search for: svd

Lately I'm getting this error quite a bit: Error in La.svd(x, nu, nv) : error code 1 from Lapack routine 'dgesdd' I'm running R 2.5.0 on a 64 bit Intel machine running Fedora (8 I think). Maybe the 64 bit platform is more fragile about declaring convergence. I'm seeing way more of these errors than I ever have before. From R-Help I see...

Hello, I have a matrix equation, Ax=b, that I need to solve for x. x should be a vector of positive numbers (between 0 and 1). A is not a square matrix in general. This lead me to using the SVD. However, using the SVD gives me positive and negative numbers, as well. I have some constraints included in the A matrix itself (i.e., that the sum of some xi should be equal to 1) but I do not know how to include the constraint that each xi should be non-negative. Is there in R (or somewhere els...

Hi, I'm trying to do PCA on a n by p wide matrix (n < p), and I'd like to get more principal components than there are rows. However, svd() only returns a V matrix of with n columns (instead of p) unless the argument nv=p is set (prcomp calls svd without setting it). Moreover, the eigenvalues returned are always min(n, p) instead of p, even if nv is set: > x <- matrix(rnorm(15), 3, 5) > dim(svd(x)$v) [1] 5 3 > leng...

Dear R list users, the singluar value decomposition of a symmetric matrix M is UDV^(T), where U = V. La.svd(M) gives as output three elements: the diagonal of D and the two orthogonal matrices u and vt (which is already the transpose of v). I noticed that the transpose of vt is not exactly u. Why is that? thank you for your attention and your help Stefano AVVISO IMPORTANTE: Questo messaggio di posta e...

--=====================_24736660==_ Content-Type: text/plain; charset="iso-8859-1"; format=flowed Content-Transfer-Encoding: quoted-printable SVD-Error on R 1.1.0 Windows 98 I get the following error applying svd on a positive definite matrix : > sk2 [,1] [,2] [,3] [,4] [,5] [1,] 1.0460139783 0.084356992 -2.810553e-04 -1.303237e-03 2.011809e-03 [2,] 0.0843569924 1.154650879 -5....

...? The angle is all that is really important, I'd like 1,1,1,2 to be basically the same as 2,2,2,4, perhaps with that the latter having more weight in resolving disrepencies. Next is the job of reducing the matrix from 500 dimensions to say 10. I think the correct way of doing this is using SVD, does that sound right? At least, I have read a paper by Schuetze which used SVD. Other algorithms (K-means, SOM) also sound applicable but may balk at the amount of data, or might not provide the distance property I'm trying to get. However I must be doing something stupid here because th...

...e the data matrix X is zero-meaned, and I used the following procedures: C = covriance(X) %% calculate the covariance matrix; [EVector,EValues]=eig(C) %% L = diag(EValues) %%L is a column vector with eigenvalues as the elements percent = L(1)/sum(L); Others argue using Sigular Value Decomposition(SVD) to calculate the same quantity, as: [U,S,V]=svd(X); L = diag(S); L = L.^2; percent = L(1)/sum(L); So which way is the correct method to calculate the percentage explained by the first principal component? Thanks for your advices on this. Fred

(I tried to send this earlier, but it doesnt seem to have come through, due to problems on my system) Hola: Both cannot be correct: > m <- matrix(1:4, 2) > svd(m) $d [1] 5.4649857 0.3659662 $u [,1] [,2] [1,] -0.5760484 -0.8174156 [2,] -0.8174156 0.5760484 $v [,1] [,2] [1,] -0.4045536 0.9145143 [2,] -0.9145143 -0.4045536 > La.svd(m) $d [1] 1.126182e-301 4.226051e-314 $u [,1] [,2] [1,] 0.0021125...

Hello! I suppose this is more a matrix theory question than a question on R, but I will give it a try... I am using La.svd to compute the singular value decomposition (SVD) of a variance matrix, i.e., a symmetric nonnegative definite square matrix. Let S be my variance matrix, and S = U D V' be its SVD. In my numerical experiments I always got U = V. Is this necessarily the case? Or I might eventually run into a SV...

