[SciPy-user] basic usage of fmin_tnc and fmin_l_bfgs_b

[Gilles Rochefort]

physeco a écrit :
> I'm new to multidimensional optimization with scipy.  Sorry for asking the
> simple question, but I can't figure out the syntax for fmin_tnc and
> fmin_l_bfgs_b.  Here's a simple example of the error I'm getting:
>
> Executing:
>   
>> def f(x):                                                                
>>     
> 	return (x[0]*x[1]-1)**2+1
>   
>> g=0.1,0.1
>> b=[(-10,10),(-10,10)]
>> so.fmin_tnc(f,g,bounds=b)
>>     
>
> Leads to the error:
> ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------
> /usr/lib/python2.5/site-packages/scipy/optimize/tnc.py in fmin_tnc(func, x0,
> fprime, args, approx_grad, bounds, epsilon, scale, offset, messages,
> maxCGit, maxfun, eta, stepmx, accuracy, fmin, ftol, xtol, pgtol, rescale)
>     244     rc, nf, x = moduleTNC.minimize(func_and_grad, x0, low, up,
> scale, offset,
>     245             messages, maxCGit, maxfun, eta, stepmx, accuracy,
> --> 246             fmin, ftol, xtol, pgtol, rescale)
>     247     return array(x), nf, rc
>     248 
>
> /usr/lib/python2.5/site-packages/scipy/optimize/tnc.py in func_and_grad(x)
>     203         def func_and_grad(x):
>     204             x = asarray(x)
> --> 205             f, g = func(x, *args)
>     206             return f, list(g)
>     207     else:
>
> <type 'exceptions.TypeError'>: 'numpy.float64' object is not iterable
> ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------
>
> I get a similar error from so.fmin_l_bfgs_b(f,g,bounds=b).  If you can point
> out my mistake, it would be greatly appreciate.
>
> Thank you!
>   
Hi, function to be minimized is supposed to return both function value 
and gradient.
You can eventually provide a gradient function separately.

If you do not want or do not have a gradient function to provide, you 
can always turn to True the approx_gradient
 argument, which compute a numerical approximation of your function.

To take back your example :

fmin_l_bfgs_b(f,g,approx_grad=True, bounds=b)

(array([ 0.99999789,  0.99999789]),
 1.0000000000178644,
 {'funcalls': 8,
  'grad': array([ -8.45989945e-06,  -8.45989945e-06]),
  'nbiter': 4,
  'task': 'CONVERGENCE: NORM OF PROJECTED GRADIENT <= PGTOL',
  'warnflag': 0})

fmin_tnc works the same as fmin_l_bfgs_b.

Gilles.


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