[Rcpp-devel] RcppEigen now on CRAN

[baptiste auguie]

Hi,

Another august piece work, thanks.
As a happy user of Armadillo (guess what, this week my packages even
passed win-builder! Yeah!) I have a general question: between (Rcpp),
RcppGSL, RcppArmadillo, RcppEigen, we now have so much choice that it
may be difficult to decide which framework to use for a particular
project. Are there comparative charts somewhere, in terms of function
coverage, speed? (those I found did not cover these three libraries).
It could also be very useful to have a kind of Rosetta stone, akin to
http://arma.sourceforge.net/docs.html#syntax but comparing (R) | Rcpp
|  RcppGSL | RcppArmadillo | RcppEigen, ideally with wise comments
from advanced users. Where could we build such meta-documentation?

Just a thought,

Best regards,

baptiste




On 28 June 2011 07:55, Douglas Bates <bates at stat.wisc.edu> wrote:
> A new package, RcppEigen, which provides linkage from R to the Eigen
> C++ template library for linear algebra (http://eigen.tuxfamily.org)
> via Rcpp, is now available on CRAN.    The Eigen library is a pure
> template library that does not depend on the BLAS or Lapack, thus
> avoiding some difficulties such as Baptiste Auguste encountered
> regarding inconsistent versions of the Lapack routines being available
> on different platforms.
>
> A great deal of care has gone into the development of the Eigen
> library to ensure good performance through the use of vectorization
> instructions such as SSE2, SSE3 and ARM_NEON, when available, and by
> using block structured algorithms.  The files RcppEigen/src/fastLm.h
> and RcppEigen/src/fastLm.cpp in the source package provide examples of
> the use of some of the decompositions available in Eigen.  Eigen
> supports both dense and sparse matrices, with various types of
> triangular, symmetric (or selfAdjoint) and diagonal adaptors.  To
> learn about the facilities available in Eigen I would recommend
> starting with the tutorial at
> http://eigen.tuxfamily.org/dox/index.html
>
> Like the Rcpp and RcppArmadillo packages, RcppEigen has a plugin for
> cxxfunction from the inline package, to allow for rapid prototyping.
> There is also a function RcppEigen.package.skeleton to aid in creating
> a package that uses the RcppEigen headers.
>
> Because Eigen essentially creates its own BLAS functions, the size of
> the libs directory for packages using RcppEigen can be very large and
> compiling source files that include the RcppEigen.h file can be
> relatively slow.  As with most template libraries, the idea is to
> off-load the complexity of the code onto the compiler to sort out,
> with the penalty that instantiating templates may slow down the
> compiler.
>
> I enclose an example of a short C++ function using RcppEigen and a
> slightly longer and more complex C++ function, compiled with inline.
> Some things to notice:
>
> - in keeping with the functional programming semantics of R, it is a
> good idea to declare Rcpp objects created from the input SEXP
> arguments as const.
>
> - a Eigen matrix of double precision values has type Eigen::MatrixXd,
> similarly Eigen::MatrixXi for integer matrices and Eigen::MatrixXcd
> for matrices of std::complex<double> (see the tutorial for the
> explanation of the X in MatrixXd)
>
> - the Eigen::VectorXd class is a column vector of doubles, similarly
> Eigen::VectorXi and Eigen::VectorXcd.  A row vector is
> Eigen::RowVectorXd, etc.  These are all just specializations of the
> Matrix template (Vectors have 1 column, RowVectors have 1 row, fixed
> at compile time).
>
> - to obtain an Eigen matrix or vector from an Rcpp object, without
> copying the contents, use the Eigen::Map adaptor class.
>
> - the Eigen classes ArrayXd and ArrayXXd are similar representations
> to VectorXd and MatrixXd but allow for element-wise operations.  The
> .array() method creates a view of a Vector or Matrix object as an
> Array.  The .matrix() method maps the other way.
>
> - the Eigen::Matrix and Eigen::Array classes have methods for
> Rcpp::wrap defined in the RcppEigen headers.
>
> - Adaptor methods like .triangularView<type>(),
> .selfadjointView<type>() and .diagonalView() (applied to a Vector) are
> the common ways of restricting to specialized forms of matrices.
>
> - most methods for Eigen classes return *this as a reference so that
> methods can be chained.
>
> - like Armadillo, Eigen performs lazy evaluation of expressions to
> reduce the number of intermediate results that must be stored.
>
> As with any new libraries, Eigen takes some getting used to, but
> overall I have found it very good to work with and recommend its use.
>
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