[Rd] Problem using F77_CALL(dgemm) in a package

[Jason Rudy]

Thanks for the tip.  That API could make my work considerably easier.

Jason

On Tue, Feb 22, 2011 at 7:37 AM, Douglas Bates <bates at stat.wisc.edu> wrote:
> On Sun, Feb 20, 2011 at 4:39 PM, Jason Rudy <jcrudy at gmail.com> wrote:
>> I've never used C++ before, so for this project I think I will stick
>> with just using the BLAS and LAPACK routines directly.  Another issue
>> is that I will need to do some sparse matrix computations, for which I
>> am planning to use CSPARSE, at least to begin with.  I am interested
>> by RcppArmadillo, and would consider it for future projects.  If you
>> don't mind, what in your opinion are the major pros and cons of an
>> RcppArmadillo solution compared to simply using the BLAS or LAPACK
>> routines through the .C interface?
>
> You may want to consider the API exported by the Matrix package that
> allows access to CHOLMOD functions in that package's library.  The
> entire CSPARSE library is also included in the Matrix package but most
> of it is not exported because the CHOLMOD functions are generally more
> effective.  (Both CHOLMOD and CSPARSE are written by Tim Davis.
> CSPARSE is good code but it was written more for instructional purpose
> than as an "industrial strength" package.)
>
>> On Sun, Feb 20, 2011 at 8:11 AM, Dirk Eddelbuettel <edd at debian.org> wrote:
>>>
>>> On 20 February 2011 at 09:50, Dirk Eddelbuettel wrote:
>>> | There is of course merit in working through the barebones API but in case you
>>> | would consider a higher-level alternative, consider these few lines based on
>>> | RcppArmadillo (which end up calling dgemm() for you via R's linkage to the BLAS)
>>>
>>> PS I always forget that we have direct support in Rcpp::as<> for Armadillo
>>> matrices. The examples reduces to three lines in C++, and you never need to
>>> worry about row or column dimension, or memory allocation or deallocation:
>>>
>>>  R> suppressMessages(library(inline))
>>>  R> txt <- '
>>>  +    arma::mat Am = Rcpp::as< arma::mat >(A);
>>>  +    arma::mat Bm = Rcpp::as< arma::mat >(B);
>>>  +    return Rcpp::wrap( Am * Bm );
>>>  +    '
>>>  R> mmult <- cxxfunction(signature(A="numeric", B="numeric"),
>>>  +                      body=txt,
>>>  +                      plugin="RcppArmadillo")
>>>  R> A <- matrix(1:9, 3, 3)
>>>  R> B <- matrix(9:1, 3, 3)
>>>  R> C <- mmult(A, B)
>>>  R> print(C)
>>>       [,1] [,2] [,3]
>>>  [1,]   90   54   18
>>>  [2,]  114   69   24
>>>  [3,]  138   84   30
>>>  R>
>>>
>>> Matrices A and B from directly initialise Armadillo matrices, and the result
>>> can be returned directly.
>>>
>>> Hth, Dirk
>>>
>>> --
>>> Dirk Eddelbuettel | edd at debian.org | http://dirk.eddelbuettel.com
>>>
>>
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>