# spec

eigenvalues of matrices and pencils

## Calling Sequence

    evals=spec(A)
                [R,diagevals]=spec(A)

                evals=spec(A,B)
                [alpha,beta]=spec(A,B)
                [alpha,beta,Z]=spec(A,B)
                [alpha,beta,Q,Z]=spec(A,B)

## Arguments

A

  real or complex square matrix

B

  real or complex square matrix with same dimensions as `A`

evals

  real or complex vector, the eigenvalues

diagevals

  real or complex diagonal matrix (eigenvalues along the diagonal)

alpha

  real or complex vector, al./be gives the eigenvalues

beta

  real vector, al./be gives the eigenvalues

R

  real or complex invertible square matrix, matrix right eigenvectors.

L

  real or complex invertible square matrix, pencil left eigenvectors.

R

  real or complex invertible square matrix, pencil right eigenvectors.

## Description

evals=spec(A)

  returns in vector `evals` the eigenvalues.

[R,diagevals] =spec(A)

  returns in the diagonal matrix `evals` the eigenvalues and in `R` the right eigenvectors.

evals=spec(A,B)

  returns the spectrum of the matrix pencil A - s B, i.e. the roots of the polynomial matrix s B - A.

[alpha,beta] = spec(A,B)

  returns the spectrum of the matrix pencil `A- s B` ,i.e. the roots of the polynomial matrix `A - s B` .Generalized eigenvalues alpha and beta are so that the matrix `A - alpha./beta B` is a singular matrix. The eigenvalues are given by `al./be` and if `beta(i) = 0` the ith eigenvalue is at infinity. (For `B = eye(A), alpha./beta` is `spec(A)`). It is usually represented as the pair (alpha,beta), as there is a reasonable interpretation for beta=0, and even for both being zero.

[alpha,beta,R] = spec(A,B)

  returns in addition the matrix `R` of generalized right eigenvectors of the pencil.

[al,be,L,R] = spec(A,B)

  returns in addition the matrix `L` and `R` of generalized left and right eigenvectors of the pencil.

[al,be,Z] = spec(A,E)

  returns the matrix `Z` of right generalized eigen vectors.

[al,be,Q,Z] = spec(A,E)

  returns the matrices `Q` and `Z` of right and left generalized eigen vectors.

For big full / sparse matrix, you can use the Arnoldi module.

## References

Matrix eigenvalues computations are based on the Lapack routines

- DGEEV and ZGEEV when the matrix are not symmetric,

- DSYEV and ZHEEV when the matrix are symmetric.

A complex symmetric matrix has conjugate offdiagonal terms and real diagonal terms.

Pencil eigenvalues computations are based on the Lapack routines DGGEV and ZGGEV.

## Real and complex matrices

It must be noticed that the type of the output variables, such as evals or R for example, is not necessarily the same as the type of the input matrices A and B. In the following paragraph, we analyse the type of the output variables in the case where one computes the eigenvalues and eigenvectors of one single matrix A.

- Real A matrix

  - Symetric

    The eigenvalues and the eigenvectors are real.

  - Not symmetric

    The eigenvalues and eigenvectors are complex.

- Complex A matrix

  - Symetric

    The eigenvalues are real but the eigenvectors are complex.

  - Not symmetric

    The eigenvalues and the eigenvectors are complex.

## Examples

    // MATRIX EIGENVALUES
    A=diag([1,2,3]);
    X=rand(3,3);
    A=inv(X)*A*X;
    spec(A)

    x=poly(0,'x');
    pol=det(x*eye()-A)
    roots(pol)

    [S,X]=bdiag(A);
    clean(inv(X)*A*X)

    // PENCIL EIGENVALUES
    A=rand(3,3);
    [al,be,R] = spec(A,eye(A));
    al./be
    clean(inv(R)*A*R)  //displaying the eigenvalues (generic matrix)
    A=A+%i*rand(A);
    E=rand(A);
    roots(det(A-%s*E))   //complex case

## See Also

- poly
- det
- schur
- bdiag
- colcomp
- dsaupd
- dnaupd