On Jul 13, 3:07 pm, 8fjm39j <dfrie...@gmail.com> 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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