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Bug 1062 - SelfAdjointEigenSolver incorrect with RowMajor matrices
SelfAdjointEigenSolver incorrect with RowMajor matrices
Status: RESOLVED FIXED
Product: Eigen
Classification: Unclassified
Component: Eigenvalues
3.2
x86 - 64-bit Linux
: Normal Wrong Result
Assigned To: Nobody
:
Depends on:
Blocks:
  Show dependency treegraph
 
Reported: 2015-09-03 12:55 UTC by tyldurd
Modified: 2015-09-04 16:33 UTC (History)
5 users (show)



Attachments

Description tyldurd 2015-09-03 12:55:40 UTC
SelfAdjointEigenSolver gives different results if used with row major or column major matrices. 
The differences seem to increase with the dimension of the matrix. 
The type also has an influence: floats give results worse than double. 
Complex floats are even worse.
I also noted differences while using ComplexEigenSolver, but they are smaller.

Here is a code sample to illustrate the problem:

#include<iostream>
#include "Eigen/Dense"

using namespace Eigen;
using namespace std;

int main() {

  typedef float mytype ;
  typedef Matrix<complex<mytype>, Dynamic, Dynamic> EigMatCM;
  typedef Matrix<complex<mytype>, Dynamic, Dynamic, RowMajor> EigMatRM;

  int d = 20;

  EigMatCM X = EigMatCM::Random(d,d);
  EigMatCM A = X + X.transpose().conjugate();
  SelfAdjointEigenSolver<EigMatCM> es(A);
  
  EigMatRM Xr = X;
  EigMatRM Ar = Xr + Xr.transpose().conjugate();
  SelfAdjointEigenSolver<EigMatRM> esr(Ar);
  
  cout<<es.eigenvalues()-esr.eigenvalues()<<endl;
}

I compared the output to the one given by numpy.linalg.eig function, and only the row major version seems to give incorrect results.
Comment 1 Gael Guennebaud 2015-09-04 16:15:40 UTC
This issue does not apply to the default branch, only the 3.2 branch is affected.
Comment 2 Gael Guennebaud 2015-09-04 16:33:21 UTC
https://bitbucket.org/eigen/eigen/commits/4458e97cf524/
Branch:      3.2
Summary:     Bug 1062: backport fix of SelfAdjointEigenSolver for RowMajor matrices from default branch

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