public static INDArray symmetricGeneralizedEigenvalues(INDArray A, INDArray B) {
        assert A.rows() == A.columns();
        assert B.rows() == B.columns();
        INDArray W = Nd4j.create(A.rows());

        A = InvertMatrix.invert(B, false).mmuli(A);
        Nd4j.getBlasWrapper().syev('V', 'L', A, W);
        return W;
    }
public static INDArray symmetricGeneralizedEigenvalues(INDArray A, INDArray B) {
        assert A.rows() == A.columns();
        assert B.rows() == B.columns();
        INDArray W = Nd4j.create(A.rows());

        A = InvertMatrix.invert(B, false).mmuli(A);
        Nd4j.getBlasWrapper().syev('V', 'L', A, W);
        return W;
    }
public static INDArray symmetricGeneralizedEigenvalues ( INDArray A , INDArray B ) { assert A . rows ( ) == A . columns ( ) ; assert B . rows ( ) == B . columns ( ) ; INDArray W = Nd4j . create ( A . rows ( ) ) ; A = InvertMatrix . invert ( B , false ) . mmuli ( A ) ; Nd4j . getBlasWrapper ( ) . syev ( ' ' , ' ' , A , W ) ; return W ; }
Compute generalized eigenvalues of the problem A x = L B x.
The data will be unchanged, no eigenvectors returned.

@param A symmetric Matrix A.
@param B symmetric Matrix B.
@return a vector of eigenvalues L.
Compute generalized eigenvalues of the problem A x = L B x . The data will be unchanged no eigenvectors returned .