However I’m not sure that even this PR would solve my specific usage of many calls to optimize.minimize_scalar with objective functions parametrized slightly differently for each.


Function call overhead is notoriously large in Python, but optimizations are usually very expensive as well.

Is the function call overhead really the bottleneck here?

-CHB



 

Victor Poughon

 

De : SciPy-User [mailto:scipy-user-bounces+victor.poughon=cnes.fr@python.org] De la part de Guillaume Gay
Envoyé : samedi 17 février 2018 16:05
À : scipy-user@python.org
Objet : Re: [SciPy-User] Minimize many scalar functions

 

Hi Victor,

I guess it depends a lot on the scalar functions and if they can be vectorized... But you can always optimize on an array of inputs with optimize.minimize, can't you?

A more explicit example would help us help you I guess.

 

Best

 

Guillaume

 

 

Le 16/02/2018 à 17:16, Poughon Victor a écrit :

Hi all,

 

I'd like to minimize hundreds of thousands of scalar valued functions. Is there a faster way than a for-loop over a def of the objective function and a call to scipy.optimize.minimize_scalar? Basically a vectorized version, where I could give a function foo and a 2D numpy array where each row is given as extra data to the objective function.

 

Thanks,

 

Victor Poughon

 




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