hi,

given this example code, I am getting a floating point exception when I run this simple program. I am a newbie and would love some help

thank you

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

using namespace Eigen;
using namespace std;

int main()
{
Matrix<int, 6, 6> A;
A << 2 , -1, -1, 0 , 0 , 0,
-1, 3, 0, -1, 0 , -1,
-1, 0, 2, -1, 0, 0,
0, -1, -1, 3, -1, 0,
0, 0, 0, -1, 2, -1,
0, -1, 0, 0, -1, 2;

cout << "A:" << endl << A << endl << endl;
EigenSolver< Matrix<int, 6, 6> > es(A);
cout << "The eigenvalues of A are:" << endl << es.eigenvalues() << endl;
cout << "The matrix of eigenvectors, V, is:" << endl <<
es.eigenvectors() << endl << endl;
return 0;
}

but I get a floating point exception when I run the program..

A:
2 -1 -1 0 0 0
-1 3 0 -1 0 -1
-1 0 2 -1 0 0
0 -1 -1 3 -1 0
0 0 0 -1 2 -1
0 -1 0 0 -1 2

Floating point exception: 8

I am using eigen package 3.2.4 and my g++ version is
Configured with: --prefix=/Library/Developer/CommandLineTools/usr --with-gxx-include-dir=/usr/include/c++/4.2.1
Apple LLVM version 6.0 (clang-600.0.56) (based on LLVM 3.5svn)
Target: x86_64-apple-darwin13.4.0
Thread model: posix

As you saw, EigenSolver does not work with a matrix of integers. You need to use a matrix of floating-point numbers.

The Eigen library probably should guard against such mistakes; this is logged as bug 750 at http://eigen.tuxfamily.org/bz/show_bug.cgi?id=750 .

Thank you so much. you are a life saver !!
replacing with MatrixXf seems to give correct eigen values but the eigen vector seem to be approximate for one eigen value and incorrect for second eigen value.

is this a limitation of eigen or is there some initialization to do so that I get correct eigen vectors ? I am working mostly with laplacian matrixes. is there something additional
that I should do ?

int main()
{
MatrixXf A(2,2);
A << 2.0, 1.0,
1.0, 2.0;
EigenSolver< MatrixXf > es(A);

cout << "A:" << endl << A << endl << endl;
cout << "The eigenvalues of A are:" << endl << es.eigenvalues() << endl;
cout << "The matrix of eigenvectors, V, is:" << endl <<
es.eigenvectors() << endl << endl;

cout << "first lamba" << es.eigenvalues()[0] << endl;
cout << "first eigen vector: " << endl << es.eigenvectors().col(0) << endl;

cout << "second lamba" << es.eigenvalues()[1] << endl;
cout << "second eigen vector: " << endl << es.eigenvectors().col(1) << endl;

return 0;
}

output
A:
2 1
1 2

The eigenvalues of A are:
(3,0)
(1,0)
The matrix of eigenvectors, V, is:
(0.707107,0) (-0.707107,0)
(0.707107,0) (0.707107,0)

first lamba(3,0)
first eigen vector:
(0.707107,0)
(0.707107,0)
second lamba(1,0)
second eigen vector:
(-0.707107,0)
(0.707107,0)

but the actual eigen vector for 3 is (1,1) and
eigen vector for 1 is (1, -1)

I do not see anything wrong, eigenvectors are supposed to be unit vectors.

BTW, if your matrices are selfadjoint (or real symmetric), then better use the SelfAdjointEigenSolver class. You might also consider using double precision (MatrixXd).