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Compare QR Decomposition Results from different Numeric Engines

Posted 9 years ago
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Currently I have several numerical engines my mac, including Mathematica itself (use some MKL kernel), customized LAPACK library in System Modeler and the OSX's native Accelerator framework. 

I wrote several programs here to compare decomposed results in different forms. No intend to find performance. Should this article be helpful for those want to transfer results among these computing platforms. QR decomposition is chosen here because this matrix transformation does not gurantee a unique output for a given input. This discussion is also inspired by this thread. 

Wolfram Language
My test data is a 5-by-5 matrix generated by

Just use the build-in function to find the orthognal matrix Q and the right upper triangle:  

For matrices on Real domain, we can simply verify with the following: 

Note that Mathematica uses row-based arrangement, which perfectly follows C/C++ standard and other common notation.  

Wolfram System Modeler
We can also decompose the matrix by using the LAPACK library bundled with System Modeler. Once you have the System modeler open and drag model and function into the User Class section, you can quickly call them via Mathematica's WSMLink seamlessly by the name of the classes/model. Here the QRDModel calls the myQRD function, you just need to load the name of the model only. 
<< WSMLink`
mmodel = "myLapackQRDModel";
sim = WSMSimulate[mmodel, {0, 1}] // Quiet;

I have the following outputs, each one is an entry to the output matrix.
{b[1,1],b[1,2],b[1,3],b[1,4],... b[5,5]}
Define an auxillary function below to extract the variable trajectory. Each variable is a function of time but with constant value, so you set an arbitary time point, you can check out the value of the entry. 
You can see that the Q here is slightly different from what we had from Mathematica. This means the Lapack method chooses a different pivoting scheme and assembled the Q with somewhat different Household elements
Q2 = Table[f[i, j][0.5], {i, 1, 5}, {j, 1, 5}];
Labeled[Framed[Chop[Q2] // MatrixForm], {"WSM Lapack", "Q"}, {Top,

Verify if the new Q is unitarian and orthonormal: 

In the WSM example, I actually call an external cpp file. A C/C++ interface is very useful and add flexibility for more control for experience C users. 

Accelerate Framework: <Accelerate/Accelerate.h>
Last part is about the build-in free LAPACK on OSX 10.6+ . You can run the QRDecomp.cpp and the print out result into this notebook on mac. If you are on Linux or Win then you can use "-L/lapack/dir -lLAPACK" flag if you know where a libLapack.a file is installed. Otherwise "-framwork Accelerate" is all you need to complile the cpp file. 

Verify the Unitarian property: 

The default R matrix is not strictly an right - upper triangle but requires permutation matrix P:
A*P = Q*R
This piece of information about P is usually found in the updated value of the "jpvt" argument in "dgeqpf_". 
dgeqpf_(&nRow, &nCol, output_Matrix, &nRow, jpvt, tau, work, &info); // C code

Very Important : Array arrangement
If you find a documentation about calling fortran LAPACK function, you have to be very careful about handling array arrangement. You usually have to transpose the matrix you see in Mathematica and C as input for the LAPACK/BLAS and their C version. You do not need to transpose the intermediate result. Also in this note book, I do not transpose the output matrix because I want to have (A = Q\*R ) or (Q*A = R ) so that the latter two results can be used as the native output by Mathematica. Just be aware that the array arrangement are different and sometimes you need to test little bit to find what you have matches your convention.

Code snippets:
model myLapackQRDModel
  Real y[5,5]=__test_data__;
  Real b[5,5];
end myLapackQRDModel;
function myQRD
  input Real a[:,:];
  output Real b[size(a, 1),size(a, 2)];
  external "C" myQRD(size(a, 1),size(a, 2),a,b)  annotation(Include="#include \"myQRD.cpp\"", Library="LAPACK");
end myQRD;
 #include <stdlib.h>
 #define MIN(a,b) ((a) < (b) ? (a) : (b))
 extern "C" {
     // for QR decomposition
     int dgeqpf_(int *m, int *n, double *a, int *lda, int *jpvt, double *tau, double *work, int *info);
     int dorgqr_(int *m, int *n, int *k, double *a, int *lda, double *tau, double *work, int *lwork,
                 int *info);

void myQRD(int m,int n, double *matrix, double* b) {
    int error=1;
    int *jpvt = new int[n]; //pivot matrix
    double *tau = new double[MIN(n,m)];
    double *work = new double[3*n]; //working array
    int info=1; // find which col has illegal value
    int i;
    for (i=0;i<m*n;i++) b[i] = matrix[i];
    /*   dgeqpf */
    int nRow = m;
    int nCol = n;
    error = dgeqpf_(&nRow, &nCol, b, &nRow, jpvt, tau, work, &info);
    delete[] work;
    /* dorgqr */
    int k = MIN(n,m);
    int lwork= MIN(10, nCol)*nCol;
    double *work2 = new double[lwork];
    dorgqr_(&nRow, &nCol, &k, b ,&nRow, tau, work2, &lwork,&info);
    delete[] work2;
C++ test for Mac user
This is a standalone test code. To compile, you need the following command:
g++ -o QRDExample QRDecomp.cpp -framework Accelerate
 //  QRDecomp.cpp
 #include <iostream>
 #include <Accelerate/Accelerate.h>
 #define MIN(a,b) ((a) < (b) ? (a) : (b))
 void myQRD(int m,int n, double *matrix, double* b);
 int main(int argc, const char * argv[])

    double data[5][5] = < copy the __test_data__ and paste here >
    int m = 5;
    int n = 5;
    // flatten the data array
    double* input = new double[m*n];
    // store the result
    double* Res = new double[m*n];
    //transpose the data array as an input to the myQRD function
    int i, j;
    for(i=0;i<m;i++ ){
        for(j=0; j< n; j++){
            input[ i * m + j ] = data[j][i];
    // find QR decomp
    myQRD(m,n,input, Res);
    //print out result, can be pasted into Mathematica
    for(i=0;i<m;i++ ){
        for(j=0; j<n; j++){
            ((j == n-1) ? std::cout<< Res[m*i+j]:std::cout<< Res[i*m+j]<<"," );
    return 0;

void myQRD(int m,int n, double *matrix, double* b) {
    /**** Same as the myQRD() in myQRD.cpp ****/
Code can be downloaded from the attachment link. 
POSTED BY: Shenghui Yang
4 Replies
@Lichtblau, because most my Mathematica outputs were formatted, I had some some trouble to paste them into the thread and use image instead. You can download the notebook at the end of my post.

I think you are right about the Mathematica implementation. I have updated the post. 
POSTED BY: Shenghui Yang
Regarding handling of exact QRDecomposition in Mathematica...I did write that code...once upon a time.
POSTED BY: Daniel Lichtblau
Interesting. I have a few remarks.

(1) I cannot cut-and-paste your Mathematica code, since it is, apparently, a picture of code.

(2) It is not using Lapack or MKL. To achieve that one would numericize the matrix first.
(Though maybe you wanted to show that it can be computed exactly and then numericized?)

Item (2) is really only an issue if you do decide to scale up the size and compare timings.

(3) I located a very old Usenet post on which Mathematica's exact QRDecomposition is
at least loosely based.!searchin/sci.math.symbolic/qr$20decomposition/sci.math.symbolic/afIBNPiWiu8/225y2JzQL84J

(At least my long term memory is still intact..)
POSTED BY: Daniel Lichtblau
Danny, his code are images because at the very end of the post he attached a complete notebook - you can download it.
POSTED BY: Sam Carrettie
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