Laszlo Zsolt Nagy wrote:
> Hello,
>
> I would like to use a numerical solver for a specific problem. My
> problem looks like this:
>
> 1. I have numeric constants, named A,B,C etc.
> 2. I have numeric variables, named x,y,z etc.
> 3. I have functions, like f1(x), f2(x), f3(x,y), f4(y) etc.
> 4. I have constraints like f1(x) < A f3(x,y) < B etc.
>
> Fortunately, all of the variables can be limited to a closed finite
> interval. (E.g. 0 <= x <= 100)
> There is a specific function, called P(x,y,z) that needs to be optimized
> (need to find its maximum/minimum).
In [7]: scipy.optimize.fmin_cobyla?
Type: function
Base Class: <type 'function'>
String Form: <function fmin_cobyla at 0x4fff3b0>
Namespace: Interactive
File:
/Library/Frameworks/Python.framework/Versions/2.4/lib/python2.4/site-packages/scipy-
0.4.7.1607-py2.4-macosx-10.4-ppc.egg/scipy/optimize/cobyla.py
Definition: scipy.optimize.fmin_cobyla(func, x0, cons, args=(),
consargs=None, rhobeg=1.0, rhoen
d=0.0001, iprint=1, maxfun=1000)
Docstring:
Minimize a function using the Contrained Optimization BY Linear
Approximation (COBYLA) method
Arguments:
func -- function to minimize. Called as func(x, *args)
x0 -- initial guess to minimum
cons -- a sequence of functions that all must be >=0 (a single function
if only 1 constraint)
args -- extra arguments to pass to function
consargs -- extra arguments to pass to constraints (default of None means
use same extra arguments as those passed to func).
Use () for no extra arguments.
rhobeg -- reasonable initial changes to the variables
rhoend -- final accuracy in the optimization (not precisely guaranteed)
iprint -- controls the frequency of output: 0 (no output),1,2,3
maxfun -- maximum number of function evaluations.
Returns:
x -- the minimum
--
Robert Kern
[EMAIL PROTECTED]
"In the fields of hell where the grass grows high
Are the graves of dreams allowed to die."
-- Richard Harter
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