[SciPy-user] Partial Derivatives
Alan G Isaac
aisaac at american.edu
Fri Feb 25 21:29:37 CST 2005
On Fri, 25 Feb 2005, "R. Padraic Springuel" apparently wrote:
> Is there a way to use the "derivative" function to take a partial
Is scipy.optimize.approx_fprime serviceable here?
I notice it fails to account for
epsilon != (x+espilon)-x
so the line
grad[k] = (apply(f,(xk+ei,)+args) - f0)/epsilon
should I believe be changed to
grad[k] = (apply(f,(xk+ei,)+args) - f0)/((xk[k]+epsilon)-xk[k])
to improve accuracy.
Of course this still uses 'apply' which is deprecated since
Python 2.3. (I don't claim to know if this is a good idea,
but it reputedly slows things down:
Also, just out of curiosity since we're here:
why the strange argument order (function 2nd)
for the gradient (and hessian) here?
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