[Numpy-discussion] Unrealistic expectations of class Polynomial or a bug?

Charles R Harris charlesr.harris@gmail....
Mon Jan 30 14:15:47 CST 2012

On Mon, Jan 30, 2012 at 6:55 AM, Charles R Harris <charlesr.harris@gmail.com
> wrote:

> On Sun, Jan 29, 2012 at 10:03 AM, eat <e.antero.tammi@gmail.com> wrote:
>> On Sat, Jan 28, 2012 at 11:14 PM, Charles R Harris <
>> charlesr.harris@gmail.com> wrote:
>>> On Sat, Jan 28, 2012 at 11:15 AM, eat <e.antero.tammi@gmail.com> wrote:
>>>> Hi,
>>>> Short demonstration of the issue:
>>>> In []: sys.version
>>>> Out[]: '2.7.2 (default, Jun 12 2011, 15:08:59) [MSC v.1500 32 bit
>>>> (Intel)]'
>>>> In []: np.version.version
>>>> Out[]: '1.6.0'
>>>> In []: from numpy.polynomial import Polynomial as Poly
>>>> In []: def p_tst(c):
>>>>    ..:     p= Poly(c)
>>>>    ..:     r= p.roots()
>>>>    ..:     return sort(abs(p(r)))
>>>>    ..:
>>>> Now I would expect a result more like:
>>>> In []: p_tst(randn(123))[-3:]
>>>> Out[]: array([  3.41987203e-07,   2.82123675e-03,   2.82123675e-03])
>>>> be the case, but actually most result seems to be more like:
>>>> In []: p_tst(randn(123))[-3:]
>>>> Out[]: array([  9.09325898e+13,   9.09325898e+13,   1.29387029e+72])
>>>> In []: p_tst(randn(123))[-3:]
>>>> Out[]: array([  8.60862087e-11,   8.60862087e-11,   6.58784520e+32])
>>>> In []: p_tst(randn(123))[-3:]
>>>> Out[]: array([  2.00545673e-09,   3.25537709e+32,   3.25537709e+32])
>>>> In []: p_tst(randn(123))[-3:]
>>>> Out[]: array([  3.22753481e-04,   1.87056454e+00,   1.87056454e+00])
>>>> In []: p_tst(randn(123))[-3:]
>>>> Out[]: array([  2.98556327e+08,   2.98556327e+08,   8.23588003e+12])
>>>> So, does this phenomena imply that
>>>> - I'm testing with too high order polynomials (if so, does there exists
>>>> a definite upper limit of polynomial order I'll not face this issue)
>>>> or
>>>> - it's just the 'nature' of computations with float values (if so,
>>>> probably I should be able to tackle this regardless of the polynomial order)
>>>> or
>>>> - it's a nasty bug in class Polynomial
>>> It's a defect. You will get all the roots and the number will equal the
>>> degree. I haven't decided what the best way to deal with this is, but my
>>> thoughts have trended towards specifying an interval with the default being
>>> the domain. If you have other thoughts I'd be glad for the feedback.
>>> For the problem at hand, note first that you are specifying the
>>> coefficients, not the roots as was the case with poly1d. Second, as a rule
>>> of thumb, plain old polynomials will generally only be good for degree < 22
>>> due to being numerically ill conditioned. If you are really looking to use
>>> high degrees, Chebyshev or Legendre will work better, although you will
>>> probably need to explicitly specify the domain. If you want to specify the
>>> polynomial using roots, do Poly.fromroots(...). Third, for the high degrees
>>> you are probably screwed anyway for degree 123, since the accuracy of the
>>> root finding will be limited, especially for roots that can cluster, and
>>> any root that falls even a little bit outside the interval [-1,1] (the
>>> default domain) is going to evaluate to a big number simply because the
>>> polynomial is going to h*ll at a rate you wouldn't believe ;)
>>> For evenly spaced roots in [-1, 1] and using Chebyshev polynomials,
>>> things look good for degree 50, get a bit loose at degree 75 but can be
>>> fixed up with one iteration of Newton, and blow up at degree 100. I think
>>> that's pretty good, actually, doing better would require a lot more work.
>>> There are some zero finding algorithms out there that might do better if
>>> someone wants to give it a shot.
>>> In [20]: p = Cheb.fromroots(linspace(-1, 1, 50))
>>> In [21]: sort(abs(p(p.roots())))
>>> Out[21]:
>>> array([  6.20385459e-25,   1.65436123e-24,   2.06795153e-24,
>>>          5.79026429e-24,   5.89366186e-24,   6.44916482e-24,
>>>          6.44916482e-24,   6.77254127e-24,   6.97933642e-24,
>>>          7.25459208e-24,   1.00295649e-23,   1.37391414e-23,
>>>          1.37391414e-23,   1.63368171e-23,   2.39882378e-23,
>>>          3.30872245e-23,   4.38405725e-23,   4.49502653e-23,
>>>          4.49502653e-23,   5.58346913e-23,   8.35452419e-23,
>>>          9.38407760e-23,   9.38407760e-23,   1.03703218e-22,
>>>          1.03703218e-22,   1.23249911e-22,   1.75197880e-22,
>>>          1.75197880e-22,   3.07711188e-22,   3.09821786e-22,
>>>          3.09821786e-22,   4.56625520e-22,   4.56625520e-22,
>>>          4.69638303e-22,   4.69638303e-22,   5.96448724e-22,
>>>          5.96448724e-22,   1.24076485e-21,   1.24076485e-21,
>>>          1.59972624e-21,   1.59972624e-21,   1.62930347e-21,
>>>          1.62930347e-21,   1.73773328e-21,   1.73773328e-21,
>>>          1.87935435e-21,   2.30287083e-21,   2.48815928e-21,
>>>          2.85411753e-21,   2.85411753e-21])
>> Thanks,
>> for a very informative feedback. I'll study those orthogonal polynomials
>> more detail.
> That said, I'm thinking it might be possible to get a more accurate
> polynomial representation from the zeros by going through a barycentric
> form rather than simply multiplying the factors together as is done now.
> Hmm...
> For evenly spaced roots the polynomial grows in amplitude rapidly at the
> ends which leads to numerical problems because a small error in the zeros
> turns into a large error in value because of the steepness of the curve at
> the zeroes. I've attached a semilogy plot of the absolute values of the
> polynomial with 30 equally spaced zeroes from -1 to 1.

I've attached a plot of the Chebyshev coefficients for the monic polynomial
with 50 zeros evenly spaced from -1, 1. The odd coefficients should be
zero, so their value tells you what the error in the coefficient
determination was (I used Gauss-Chebyshev integration). The value of the
resulting Chebyshev series cannot be evaluated with sufficient accuracy in
double precision due to the dynamic range of the coefficients and I expect
that simple inability of double precision to correctly represent the values
extends to the root finding.

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