Re: Generalised bisection
- From
- Ealdwulf Wuffinga <ealdwulf@googlemail.com>
- Date
- Mar 13, 2009, 09:58 UTC
- Message-ID
- <efe2b6d70903130258t2594b027m5812e9a5895f477e@mail.gmail.com>
- In-Reply-To
- <43d8ce650903112345x3d40b70ap7e4c0f8c7d0b6069@mail.gmail.com>
On Thu, Mar 12, 2009 at 6:45 AM, John Tapsell <johnflux@gmail.com> wrote:
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> 2009/3/11 Ealdwulf Wuffinga <ealdwulf@googlemail.com>: >> On Wed, Mar 11, 2009 at 9:35 AM, John Tapsell <johnflux@gmail.com> wrote: >> What I use is the multiprecision floating point number class. doubles >> don't seem to be long enough. > > Hmm, really really? Sometimes this sort of thing can be fixed by just > readjusting the formulas. What formulas are you using that require > more precision than doubles?
I'll have to reply to this later when I have more time. However, there is a (rather verbose) file in the doc directory which describes them - in texmacs format, but I've just uploaded a pdf version as well. It is BayesianSearch_Debugging.pdf. The description of this code starts in section 2.2 (since I wrote that, I have generalised it to the DAG case as in git).
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> A little bit of math trickery helps here :-) > > y = x^b > > log(y) = log(x^b) = b * log(x) > e^log(y) = e^(b log(x)) > > y = exp(b * log(x)) > > So as long as you have 'exp' and 'log' functions, you can raise x to > the power of b, even if b is fractional.
Sadly gmp does not have log or exp. mpfr does, but it does not have a python interface.
Alex