Rewriting Matlab eig(A,B) (Generalized eigenvalues

Do anyone have any idea how can I rewrite eig(A,B) from Matlab used to calculate generalized eigenvector/eigenvalues? I've been struggling with this problem lately. So far:

Matlab definition of eig function I need:

[V,D] = eig(A,B) produces a diagonal matrix D of generalized
eigenvalues and a full matrix V whose columns are the corresponding
eigenvectors so that A*V = B*V*D.

So far I tried the Eigen library (http://eigen.tuxfamily.org/dox/classEigen_1_1GeneralizedSelfAdjointEigenSolver.html)

My implementation looks like this:

std::pair<Matrix4cd, Vector4d> eig(const Matrix4cd& A, const Matrix4cd& B)
{
    Eigen::GeneralizedSelfAdjointEigenSolver<Matrix4cd> solver(A, B);

    Matrix4cd V = solver.eigenvectors();
    Vector4d D = solver.eigenvalues();

    return std::make_pair(V, D);
}

But first thing that comes to my mind is, that I can't use Vector4cd as .eigenvalues() doesn't return complex values where Matlab does. Furthermore results of .eigenvectors() and .eigenvalues() for the same matrices are not the same at all:

C++:

Matrix4cd x;
Matrix4cd y;
pair<Matrix4cd, Vector4d> result;

for (int i = 0; i < 4; i++)
{
    for (int j = 0; j < 4; j++)
    {
        x.real()(i,j) = (double)(i+j+1+i*3);
        y.real()(i,j) = (double)(17 - (i+j+1+i*3));

        x.imag()(i,j) = (double)(i+j+1+i*3);
        y.imag()(i,j) = (double)(17 - (i+j+1+i*3));
    }
}
result = eig(x,y);
cout << result.first << endl << endl;
cout << result.second << endl << endl;

Matlab:

for i=1:1:4
    for j=1:1:4
        x(i,j) = complex((i-1)+(j-1)+1+((i-1)*3), (i-1)+(j-1)+1+((i-1)*3));
        y(i,j) = complex(17 - ((i-1)+(j-1)+1+((i-1)*3)), 17 - ((i-1)+(j-1)+1+((i-1)*3)));
    end
end

[A,B] = eig(x,y)

So I give eig the same 4x4 matrices holding values 1-16 ascending (x) and descending (y). But I receive different results, furthermore Eigen method returns double from eigenvalues while Matlab returns complex dobule. I also find out that there is other Eigen solver named GeneralizedEigenSolver. That one in the documentation (http://eigen.tuxfamily.org/dox/classEigen_1_1GeneralizedEigenSolver.html) has written that it solves A*V = B*V*D but to be honest I tried it and results (matrix sizes) are not the same size as Matlab so I got quite lost how it works (examplary results are on the website I've linked). It also has only .eigenvector method.

C++ results:

(-0.222268,-0.0108754) (0.0803437,-0.0254809) (0.0383264,-0.0233819) (0.0995482,0.00682079)
(-0.009275,-0.0182668) (-0.0395551,-0.0582127) (0.0550395,0.03434) (-0.034419,-0.0287563)
(-0.112716,-0.0621061) (-0.010788,0.10297) (-0.0820552,0.0294896) (-0.114596,-0.146384)
(0.28873,0.257988) (0.0166259,-0.0529934) (0.0351645,-0.0322988) (0.405394,0.424698)

-1.66983
-0.0733194
0.0386832
3.97933

Matlab results:

[A,B] = eig(x,y)


A =

  Columns 1 through 3

  -0.9100 + 0.0900i  -0.5506 + 0.4494i   0.3614 + 0.3531i
   0.7123 + 0.0734i   0.4928 - 0.2586i  -0.5663 - 0.4337i
   0.0899 - 0.4170i  -0.1210 - 0.3087i   0.0484 - 0.1918i
   0.1077 + 0.2535i   0.1787 + 0.1179i   0.1565 + 0.2724i

  Column 4

  -0.3237 - 0.3868i
   0.2338 + 0.7662i
   0.5036 - 0.3720i
  -0.4136 - 0.0074i


B =

  Columns 1 through 3

  -1.0000 + 0.0000i   0.0000 + 0.0000i   0.0000 + 0.0000i
   0.0000 + 0.0000i  -1.0000 - 0.0000i   0.0000 + 0.0000i
   0.0000 + 0.0000i   0.0000 + 0.0000i  -4.5745 - 1.8929i
   0.0000 + 0.0000i   0.0000 + 0.0000i   0.0000 + 0.0000i

  Column 4

   0.0000 + 0.0000i
   0.0000 + 0.0000i
   0.0000 + 0.0000i
  -0.3317 + 1.1948i

Second try was with Intel IPP but it seems that it solves only A*V = V*D and support told me that it's not supported anymore.

