Computing Eigenvalues without balancing in Python using the NAG library

December 17th, 2013 | Categories: Linear Algebra, matlab, NAG Library, python | Tags:

In a recent Stack Overflow query, someone asked if you could switch off the balancing step when calculating eigenvalues in Python. In the document A case where balancing is harmful, David S. Watkins describes the balancing step as ‘the input matrix A is replaced by a rescaled matrix A* = D-1AD, where D is a diagonal matrix chosen so that, for each i, the ith row and the ith column of A* have roughly the same norm.’  

Such balancing is usually very useful and so is performed by default by software such as  MATLAB or Numpy.  There are times, however, when one would like to switch it off.

In MATLAB, this is easy and the following is taken from the online MATLAB documentation

A = [ 3.0     -2.0      -0.9     2*eps;
     -2.0      4.0       1.0    -eps;
     -eps/4    eps/2    -1.0     0;
     -0.5     -0.5       0.1     1.0];

[VN,DN] = eig(A,'nobalance')
VN =

    0.6153   -0.4176   -0.0000   -0.1528
   -0.7881   -0.3261         0    0.1345
   -0.0000   -0.0000   -0.0000   -0.9781
    0.0189    0.8481   -1.0000    0.0443

DN =
    5.5616         0         0         0
         0    1.4384         0         0
         0         0    1.0000         0
         0         0         0   -1.0000

At the time of writing, it is not possible to directly do this in Numpy (as far as I know at least). Numpy’s eig command currently uses the LAPACK routine DGEEV to do the heavy lifting for double precision matrices.  We can see this by looking at the source code of numpy.linalg.eig where the relevant subsection is

lapack_routine = lapack_lite.dgeev
        wr = zeros((n,), t)
        wi = zeros((n,), t)
        vr = zeros((n, n), t)
        lwork = 1
        work = zeros((lwork,), t)
        results = lapack_routine(_N, _V, n, a, n, wr, wi,
                                  dummy, 1, vr, n, work, -1, 0)
        lwork = int(work[0])
        work = zeros((lwork,), t)
        results = lapack_routine(_N, _V, n, a, n, wr, wi,
                                  dummy, 1, vr, n, work, lwork, 0)

My plan was to figure out how to tell DGEEV not to perform the balancing step and I’d be done. Sadly, however, it turns out that this is not possible. Taking a look at the reference implementation of DGEEV, we can see that the balancing step is always performed and is not user controllable–here’s the relevant bit of Fortran

*     Balance the matrix
*     (Workspace: need N)
      IBAL = 1

So, using DGEEV is a dead-end unless we are willing to modifiy and recompile the lapack source — something that’s rarely a good idea in my experience. There is another LAPACK routine that is of use, however, in the form of DGEEVX that allows us to control balancing.  Unfortunately, this routine is not part of the numpy.linalg.lapack_lite interface provided by Numpy and I’ve yet to figure out how to add extra routines to it.

I’ve also discovered that this functionality is an open feature request in Numpy.

Enter the NAG Library

My University has a site license for the commercial Numerical Algorithms Group (NAG) library.  Among other things, NAG offers an interface to all of LAPACK along with an interface to Python.  So, I go through the installation and do

import numpy as np
from ctypes import *
from nag4py.util import Nag_RowMajor,Nag_NoBalancing,Nag_NotLeftVecs,Nag_RightVecs,Nag_RCondEigVecs,Integer,NagError,INIT_FAIL
from nag4py.f08 import nag_dgeevx

eps = np.spacing(1)

def unbalanced_eig(A):
    Compute the eigenvalues and right eigenvectors of a square array using DGEEVX via the NAG library.
    Requires the NAG C library and NAG's Python wrappers
    The balancing step that's performed in DGEEV is not performed here.
    As such, this function is the same as the MATLAB command eig(A,'nobalance')

    A : (M, M) Numpy array
        A square array of real elements.

        On exit: 
        A is overwritten and contains the real Schur form of the balanced version of the input matrix .

    w : (M,) ndarray
        The eigenvalues
    v : (M, M) ndarray
        The eigenvectors

    Author: Mike Croucher (
    Testing has been mimimal

    order = Nag_RowMajor
    balanc = Nag_NoBalancing
    jobvl = Nag_NotLeftVecs
    jobvr = Nag_RightVecs
    sense = Nag_RCondEigVecs

    n = A.shape[0]
    pda = n
    pdvl = 1

    wr = np.zeros(n)
    wi = np.zeros(n)

    pdvr = n
    vr = np.zeros(pdvr*n)


    scale = np.zeros(n)
    abnrm = c_double(0)
    rconde = np.zeros(n)
    rcondv = np.zeros(n)

    fail = NagError()

               n, A.ctypes.data_as(POINTER(c_double)), pda, wr.ctypes.data_as(POINTER(c_double)),
               vr.ctypes.data_as(POINTER(c_double)),pdvr,ilo,ihi, scale.ctypes.data_as(POINTER(c_double)),
               abnrm, rconde.ctypes.data_as(POINTER(c_double)),rcondv.ctypes.data_as(POINTER(c_double)),fail)

    if all(wi == 0.0):
            w = wr
            v = vr.reshape(n,n)
            w = wr+1j*wi
            v = array(vr, w.dtype).reshape(n,n)


Define a test matrix:

A = np.array([[3.0,-2.0,-0.9,2*eps],

Do the calculation

(w,v) = unbalanced_eig(A)

which gives

(array([ 5.5616,  1.4384,  1.    , -1.    ]),
 array([[ 0.6153, -0.4176, -0.    , -0.1528],
       [-0.7881, -0.3261,  0.    ,  0.1345],
       [-0.    , -0.    , -0.    , -0.9781],
       [ 0.0189,  0.8481, -1.    ,  0.0443]]))

This is exactly what you get by running the MATLAB command eig(A,’nobalance’).

Note that unbalanced_eig(A) changes the input matrix A to

array([[ 5.5616, -0.0662,  0.0571,  1.3399],
       [ 0.    ,  1.4384,  0.7017, -0.1561],
       [ 0.    ,  0.    ,  1.    , -0.0132],
       [ 0.    ,  0.    ,  0.    , -1.    ]])

According to the NAG documentation, this is the real Schur form of the balanced version of the input matrix.  I can’t see how to ask NAG to not do this. I guess that if it’s not what you want unbalanced_eig() to do,  you’ll need to pass a copy of the input matrix to NAG.

The IPython notebook

The code for this article is available as an IPython Notebook

The future

This blog post was written using Numpy version 1.7.1. There is an enhancement request for the functionality discussed in this article open in Numpy’s git repo and so I expect this article to become redundant pretty soon.

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