[SciPy-User] [ANN] Guaranteed solution of nonlinear equation(s)

[Dmitrey]

   --- Исходное сообщение ---
   От кого: "Yury V. Zaytsev" <yury at shurup.com>
   Кому: "SciPy Users List" <scipy-user at scipy.org>
   Дата: 25 мая 2011, 10:25:08
   Тема: Re: [SciPy-User] [ANN] Guaranteed solution of nonlinear
   equation(s)



     Hi Dmitrey,
     
     On Tue, 2011-05-24 at 13:22 +0300, Dmitrey wrote:
     
     > For more info see      http://forum.openopt.org/viewtopic.php?id=423     
     1) Are there any scientific publications detailing the algorithm that
     can be referenced from a paper?
        

   not yet.

      2) Sorry, I am not sure if I've got it right from the examples. 
     
     Is it true that your implementation of interalg is impossible to use
     outside of OpenOpt the way one would, for example, use fmin from SciPy?
        

   As it is mentioned in http://openopt.org/interalg , only FuncDesigner
   models can be handled (because interval analysis is required).
   However, you could easily compare scipy optimize fmin and other
   openopt-connected solvers with interalg.

      I am currently using downhill simplex because that's almost the only
     algorithm that does not diverge or get stuck for my non-linear problem
     and my objective function can't be expressed as a simple algebraic
     expression of elementary functions.
     
     Computing it actually involves summing over series of exponential
     integrals and other nasty things, which require tight loops so I wrote a
     Python extension is C to speed it up.
     
     I can pass fmin any function, is there a way to do the same for
     interalg? If not, are there plans to implement it or how much effort
     would this involve?
        

   From all your questions, it seems you haven't read interalg webpage.
   Currently only these funcs are supported:
   +, -, *, /, pow (**), sin, cos, arcsin, arccos, arctan, sinh, cosh,
   exp, sqrt, abs, log, log2, log10, floor, ceil
   Future plans: 1-D splines , min, max
   Also, any monotone func R->R (or with known "critical points", where
   order of monotonity changes) can be easily connected.
   Regards, D.
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