[SciPy-user] Questions about scipy.optimize.fmin_cobyla
Mon Jul 16 08:16:30 CDT 2007
> firstname.lastname@example.org wrote:
>> 2) In function f(X), there are item of log(xi). Although I have
>> constrained the value of xi in the constrain functions, xi still may be
>> equal or less than 0 during minimization, then "math domain error"
>> What should I do ?
> I would replace xi and log(xi) by exp(xi) and xi
> You can try also free (BSD lic) OpenOpt solver lincher, it should handle
> your problem well
> something like
> from scikits.openopt import NLP
> p = NLP(f, x0, lb = zeros(x0.size))
> r = p.solve('lincher')
> however, currently lincher requires QP solver, and the only one
> connected is CVXOPT one, so you should have cvxopt installed (GPL). BTW
> cvxopt is capable of solving your problem, it has appropriate NLP solver
> Also, take a look at tnc
> - afaik it allows to handle lb - ub bounds
cvxopt can only handle convex functions, but my function is too
complicated to get the expression of derivate easily, so according to
a previous post from Joachim Dahl(email@example.com) in this list,
it is probably non-convex.
Can openopt handle non-convex functions?
My function is too complex to compute derivate, so I can't use tnc to do
>> My method is when "math domain error" occurred, catch it and set the
>> return value of f to a very large number. Should this work or not?
> This will not work with any NLP or Nonsmooth solver, that calls for
> gradient/subgradient this will work for some global solvers like
> anneal, but they are capable of small-scaled problems only (nVars up
> to 10) HTH, D.
The number of variables is less than 15, does this make any sense?
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