Thanks for your suggestion. I've now running a worksheet using your
method to minimize a large polynomial. So far, minimize() has not yet
returned.
One minor question: in your definition of the gradient function,
sage: gradfun = lambda x:np.array(map(lambda f:f(*x),
eq.gradient()))
should that be 'eq.gradient()' or should it be 'req.gradient()' ?
(recall that req = eq.change_ring(RDF))
thanks,
Daniel
Daniel Friedan
On Jul 13, 2:05 pm, Harald Schilly <[email protected]> wrote:
> On Jul 13, 3:07 pm, 8fjm39j <[email protected]> wrote:
>
> > Any help would be much appreciated.
>
> I'm not sure if problems of this size work. Also, you should add the
> gradient to the minimize method. Here is a snippet that might help
> you. You do not need the SR as far as i can see.
>
> sage: RQ = PolynomialRing(QQ, 30, 'x', sparse=True)
> sage: RQ.inject_variables()
> Defining x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13,
> x14, x15, x16, x17, x18, x19, x20, x21, x22, x23, x24, x25, x26, x27,
> x28, x29
> sage: eq = sum([ (v + random())^2 for v in RQ.gens() ])
> sage: req = eq.change_ring(RDF)
> sage: import numpy as np
> sage: gradfun = lambda x:np.array(map(lambda f:f(*x), eq.gradient()))
> sage: minimize(lambda x : req(*x), [0]*req.parent().ngens(),
> gradient=gradfun)
> Optimization terminated successfully.
> Current function value: 0.000000
> Iterations: 2
> Function evaluations: 4
> Gradient evaluations: 4
> (-0.55788239962, -0.0493798356231, -0.593303577877, -0.339802652733,
> -0.00394559417147, -0.178836124785, -0.343306688157, -0.126282234205,
> -0.642885679398, -0.27541451953, -0.689213436111, -0.41996375463,
> -0.602566339938, -0.626694430444, -0.771426488128, -0.0283310587547,
> -0.913384222525, -0.128570101865, -0.75252338794, -0.834385792852,
> -0.658475228648, -0.266546504385, -0.683600111652, -0.063955541513,
> -0.790083400019, -0.0634933885369, -0.136504640143, -0.978047564451,
> -0.743009613932, -0.276400559549)
>
> H
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