[SciPy-user] Questions about scipy.optimize.fmin_cobyla

fdu.xiaojf@gmai... fdu.xiaojf@gmai...
Mon Jul 16 09:00:36 CDT 2007

dmitrey wrote:
 > fdu.xiaojf@gmail.com wrote:
 >>  >
 >> 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(dahl.joachim@gmail.com) in this list,
 >> it is probably non-convex.
 >> Can openopt handle non-convex functions?
 > this is incorrect question. For any NLP solver (w/o global ones of
 > course) I can construct a non-convex func that will failed to be solved.
 > Moreover, I can construct *convex* func, which one will be very, very
 > hard to solve (for the solver chose). So I can only talk about "this
 > solver is more or less suitable for solving non-convex funcs".
 > Fortunately, lincher is rather suitable for solving non-convex funcs,
 > but let me remember you once again - it requires cvxopt qp solver yet,
 > no other ones are connected or are written (I hope till the end of
 > summer openopt will have it own QP/QPCP solver).


 >> My function is too complex to compute derivate, so I can't use tnc to do
 >> the job.
 > if derivatives are not provided to tnc, it will calculate that ones by
 > itself via finite-differences, as any other NLP solver.
Oh, yes, tnc can calculate derivatives if the parameter approx_grad is

 >>  >
 >>  >> 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?
 > Very unlikely.
 > 10 is already a big problem.
 >> The reason why I chose cobyla is that cobyla can handle inequality and 
equality constraints, and it doesn't require derivate information.
 > lincher is capable of handling equality constraints. However, there can
 > be some difficult cases (for example, when constraints form a
 > linear-dependent system in xk - point from iter k)
 > But for rather small nVars = 15 I guess no problems will be obtained.

Can linear avoid the "math domain error" prolbem in cobyla ? In my
function, there are items like log(xi*R*T/V) (R,T,V are positive
variables too).


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