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Generalized Eigenvalues and Eigenvectors of a General Matrix
Syntax lam = geneig(A, B)
[E, Lam] = geneig(A, B )
[E, Alp, Beta] = geneig(A, B )
See Also eig , eigen , svd

Description
Computes the generalized eigenvalues of the matrix pair A, B, where a and b are square real, double-precision or complex matrix. The matrices A and B must have the same dimension.

The return value lam is a complex column vector, with the same row dimension as A, containing the generalized eigenvalues.

The return value Lam is a complex diagonal matrix with the same dimensions as A and with the generalized eigenvalues along its diagonal.

The return values Alp and Beta are a complex diagonal matrices with the same dimensions as A and with
     Beta * Lam = Alp
This is a more stable form for representing the generalized eigenvalues.

The return value E is a matrix with the same dimensions as A and with each of its columns corresponding to a generalized eigenvector. For each eigenvector is scaled so that its Euclidean norm is one; i.e., for each i,
            __________________________
     1 =   /       2                2
         \/ E(i, 1)  + ... + E(i, n)
where n is the row dimension of A.

The return values satisfy the following two equations
            A * E = B * E * Lam
     A * E * Beta = B * E * Alp 
and the vector lam is equal to the diagonal of Lam.

Example
If you enter
     A        = {[1., 2.], [3., 4.]}
     B        = identity(2)
     [E, Lam] = geneig(A, B)
     print E, Lam
 O-Matrix will respond
     {
     [ (0.824565,0) , (0.415974,0) ]
     [ (-0.565767,0) , (0.909377,0) ]
     }
     {
     [ (-0.372281,0) , (0,0) ]
     [ (0,0) , (5.37228,0) ]
     }
 Note that coercion converted the argument B from an integer matrix to a real matrix before the calculation was done.

Mlmode
In Mlmode , this function is called eig instead of geneig. If in Mlmode you enter
     A      = {[1., 2.], [3., 4.]}
     B      = eye(2)
     [E, Lam] = eig(A, B)
     E, Lam
 O-Matrix will respond
     {
     [ (0.824565,0) , (0.415974,0) ]
     [ (-0.565767,0) , (0.909377,0) ]
     }
     {
     [ (-0.372281,0) , (0,0) ]
     [ (0,0) , (5.37228,0) ]
     }