find the inverse I get the following error message: Error in solve.default(A) : singular matrix `a' in solve Has anybody got any ideas as to why this is happening? Thanks Laura Gross _______________________________________________________________________ Never pay another

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

I'm simulating a Markov process using a vector of proportions. Each value in the vector represents the proportion of the population who are in a particular state (so the vector sums to 1). I have a square matrix of transition probabilities, and for each tick of the Markov clock the vector is multiplied by the transition matrix. To illustrate the sort of thing I mean: pm <-

Hello all- I'm having some problems with memory consumption under R. I've tried increasing the appropriate memory values, but it keeps asking for more; I've even upped the heap size to 600M, significantly eating into swap (256M real, 500+M swap). So, performance slows to a crawl. What I'm trying to do is run isoMDS on a 4000x4000 matrix. My first question is, how much memory

Hello: I modified benchmark used in http://www.informatik.uni-frankfurt.de/~stst/benchspl.txt now the attached code will work with R, except for last test for some reason (Sorry ran out of time to play with this). Immediate problem is that R does not show fraction of seconds. So, that kinda makes comparison some what hard. I also looked at memory and CPU usage on NT console. R seems to go

Hi R-devel list, OpenBLAS is readily available for unix-likes: https://cloud.r-project.org/web/packages/gcbd/vignettes/gcbd.pdf However, my questions are: 1) Would R-devel consider using OpenBLAS for the main distribution of R for all platforms including Windows? 2) If so, would R-devel set the default multi-thread level to the number of (real) cores on a machine? My sense is there're a

Dear all, I have a population with three age-classes, at time t=0 the population is: n.zero <- c(1,0,0) I have a transition matrix A which denotes "fertility" and "survival": A <- matrix(c(0,1,5, 0.3,0,0, 0,0.5,0), ncol=3, byrow=TRUE) To obtain the population at t=1, I calculate: A %*% n.zero To obtain the population t=2, I calculate: A %*% (A %*% n.zero) ... and so

...e sources from LAPACK version 3.11.0 (with changes only to double complex subroutines). * The included LAPACK sources have been updated to include the four Fortran 90 routines rather than their Fortran 77 predecessors. This may give some different signs in SVDs or eigendecompositions.. (This completes the transition to LAPACK 3.10.x begun in R 4.2.0.) * The LAPACK sources have been updated to version 3.11.0. (No new subroutines have been added, so this almost entirely bug fixes: Those fixes do affect some computations with NaNs, including R's...

...e sources from LAPACK version 3.11.0 (with changes only to double complex subroutines). * The included LAPACK sources have been updated to include the four Fortran 90 routines rather than their Fortran 77 predecessors. This may give some different signs in SVDs or eigendecompositions.. (This completes the transition to LAPACK 3.10.x begun in R 4.2.0.) * The LAPACK sources have been updated to version 3.11.0. (No new subroutines have been added, so this almost entirely bug fixes: Those fixes do affect some computations with NaNs, including R's...