minimize_constrained for a one dimensional function

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Hello all,

I have a bit of a problem with the minimize_constrained function: When I call

minimize_constrained(lambda x: x^2, [lambda x: 1], 99.6)

I get a TypeError: iteration over a 0-d array (why?). If I call

minimize_constrained(lambda x: x^2, \
    [lambda x: vector({0:1,1:x}).inner_product(vector((0,0)))], 99.6)

I get a TypeError: unable to find a common ring for all elements. This seems to be the case because the the constraint function is sometimes called with a numpy.ndarray and at other times with a sage.modules.vector_real_double_dense.Vector_real_double_dense. In the first case I need to call float(x[0]), in the latter I can use x[0] directly.

Is it be possible to convert the data to sage vectors throughout the iterations or do I have to set up a handler to sanitize the input myself?