https://software.intel.com/en-us/node/505270 (list of constructors for Intel IPP)

I got suggestion to move from Intel IPP to MKL. I did it and hit the wall again. I tried to check all algorithms for Eigen but it seems that there are only A*V = V*D problems solved. I was checking lapack95.lib. The list of algorithms used by this library is available there: https://software.intel.com/sites/products/documentation/doclib/mkl_sa/11/mkl_lapack_examples/index.htm#dsyev.htm

Somewhere on the web I could find topic on Mathworks when someone said that managed to solve my problem partially with usage of MKL:

http://jp.mathworks.com/matlabcentral/answers/40050-generalized-eigenvalue-and-eigenvectors-differences-between-matlab-eig-a-b-and-mkl-lapack-dsygv

Person said that he/she used dsygv algorithm but I can't locate anything like that on the web. Maybe it's a typo.

Anyone has any other proposition/idea how can I implement it? Or maybe point my mistake. I'd appreciate that.

EDIT: In comments I've received a hint that I was using Eigen solver wrong. My A matrix wasn't self-adjoint and my B matrix wasn't positive-definite. I took matrices from program I want to rewrite to C++ (from random iteration) and checked if they meet the requirements. They did:

Rj =

  1.0e+02 *

 Columns 1 through 3

   0.1302 + 0.0000i  -0.0153 + 0.0724i   0.0011 - 0.0042i
  -0.0153 - 0.0724i   1.2041 + 0.0000i  -0.0524 + 0.0377i
   0.0011 + 0.0042i  -0.0524 - 0.0377i   0.0477 + 0.0000i
  -0.0080 - 0.0108i   0.0929 - 0.0115i  -0.0055 + 0.0021i

  Column 4

  -0.0080 + 0.0108i
   0.0929 + 0.0115i
  -0.0055 - 0.0021i
   0.0317 + 0.0000i

Rt =

  Columns 1 through 3

   4.8156 + 0.0000i  -0.3397 + 1.3502i  -0.2143 - 0.3593i
  -0.3397 - 1.3502i   7.3635 + 0.0000i  -0.5539 - 0.5176i
  -0.2143 + 0.3593i  -0.5539 + 0.5176i   1.7801 + 0.0000i
   0.5241 + 0.9105i   0.9514 + 0.6572i  -0.7302 + 0.3161i

  Column 4

   0.5241 - 0.9105i
   0.9514 - 0.6572i
  -0.7302 - 0.3161i
   9.6022 + 0.0000i

As for Rj which is now my A - it is self-adjoint because Rj = Rj' and Rj = ctranspose(Rj). (http://mathworld.wolfram.com/Self-AdjointMatrix.html)

As for Rt which is now my B - it is Positive-Definite what is checked with method linked to me. (http://www.mathworks.com/matlabcentral/answers/101132-how-do-i-determine-if-a-matrix-is-positive-definite-using-matlab). So

>> [~,p] = chol(Rt)

p =

     0

I've rewritten matrices manually to C++ and performed eig(A,B) again with matrices meeting requirements:

Matrix4cd x;
Matrix4cd y;
pair<Matrix4cd, Vector4d> result;

x.real()(0,0) = 13.0163601949795;
x.real()(0,1) = -1.53172561296005;
x.real()(0,2) = 0.109594869350436;
x.real()(0,3) = -0.804231869422614;

x.real()(1,0) = -1.53172561296005;
x.real()(1,1) = 120.406645675346;
x.real()(1,2) = -5.23758765476463;
x.real()(1,3) = 9.28686785230169;

x.real()(2,0) = 0.109594869350436; 
x.real()(2,1) = -5.23758765476463;
x.real()(2,2) = 4.76648319080400;
x.real()(2,3) = -0.552823839520508;

x.real()(3,0) = -0.804231869422614;
x.real()(3,1) = 9.28686785230169;
x.real()(3,2) = -0.552823839520508;
x.real()(3,3) = 3.16510496622613;

x.imag()(0,0) = -0.00000000000000;
x.imag()(0,1) = 7.23946944213164;
x.imag()(0,2) = 0.419181335323979;
x.imag()(0,3) = 1.08441894337449;

x.imag()(1,0) = -7.23946944213164;
x.imag()(1,1) = -0.00000000000000;
x.imag()(1,2) = 3.76849276970080;
x.imag()(1,3) = 1.14635625342266;

x.imag()(2,0) = 0.419181335323979;
x.imag()(2,1) = -3.76849276970080;
x.imag()(2,2) = -0.00000000000000;
x.imag()(2,3) = 0.205129702522089;

x.imag()(3,0) = -1.08441894337449;
x.imag()(3,1) = -1.14635625342266;
x.imag()(3,2) = 0.205129702522089;
x.imag()(3,3) = -0.00000000000000;

