[Rcpp-devel] Differences between RcppEigen and RcppArmadillo

[Douglas Bates]

On Wed, Jun 13, 2012 at 6:53 PM, Julian Smith <hilbertspaces at gmail.com> wrote:
> Doesn't svd in R by default compute D, U and V?

> http://stat.ethz.ch/R-manual/R-patched/library/base/html/svd.html

You're right but the default is the 'thin' U when X is n by p and n >=
p.  Does the svd in Armadillo return the full n by n matrix U?

Another thing to check is what underlying Lapack routine is called.
There are two: dgesvd, which is the older algorithm and the one with
the expected name if you know the Lapack conventions, and dgesdd which
is a newer and faster algorithm.  R uses dgesdd.

> On Wed, Jun 13, 2012 at 4:07 PM, Douglas Bates <bates at stat.wisc.edu> wrote:
>>
>> On Wed, Jun 13, 2012 at 5:16 PM, Dirk Eddelbuettel <edd at debian.org> wrote:
>> >
>> > On 13 June 2012 at 15:05, Julian Smith wrote:
>> > | I agree that RcppEigen is a little bit faster, but ease of use is
>> > important to
>> > | me, so I feel like RcppArmadillo might win out in my application.
>> >
>> > Yup, that my personal view too.
>> >
>> > | | RcppArmadillo will use the very same LAPACK and BLAS libs your R
>> > session
>> > | | uses. So MKL, OpenBlas, ... are all options.  Eigen actually has its
>> > own
>> > | code
>> > | | outperforming LAPACK, so it doesn't  as much there.
>> > |
>> > | Why do you think R outperforms RcppArmadillo in this example below?
>> > Anyway to
>> > | speed this up?
>> >
>> > That is odd. "I guess it shouldn't." I shall take another look -- as I
>> > understand it both should go to the same underlying Lapack routine.  I
>> > may
>> > have to consult with Conrad on this.
>> >
>> > Thanks for posting a full and reproducible example!
>> >
>> > Dirk
>> >
>> > | require(RcppArmadillo)
>> > | require(inline)
>> > |
>> > | arma.code <- '
>> > |   using namespace arma;
>> > |   NumericMatrix Xr(Xs);
>> > |   int n = Xr.nrow(), k = Xr.ncol();
>> > |   mat X(Xr.begin(), n, k, false);
>> > |   mat U;
>> > |   vec s;
>> > |   mat V;
>> > |   svd(U, s, V, X);
>> > |   return wrap(s);
>> > | '
>>
>> Because the arma code is evaluating the singular vectors (U and V) as
>> well as the singular values (S) whereas the R code is only evaluating
>> the singular values.  There is considerably more effort required to
>> evaluate the singular vectors in addition to the singular values.
>>
>> > | rcppsvd <- cxxfunction(signature(Xs="numeric"),
>> > |                         arma.code,
>> > |                         plugin="RcppArmadillo")
>> > |
>> > | A<-matrix(rnorm(5000^2), 5000)
>> > |
>> > | > system.time(rcppsvd(A))
>> > |     user   system  elapsed
>> > | 1992.406    4.862 1988.737
>> > |
>> > | > system.time(svd(A))
>> > |    user  system elapsed
>> > | 652.496   2.641 652.614
>> > |
>> > | On Wed, Jun 13, 2012 at 11:43 AM, Dirk Eddelbuettel <edd at debian.org>
>> > wrote:
>> > |
>> > |
>> > |     On 13 June 2012 at 10:57, Julian Smith wrote:
>> > |     | I've been toying with both RcppArmadillo and RcppEigen the past
>> > few days
>> > |     and
>> > |     | don't know which library to continue using. RcppEigen seems
>> > really slick,
>> > |     but
>> > |     | appears to be lacking some of the decompositions I want and
>> > isn't nearly
>> > |     as
>> > |     | fast to code. RcppArmadillo seems about as fast, easier to code
>> > up etc.
>> > |     What
>> > |     | are some of the advantages/disadvantages of both?
>> > |
>> > |     That's pretty close.  I have been a fan of [Rcpp]Armadillo which I
>> > find
>> > |     easier to get my head around.  Doug, however, moved from
>> > [Rcpp]Armadillo
>> > |     to
>> > |     [Rcpp]Eigen as it has some things he needs.  Eigen should have a
>> > "larger"
>> > |     API
>> > |     than Armadillo, but I find the code and docs harder to navigate.
>> > |
>> > |     And you should find Eigen to be a little faster. Andreas Alfons
>> > went as far
>> > |     as building 'robustHD' using RcppArmadillo with a drop-in for
>> > RcppEigen (in
>> > |     package 'sparseLTSEigen'; both package names from memmory and I
>> > may have
>> > |     mistyped).  He reported a performance gain of around 25% for his
>> > problem
>> > |     sets.  On the 'fastLm' benchmark, we find the fast Eigen-based
>> > |     decompositions
>> > |     to be much faster than Armadillo.
>> > |
>> > |     | Can you call LAPACK or BLAS from either? Is there a wrapper in
>> > RcppEigen
>> > |     to
>> > |     | call LAPACK functions? Want some other decomposition methods,
>> > dont like
>> > |     the
>> > |     | JacobiSVD method in Eigen.
>> > |
>> > |     You need to differentiate between the Eigen and Armadillo docs
>> > _for their
>> > |     libraries_ and what happens when you access the Rcpp* variant from
>> > R.
>> > |
>> > |     RcppArmadillo will use the very same LAPACK and BLAS libs your R
>> > session
>> > |     uses. So MKL, OpenBlas, ... are all options.  Eigen actually has
>> > its own
>> > |     code
>> > |     outperforming LAPACK, so it doesn't  as much there.
>> > |
>> > |     Hope this helps,   Dirk (at useR!)
>> > |
>> > |     |
>> > |     |
>> > ----------------------------------------------------------------------
>> > |     | _______________________________________________
>> > |     | Rcpp-devel mailing list
>> > |     | Rcpp-devel at lists.r-forge.r-project.org
>> > |     |
>> > https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/rcpp-devel
>> > |     --
>> > |     Dirk Eddelbuettel | edd at debian.org | http://dirk.eddelbuettel.com
>> > |
>> > |
>> >
>> > --
>> > Dirk Eddelbuettel | edd at debian.org | http://dirk.eddelbuettel.com
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