I am trying to perform Singular Value Decomposition (SVD) on a Term Document Matrix I created using the 'tm' package. Eventually I want to do a Latent Semantic Analysis (LSA). There are 5677 documents with 771 terms (the DTM is 771 x 5677). When I try to do the SVD, it runs out of memory. I am using a 12GB Dual core Machine with Windows XP and...

Dear R-devel: I could use some advice about matrix calculations and steps that might make for faster computation of generalized inverses. It appears in some projects there is a bottleneck at the use of svd in calculation of generalized inverses. Here's some Rprof output I need to understand. > summaryRprof("Amelia.out") $by.self self.time self.pct total.time total.pct "La.svd" 150.34 27.66 164.82 30.32 &quot...

Hi, I'm just wondering if it is planned to include the Lapack routine DGESDD (and friends) in R-1.4.0? This is faster (supposedly by a factor of ~6 for large matrices) than DGESVD which is currently (R-1.3.1) called by La.svd. And if it is not in the plans yet, is there a chance it could be? I've added it to my local version of R-1.3.1 and so far see a factor of 4 improvement over La.svd and a factor of 3 over svd (see output below). I'd be glad to hand off what I...

Per the discussion about the problems with prcomp() when n << p, which boils down to a problem with svd() when n << p, here is a patch to prcomp() which substitutes La.svd() instead of svd(). -Greg (This is really a feature enhancement, but submitted to R-bugs to make sure it doesn't get lost. ) *** R-1.6.0/src/library/mva/R/prcomp.R Mon Aug 13 17:41:50 2001 --- R-1.6.0-GRW//src/library...

After fixing princomp(), recently, {tiny negative eigen-values are possible for non-negative definite matrices} Fritz Leisch drew my attention to the fact the not only eigen() can be funny, but also svd(). Adrian Trappleti found that the singular values returned can be "-0" instead of "0". This will be a problem in something like sd <- svd(Mat) $ d max(sd) / min(sd) which gives -Inf instead of Inf in the above case. I did some more searching and testing with...

Hello List, i need help for eigen and svd functions. I have a non-symmetric square matrix. These matrix is not positive (some eigenvalues are negative). I want to diagonalise these matrix. So, I use svd and eigen and i compare the results. eigen give me the "good" eigenvalues (positive and negative). I compare with another so...

Dear All, I need to perform a SVD on a very large data matrix, of dimension ~ 500,000 x 1,000 , and I am looking for an efficient algorithm that can perform an approximate (partial) SVD to extract on the order of the top 50 right and left singular vectors. Would be very grateful for any advice on what R-packages are available to p...

...th many contributors. Type `contributors()' for more information. Type `demo()' for some demos, `help()' for on-line help, or `help.start()' for a HTML browser interface to help. Type `q()' to quit R. > set.seed(1) > x <- matrix(runif(1e4), 1e2, 1e2) > invisible(La.svd(x)) # This ran fine. > x <- matrix(runif(1e5), 1e3, 1e2) > invisible(La.svd(x)) (Dr. Watson says there's an access violation.) So far La.svd is the only thing I know of that generates Dr. Watson. The version info: _ platfor...

Dear R-helpers, could anybody explain me briefly what is the difference between eigenvectors returned by 'eigen' and 'svd' functions and how they are related? Thanks in advance Ondrej Mikula

Hi, I met a probem recently and need your help. I would really appreciate it. I kept receiving the following error message when running a program: 'Error in svd(X) : infinite or missing values in x'. However, I did not use any svd function in this program though I did include the function pseudoinverse. Is the problem caused by doing pseudoinverse? Best regards, Tongtong

...put it back, I don't get the original hermitian matrix. I am having the same problem with spectral value decomposition as well. I am using R 1.7.0 on Windows. Here is my code: X <- matrix(rnorm(16)+1i*rnorm(16),4) X <- X + t(X) X[upper.tri(X)] <- Conj(X[upper.tri(X)]) Y <- La.svd(X) Y$u %*% diag(Y$d) %*% t(Y$v) # this doesn't give back X Y$u %*% diag(Y$d) %*% Y$v # this works fine. Z <- La.eigen(X) # the eigen values should be real, but are not. Z$vec %*% diag(Z$val) %*% t(Z$vec) # this doesn't give back X The help for "La.svd" says that the...