I'd like to run a minimization problem in several different dimensions, and then find a minimum over the results (for more information on the problem see here).
I can easily define both the objective function and the main constraint in a dimenson-independent way with a simple combination of map, sum, and prod:
d = 25
t = 0.75
f = lambda p: sum(map(lambda x: d^2 * tan(x * pi/2) + 8*d, p))
c1 = lambda p: 1 - prod(map(lambda q: 1 - q, p)) - t
On the other hand, I'm not sure on how to express the fact that every variable should be an element of the interval [a,b], where 0 < a < b < 1 are to be given beforehand. I thought I could use min and max like so:
a = 0.1
b = 0.95
lower_bound = lambda x: x - a
upper_bound = lambda x: b - x
c_lower = lambda p: min(map(lower_bound, p))
c_upper = lambda p: max(map(upper_bound, p))
but I'm worried that using those instead of a pair of constraints for every variable would have a negative effect on minimize_constrained.