y.real()(0,0) = 4.81562784930907;
y.real()(0,1) = -0.339731222392148;
y.real()(0,2) = -0.214319720979258;
y.real()(0,3) = 0.524107127885349;

y.real()(1,0) = -0.339731222392148;
y.real()(1,1) = 7.36354235698375;
y.real()(1,2) = -0.553927983436786;
y.real()(1,3) = 0.951404408649307;

y.real()(2,0) = -0.214319720979258;
y.real()(2,1) = -0.553927983436786;
y.real()(2,2) = 1.78008768533745;
y.real()(2,3) = -0.730246631850385;

y.real()(3,0) = 0.524107127885349;
y.real()(3,1) = 0.951404408649307;
y.real()(3,2) = -0.730246631850385;
y.real()(3,3) = 9.60215057284395;

y.imag()(0,0) = -0.00000000000000;
y.imag()(0,1) = 1.35016928394966;
y.imag()(0,2) = -0.359262708214312;
y.imag()(0,3) = -0.910512495060186;

y.imag()(1,0) = -1.35016928394966;
y.imag()(1,1) = -0.00000000000000;
y.imag()(1,2) = -0.517616473138836;
y.imag()(1,3) = -0.657235460367660;

y.imag()(2,0) = 0.359262708214312;
y.imag()(2,1) = 0.517616473138836;
y.imag()(2,2) = -0.00000000000000;
y.imag()(2,3) = -0.316090662865005;

y.imag()(3,0) = 0.910512495060186;
y.imag()(3,1) = 0.657235460367660;
y.imag()(3,2) = 0.316090662865005;
y.imag()(3,3) = -0.00000000000000;

result = eig(x,y);

cout << result.first << endl << endl;
cout << result.second << endl << endl;

And the results of C++:

(0.0295948,0.00562174) (-0.253532,0.0138373) (-0.395087,-0.0139696) (-0.0918132,-0.0788735)
(-0.00994614,-0.0213973) (-0.0118322,-0.0445976) (0.00993512,0.0127006) (0.0590018,-0.387949)
(0.0139485,-0.00832193) (0.363694,-0.446652) (-0.319168,0.376483) (-0.234447,-0.0859585)
(0.173697,0.268015) (0.0279387,-0.0103741) (0.0273701,0.0937148)  (-0.055169,0.0295393)

0.244233
2.24309
3.24152
18.664

Results of MATLAB:

>>  [A,B] = eig(Rj,Rt)

A =

  Columns 1 through 3

   0.0208 - 0.0218i   0.2425 + 0.0753i  -0.1242 + 0.3753i
  -0.0234 - 0.0033i  -0.0044 + 0.0459i   0.0150 - 0.0060i
   0.0006 - 0.0162i  -0.4964 + 0.2921i   0.2719 + 0.4119i
   0.3194 + 0.0000i  -0.0298 + 0.0000i   0.0976 + 0.0000i

  Column 4

  -0.0437 - 0.1129i
   0.2351 - 0.3142i
  -0.1661 - 0.1864i
  -0.0626 + 0.0000i

B =

   0.2442         0         0         0
        0    2.2431         0         0
        0         0    3.2415         0
        0         0         0   18.6640

Eigenvalues are the same! Nice, but why Eigenvectors are not similar at all?

回答1:

There is no problem here with Eigen.

In fact for the second example run, Matlab and Eigen produced the very same result. Please remember from basic linear algebra that eigenvector are determined up to an arbitrary scaling factor. (I.e. if v is an eigenvector the same holds for alpha*v, where alpha is a non zero complex scalar.)

It is quite common that different linear algebra libraries compute different eigenvectors, but this does not mean that one of the two codes is wrong: it simply means that they choose a different scaling of the eigenvectors.

EDIT

The main problem with exactly replicating the scaling chosen by matlab is that eig(A,B) is a driver routine, which depending from the different properties of A and B may call different libraries/routines, and apply extra steps like balancing the matrices and so on. By quickly inspecting your example, I would say that in this case matlab is enforcing following condition:

all(imag(V(end,:))==0) (the last component of each eigenvector is real)

but not imposing other constraints. This unfortunately means that the scaling is not unique, and probably depends on intermediate results of the generalised eigenvector algorithm used. In this case I'm not able to give you advice on how to exactly replicate matlab: knowledge of the internal working of matlab is required.

As a general remark, in linear algebra usually one does not care too much about eigenvector scaling, since this is usually completely irrelevant for the problem solved, when the eigenvectors are just used as intermediate results.

The only case in which the scaling has to be defined exactly, is when you are going to give a graphic representation of the eigenvalues.

回答2:

The eigenvector scaling in Matlab seems to be based on normalizing them to 1.0 (ie. the absolute value of the biggest term in each vector is 1.0). In the application I was using it also returns the left eigenvector rather than the more commonly used right eigenvector. This could explain the differences between Matlab and the eigensolvers in Lapack MKL.