lapack

I need to use a Fortran routine called dgebal (documentation here ) in my Rcpparmadillo code. I have included the following headers: # include <RcppArmadillo.h> # include <math.h> However, when I try to compile my code using sourceCpp() I get the following error: error: 'dgebal_' was not declared in this scope If I further include <R_ext/Lapack.h> and <R_ext/BLAS.h> , the code compiles without error and runs fine. However the compiler throws a bunch of warnings like this: C:/PROGRA~1/R/R-32~1.3/include/R_ext/BLAS.h:49:64: warning: declaration of 'double dasum_(const int*, const double*, const

My goal is to use LAPACK with Emscripten. My question is: how to port LAPACK to JS? The are two ways I can think of: CLAPACK to JS where my question is: does anybody know an unofficial version that is later than 3.2.1? And the other way to think of is: how to port FORTRAN to JS? Emscripten is capable of transforming C code to JavaScript. But unfortunately, LAPACK 3.5.0 ( http://www.netlib.org/lapack/ ) is only available in FORTRAN95. The CLAPACK project ( http://www.netlib.org/clapack/ ) is basically what I want: a C version of LAPACK. But this one is outdated; the latest is 3.2.1. F2C only

Do yo know any example to use LAPACK To calculate SVD? The routine dgesdd computes the SVD for a double precision matrix. Do you just need an example of how to use it? Have you tried reading the documentation? An example using the C LAPACK bindings (note that I wrote this just now, and haven't actually tested it. Also note that the exact types for arguments to clapack vary somewhat between platforms so you may need to change int to something else): #include <clapack.h> void SingularValueDecomposition(int m, // number of rows in matrix int n, // number of columns in matrix int lda, // leading

First question, is it a good thing to start using c++11 if I will develop a code for the 3 following years? Then if it is, what is the "best" way to implement a matrix if I want to use it with Lapack? I mean, doing std::vector<std::vector< Type > > Matrix is not easily compatible with Lapack. Up to now, I stored my matrix with Type* Matrix(new Type[N]) (the pointer form with new and delete were important because the size of the array is not given as a number like 5, but as a variable). But with C++11 it is possible to use std::array. According to this site , this container seems to be the best

I am using Numeric Library Bindings for Boost UBlas to solve a simple linear system. The following works fine, except it is limited to handling matrices A(m x m) for relatively small 'm'. In practice I have a much larger matrix with dimension m= 10^6 (up to 10^7). Is there existing C++ approach for solving Ax=b that uses memory efficiently. #include<boost/numeric/ublas/matrix.hpp> #include<boost/numeric/ublas/io.hpp> #include<boost/numeric/bindings/traits/ublas_matrix.hpp> #include<boost/numeric/bindings/lapack/gesv.hpp> #include <boost/numeric/bindings/traits/ublas_vector2.hpp> // compileable

I am attempting to use ZGEEV to calculate eigenvalues and eigenvectors, however am having some trouble with the output being incorrect and also inconsistent when used at different optimization levels. Below is my Fortran code with results at -O1 and -O2 optimization levels. I have also included Python code for comparison. I can only assume that I am calling zgeev() incorrectly somehow, however I am not able to determine how. I believe it is unlikely to be an issue with my LAPACK installation as I have compared the output on two different computers, on Windows and Linux. Fortran code: program

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