[SciPy-user] optimize.fsolve

[Brett Olivier]

Hi

This is a simple example but perhaps it will help:

######

import scipy

x0 = 10.0
x2 = 0.0
init_val = [10,20]

def DE_explicit_ss(s):
    sdot = scipy.zeros((2),'d')
    sdot[0] = (10.0*x0 - s[0])-(5.0*s[0] - s[1])
    sdot[1] = (5.0*s[0] - s[1])-(s[1] - x2)
    return sdot

sres = scipy.optimize.fsolve(DE_explicit_ss, init_val)

print 'result'
print sres

######

"fsolve" is a wrapper around MINPACK's hybrd and hybrj algorithms.

From scipy/Lib/optimize/minpack/hybrd.f

c     the purpose of hybrd is to find a zero of a system of
c     n nonlinear functions in n variables by a modification
c     of the powell hybrid method. the user must provide a
c     subroutine which calculates the functions. the jacobian is
c     then calculated by a forward-difference approximation.

Brett

On Thursday 18 August 2005 15:14, Ryan Krauss wrote:
> Yes, I think it is doing exactly what Matlab would do.  You can have a
> vector input, you just need to have a scalar output that you are trying
> to drive to zero.
>
> Howey, David A wrote:
> > Thanks for this... interesting.
> >
> > What I'm trying to do eventually use fsolve to solve for two unknowns
> > (call them p and v) in two non-linear equations (call them F1, F2). In
> > matlab, one would define a vector function say myfunc returning a vector
> > F with F(1) (first element in the vector) evaluating to the first
> > equation and F(2) to the second. The function would accept a vector
> > input, say x, which is = [ p ;  v]. ie a vector of the two unknowns.
> >
> > Do you think scipy's fsolve can work on vectors (arrays) like this in the
> > same way?
> >
> > Dave
> >
> >
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-- 
Brett G. Olivier Ph.D.
Triple-J Group for Molecular Cell Physiology
Stellenbosch University
bgoli at sun dot ac dot za   http://glue.jjj.sun.ac.za/~bgoli
Tel +27-21-8082704  Fax +27-21-8085863  Mobile +27-82-7329306
 
I wouldn't be so paranoid if you weren't all out to get me!!