similar to: NaN and linear algebra

No, blas/veclib is tested, so aprt this extreme case you should report some other more commonly used cases in which something fails on OS X. This will help us to work it out. As said, I'll try some tests without using veclib and let you know. I've fowarded this mail to r-devel, which seems to be the right place, so for future msg on the subject please use r-devel. stefano On

Hello, Currently, determinant(A) calculates the determinant of 'A' by factorizing A=LU and computing prod(diag(U)) [or the logarithm of the absolute value]. The factorization is done by LAPACK routine DGETRF, which gives a status code INFO, documented [1] as follows: *> INFO is INTEGER *> = 0: successful exit *> < 0: if INFO = -i, the i-th

In my package 'glmmML' I'm using old C code and linpack in the optimizing procedure. Specifically, one part of the code looks like this: F77_CALL(dpoco)(*hessian, &bdim, &bdim, &rcond, work, info); if (*info == 0){ F77_CALL(dpodi)(*hessian, &bdim, &bdim, det, &job); ........ This usually works OK, but with an ill-conditioned data

r-devel list, I have recently written an R package that solves a linear least squares problem, and computes the multivariate normal density function. The bulk of the code is written in C, with interfacing code to the BLAS and Lapack libraries. The motivation here is speed. I ran into a problem computing the determinant of a symmetric matrix in packed storage. Apparently, there are no explicit

From the R Installation and Admin manual: "There is a tendency for re-distributors of R to use ?enhanced? linear algebra libraries without explaining their downsides.? There?s a downside not mentioned in the manual that caught and baffled me for a while. I was using all 64 cores of an AWS instance via parallel::mclapply() and doing matrix multiplications in the parallelized function. If

> Do you have R code (including set.seed(.) if relevant) to show on how to generate > the large square matrices you've mentioned in the beginning? So we get to some > reproducible benchmarks? Hi Martin, Here is the program I used. I only generate 2 random numbers and reuse them to make the benchmark run faster. Let me know if there is something I can do to help--alternate

I am using R to multiply some large (30k x 30k double) matrices on a 64 core machine (xeon phi). I added some timers to src/main/array.c to see where the time is going. All of the time is being spent in the matprod function, most of that time is spent in dgemm. 15 seconds is in matprod in some code that is checking if there are NaNs. > system.time (C <- B %*% A) nancheck: wall time

Le 17/02/2020 ? 17:50, Tomas Kalibera a ?crit?: > On 2/17/20 5:36 PM, Serguei Sokol wrote: >> Hi, >> >> A colleague of mine has spotted me a passage of the doc ?option >> talking about Inf and NaN check in 'matprod=default' section: >> https://stat.ethz.ch/R-manual/R-devel/library/base/html/options.html >> >> I am wondering if NA should be

> > On 9 Feb 2017, at 16:00, G?ran Brostr?m <goran.brostrom at umu.se> wrote: > > > > In my package 'glmmML' I'm using old C code and linpack in the optimizing procedure. Specifically, one part of the code looks like this: > > > > F77_CALL(dpoco)(*hessian, &bdim, &bdim, &rcond, work, info); > > if (*info == 0){ > >

Greetings: We are evaluating the performance of R matrix algebra es as we port a MATLAB R14 script into R. The MATLAB code basically evaluates the AX=B system on sparse matrices that result in output matrices of 100 to 1,000,000 rows/columns. Our R prototype script uses spase base matrices and the methods qr() and qr.coeff(). The following statements are called inside a doubly-nested loop: G

I am working on Johansen cointegration test, using urca and var package. in the selection of var, I have got following results. >VARselect(newd, lag.max = 10,type = "none") $selection AIC(n) HQ(n) SC(n) FPE(n) 6 6 6 5 $criteria 1 2 3 4 5 6 7 8 9 AIC(n) -3.818646e+01 -3.864064e+01

Greetings; I've posted the following to R's bug tracking system (at https://bugs.r-project.org/bugzilla/show_bug.cgi?id=17210 ) and Martin Maechler requested that I post to this list as well. If I start R from the command line with --vanilla, then repeatedly execute the following line: det(matrix(nrow=10,ncol=10)) ... I eventually get a crash, with error: *** Error in

Hi Tomas, Can you share the full code for your benchmark, compiler options, and performance results so that I can try to reproduce them? There are a lot of variables that can affect the results. Private email is fine if it is too much for the mailing list. I am measuring on Knight's Landing (KNL) that was released in November. KNL is not a co-processor so no offload is necessary. R executes

thank you for your valuable reply. I have attached my commands, results, and data with this mail..maybe it will be beneficial for you to feedback. On Tue, Nov 21, 2017 at 9:13 PM, Jeff Newmiller <jdnewmil at dcn.davis.ca.us> wrote: > Your example is incomplete... as the bottom of this and every post says, > we need to be able to proceed from an empty R environment to wherever you

Hi all, im triyng to implement a bayesian model with R and c++. I have a strange problem. I can't reproduce the error with a small script and then i post the original one. The problem is after the line for(MCMC_iter2=0;MCMC_iter2<thin;MCMC_iter2++) For the first 34 iterations all work fine, after, all the simulations of mu_acc_P return an "nan". If i delete the line

Your example is incomplete... as the bottom of this and every post says, we need to be able to proceed from an empty R environment to wherever you are having the problem (reproducible), in as few steps as possible (minimal). The example needs to include data, preferably in R syntax as the dput function creates... see the howtos referenced below for help with that. [1], [2], [3] You also need to

I'd like to use some matrix algebra in my c code that is called from R. I need matrix multiplication, transposition, and Cholesky decomposition. I haven't come across any easy way to do this, but from browsing the web and R-devel a few options come to mind: 1. use F77_CALL() to call matrix multiplication functions from blas. This should be ok as long as I remember that c and FORTRAN store

I am debugging some code and found a function that returns and error most of the time. I have issolated the problem to when it raises an argument to the 1/3 power but for the life of me I can not figure out why it is not working. i have gone through the FAQs and have found nothing (not to say I might have missed something). I am highly embarrased with my inability find the problem (my face is

> "There?s a downside not mentioned in the manual that caught and baffled me for a while. I was using all 64 cores of an AWS instance via parallel::mclapply() and doing matrix multiplications in the parallelized function. If the matrices were big enough the linked BLAS or LAPACK would try to use all 64 cores for each multiplication, which meant 64^2 processes or threads in some combination

I also have this questions for this. I wonder if R initiates a system environment or options to instruct the packages on the number of cores to use? It doesn't have to be mandatory for now, but at least package maintainers can have shared consensus and start to adopt this humble settings rather than abusing parallel::detectCores() to max out the number of threads by default. > On Aug 7,