search for: eigendecomposition

...t.or.vec(n,n) for(i in 1:n) { D[i,i]<-1/V[i] } P<-Q*D*R ## P should be the inverse of the matrix m. Check using solve(m) ## solve(m) does not equal P? Any ideas of what I am missing/not understanding? -- View this message in context: http://r.789695.n4.nabble.com/Inverse-matrix-using-eigendecomposition-tp4188673p4188673.html Sent from the R help mailing list archive at Nabble.com.

...infomatrix_inv = solve(infomatrix, tol = 10^(-43)) does not deliver adequate results (matrixproduct of infomatrix_inv and infomatrix is not 1). Regular use of solve() delivers the error 'system is computationally singular: reciprocal condition number: 2.949.10^(-41)' So I went over to an eigendecomposition using eigen(): (so that infomatrix = V D V^(-1) ==> infomatrix^(-1)= V D^(-1) V^(-1) ) in the hope this would deliver better results.) *********************** > infomatrix_eigen = eigen(infomatrix) > infomatrix_eigen_D = diag(infomatrix_eigen$values) > infomatrix_eigen_V = infomatrix_e...

...ctors 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 exported in R_ext/Lapack.h? my superficial understanding is that dsyevr is "better" (faster? stabler?) for both complete and partial eigenproblems than dsyevd/dsyevx, but only the complete eigenproblem interface to dsyevr appears to be exported in Lapack.h (as dsyev)....

...ctors 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 exported in R_ext/Lapack.h? my superficial understanding is that dsyevr is "better" (faster? stabler?) for both complete and partial eigenproblems than dsyevd/dsyevx, but only the complete eigenproblem interface to dsyevr appears to be exported in Lapack.h (as dsyev)....

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

...to a 2D plane. That's why we are taking the 1st and 2nd element of the `y[1, ]` vector. However what I don't understand is: Shouldn't the 1st eigenvector direction be the vector denoted by `y[, 1]`, instead of `y[1, ]`? (Again, here `y` is the eigenvector matrix, obtained by PCA or by eigendecomposition of `t(x) %*% x`.) i.e. the eigenvectors should be column vectors, not those horizontal vectors. Even though we are plotting them on 2D plane, we should draw the 1st direction to be from `(0, 0)` pointing to `(y[1, 1], y[2, 1])`? I think the R code biplot.princomp has a bug: the loading matrix (...

...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...