[SciPy-dev] Best interface for computing the logarithm of the determinant?
josef.pktd@gmai...
josef.pktd@gmai...
Tue Feb 16 17:04:44 CST 2010
On Tue, Feb 16, 2010 at 4:29 PM, <josef.pktd@gmail.com> wrote:
> On Tue, Feb 16, 2010 at 4:19 PM, Dag Sverre Seljebotn
> <dagss@student.matnat.uio.no> wrote:
>> Nathaniel Smith wrote:
>>> So when you have a matrix whose determinant you want, it's often wise
>>> to compute the logarithm of the determinant instead of the determinant
>>> itself, because determinants involve lots and lots of multiplications
>>> and the result might otherwise underflow/overflow. Therefore, in
>>> scikits.sparse, I'd like to provide an API for doing this (and this is
>>> well-supported by the underlying libraries).
>>>
>>> But the problem is that for a general matrix, the determinant may be
>>> zero or negative. Obviously we can deal with this, but what's the best
>>> API? I'd like to use one consistently across the different
>>> factorizations in scikits.sparse, and perhaps eventually in numpy as
>>> well.
>>>
>>> Some options:
>>>
>>> 1) Split off the sign into a separate return value ('sign' may be 1, -1, 0):
>>> sign, value = logdet(A)
>>> actual_determinant = sign * exp(value)
>>>
>>> 2) Allow complex/infinite return values, even when A is a real matrix:
>>> logdet(eye(3)) == pi*1j
>>> logdet(zeros((3, 3))) == -Inf
>>
>> I'm +1 for this one. It is easy to interpret, and if one knows that the
>> result is positive (like after a successful CHOLMOD LL^T) is is easy
>> enough to take the real part by appending .real.
>>
>> Also it makes more sense for code that may be dealing with both complex
>> and real matrices compared to 1).
>
> I'm also in favor of this, but would prefer if promotion to complex
> only occurs if the result requires it.
> np.real_if_close might be safer than .real
>
> Josef
>
>>
>>>
>>> 3) "Scientific notation" (This is what UMFPACK's API does): return a
>>> mantissa and base-10 exponent:
>>> mantissa, exponent = logdet(A)
>>> actual_determinant = mantissa * 10 ** exponent
>>
>> This is my least preferred option, because "10" has nothing to do with
>> floats.
>>
>>>
>>> 4) Have separate functions for computing the sign, and the log of the
>>> absolute value (This is what GSL does, though it seems pointlessly
>>> inefficient):
>>> sign = sgndet(A)
>>> value = logdet(A)
>>> actual_determinant = sign * exp(value)
>>
>> Well, it doesn't have to be inefficient, you use cache the return value
>> internally on id(A) and possibly weak reference callbacks to remove the
>> cached values...but pretty ugly, yeah.
An alternative would be to provide 2 public functions where logdet()
would have the same behavior as np.log(np.det()) and any convenient
representation for an internal functions.
for multivariate normal distribution (loglikelihood) logdet would be
very useful. Is it possible to get an efficient implementation in
scipy or are the functions only available in CHOLMOD ?
Josef
>>
>> --
>> Dag Sverre
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>
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