similar to: one thing to check

Ordinary functions should not alter their inputs but in R-2.15.2 svd(LINPACK=TRUE,X) does. (It worked in 2.15.0 but not in 2.15.1 or 2.15.2 and became deprecated in 2.15.2.) > X <- matrix(c(1,2,3, 5,7,11, 13,17,19), 3, 3) > X [,1] [,2] [,3] [1,] 1 5 13 [2,] 2 7 17 [3,] 3 11 19 > svd(X, LINPACK=TRUE)$d [1] 31.9718214 2.3882717 0.3143114 Warning message:

Hello, Like QR decomposition, I am looking for decomposition to get U and V matrix of SVD decomposition (according to LINPACK, X = UDV'). Do you know if there is a function which could calculate this decomposition? Look forward to your reply, Haleh

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 that this issue comes up from time to

I remember there was advice about a server that one could use for reverse dependency checks, but I forgot to write it down.? (Or I did save the info and forgot where I saved it...)?? I have been doing the checks for survival myself, but the count is getting out of hand (663, not counting bioconductor). Any pointers? Terry Therneau [[alternative HTML version deleted]]

I am a relative newcomer to both the R and C/C++ software worlds -- I'm taking a C Programming class currently. I noticed the other day that the C:\Program Files\R1_8_1\src\include\R_ext directory on my WinXP box has the header files BLAS.h Lapack.h Linpack.h RLapack.h I am interested in (perhaps) using one or more of these header files in a straight C program I'm working on in Visual

I use R Version 0.60 Alpha (September 18, 1997) on a Linux Pentium (Debian 1.3) and on a Sparc-Sun-Solaris 2.5.=20 R> svd(matrix(1:16,4,4)) =09yields on both machines Error: error 4 in dsvdc R> svd(matrix(1:20,4,5)) =09gives a result on the Linux computer $d [1] 0 0 0 NA $u [,1] [,2] [,3] [,4] [1,] 1 0 0 0 [2,] 0 1 0 0 [3,] 0 0 1 0 [4,] 0

Dear developers, I am trying to reproduce the results of applying opt -O3 to a source file in the form of LLVM IR. I want to get the same IR by manually ordering the passes used by O3 and passing them to opt. To illustrate what I am doing on an example, as an input I use linpack benchmark from the LLVM test suite[1]: 1. First I produce the intermediate representation using clang: clang -O3

--=====================_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

I think this has to do with how the pass manager is populated when we give -O3 vs when we give particular pass names. Some passes have multiple createXYZPass() methods that accept arguments too. These methods call non-default pass constructors, which in turn cause the passes to behave in a different manner. eg: Pass *llvm::createLICMPass() { return new LegacyLICMPass(); } Pass

Hi, is there support (i.e. packages or similar) in R for functional data analysis as suggested by Ramsay (1982) and Besse and Ramsay (1986)? Cheers Roland -- E-Mail : Roland.Goecke at ieee.org -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or

Dear R Developers, I should have had the Design library running in R by now but have kept putting off changing some calls to LINPACK routines to use those builtin to R. Specifically I call dqrsl1 and dqr. Would it be an easy task to put those in the next release of R? If not I'll finally bite the bullet and get back into reading LINPACK documentation (which I have but haven't examined

The first one failed on a diff: ******************** TEST (simple) 'sse.expandfft' FAILED! ******************** Execution Context Diff: /home/rkotler/llvmpb3/build/projects/test-suite/tools/fpcmp: Compared: 1.139094e-07 and 1.159249e-07 abs. diff = 2.015500e-09 rel.diff = 1.738626e-02 Out of tolerance: rel/abs: 1.600000e-02/0.000000e+00 ******************** TEST (simple)

I'm getting three failures. TEST-FAIL: exec /home/rkotler/llvmpb3/build/projects/test-suite/SingleSource/UnitTests/Vector/SSE/sse.expandfft TEST-RESULT-exec-time: user 0.3200 TEST-RESULT-exec-real-time: real 0.3172 TEST-FAIL: exec /home/rkotler/llvmpb3/build/projects/test-suite/SingleSource/UnitTests/Vector/SSE/sse.stepfft TEST-RESULT-exec-time: user 0.4000

man for `qr` says that the function uses LINPACK's DQRDC, while it in fact uses DQRDC2. ``` The QR decomposition of the matrix as computed by LINPACK or LAPACK. The components in the returned value correspond directly to the values returned by DQRDC/DGEQP3/ZGEQP3 ```

Hello. I am trying to invert a matrix, and I am finding that I can get different answers depending on whether I set LAPACK true or false using "qr". I had understood that LAPACK is, in general more robust and faster than LINPACK, so I am confused as to why I am getting what seems to be invalid answers. The matrix is ostensibly the Hessian for a function I am optimizing. I want to get

I'm probably misunderstanding something in "Writing R Extensions" version 1.0.0. In the chapter on the R API, section 4.7, it is stated that the functions listed in R_ext/Linpack.h are available to users' Fortran code. I am developing a developing a library of ode solvers, based on lsoda and ddassl, and which in turn call some routines from linpack and double precision blas. I

I forgot to mention that you can also run "make TEST=simple report" which will generate a nice report. Do you know why these tests fail ? You can step into the test directory and run 'make TEST=simple' from there. It will save you some time. On Dec 12, 2012, at 4:04 PM, reed kotler <rkotler at mips.com> wrote: > I'm getting three failures. > > TEST-FAIL:

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

when I create the report, there are no failures in it. so maybe these are being filtered for known failures. On 12/12/2012 05:03 PM, reed kotler wrote: > The first one failed on a diff: > ******************** TEST (simple) 'sse.expandfft' FAILED! > ******************** > Execution Context Diff: > /home/rkotler/llvmpb3/build/projects/test-suite/tools/fpcmp: Compared: >

I use the 'make TEST=simple' as a pre-commit test. I think that everybody should run these tests before committing to LLVM. On Dec 12, 2012, at 5:06 PM, reed kotler <rkotler at mips.com> wrote: > when I create the report, there are no failures in it. so maybe these are being filtered for known failures. > > On 12/12/2012 05:03 PM, reed kotler wrote: >> The first