[SciPy-User] expanding optimize.brentq

Charles R Harris charlesr.harris@gmail....
Mon Mar 18 13:58:20 CDT 2013

On Mon, Mar 18, 2013 at 11:13 AM, <josef.pktd@gmail.com> wrote:

> Just a thought, given the problems with fsolve
> In scipy.stats.distribution, we use an expanding brentq, which looks
> like it works very well.
> https://github.com/scipy/scipy/pull/216/files#L0R1175
> A question given that I have not much experience with root finders:
> Is there a general algorithm that has similar properties?
> In these application we know that the function is strictly monotonic,
> but we don't have a bound for brentq without checking. For some cases
> I know one of the bounds (e.g. 0 for non-negative solutions).
> (I could use that right now as a replacement for fsolve.)
I assume you are talking about the 1-D case where you don't have bounding
intervals to start with. There are various search methods that can be used
to find such intervals, but they can't be guaranteed to work. They all come
down to making trials of various points looking for sign reversals, and the
searches are either subdividing a larger interval or creating larger and
larger intervals by, say, doubling each time around. The addition of such
methods would be useful, but they need a failure mode :(

Your application looks to be finding points on a monotone function on the
interval [0, inf]. For that you could use the interval [0, 1] and map it to
[0, inf] with the substitution x/1-x, although the point x == 1 will be a
bit tricky unless your function handles inf gracefully. The accuracy will
also tend to be relative rather than absolute, but that seems to be a given
in any case. I suppose this approach falls under the change of variables
method. Those tend to be problem specific, so I don't know if they would be
a good fit for scipy, but certainly they could work well in specialized
domains. It would also seem to be a good idea to match the asymptotic
behavior if possible, which will tend to linearize the problem. So other
options would be functions like log, erf, arctan, etc, for the
substitution, but in those cases you probably already have the the inverse

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