EIGS  EIGS Sparse Matrix Eigendecomposition


Usage
 
 Computes the eigendecomsition of a sparse square matrix.  The
 eigs function has several forms.  The most general form is
 
   [V,D] = eigs(A,k,sigma)
 
 where A is the matrix to analyze, k is the number of
 eigenvalues to compute and sigma determines which eigenvallues
 to solve for.  Valid values for sigma are
    'lm' - largest magnitude 
    'sm' - smallest magnitude
    'la' - largest algebraic (for real symmetric problems)
    'sa' - smallest algebraic (for real symmetric problems)
    'be' - both ends (for real symmetric problems)
    'lr' - largest real part 
    'sr' - smallest real part
    'li' - largest imaginary part
    'si' - smallest imaginary part
  scalar - find the eigenvalues closest to sigma.
 The returned matrix V contains the eigenvectors, and D
 stores the eigenvalues.  The related form
 
    d = eigs(A,k,sigma)
 
 computes only the eigenvalues and not the eigenvectors.  If sigma
 is omitted, as in the forms
 
   [V,D] = eigs(A,k)
 
 and
 
   d = eigs(A,k)
 
 then eigs returns the largest magnitude eigenvalues (and optionally
 the associated eigenvectors).  As an even simpler form, the forms
 
   [V,D] = eigs(A)
 
 and
 
   d = eigs(A)
 
 then eigs returns the six largest magnitude eigenvalues of A and
 optionally the eigenvectors.  The eigs function uses ARPACK to
 compute the eigenvectors and/or eigenvalues.  Note that due to a 
 limitation in the interface into ARPACK from FreeMat, the number of
 eigenvalues that are to be computed must be strictly smaller than the
 number of columns (or rows) in the matrix.
 