# Constraint Satisfaction Problem: Choosing real numbers with certain characteristics

I have a set of n real numbers. I also have a set of functions,

f_1, f_2, ..., f_m.


Each of these functions takes a list of numbers as its argument. I also have a set of m ranges,

[l_1, u_1], [l_2, u_2], ..., [l_m, u_m].


I want to repeatedly choose subsets {r_1, r_2, ..., r_k} of k elements such that

l_i <= f_i({r_1, r_2, ..., r_k}) <= u_i for 1 <= i <= m.


Note that the functions are smooth. Changing one element in {r_1, r_2, ..., r_k} will not change f_i({r_1, r_2, ..., r_k}) by much.

These are the m constraints that I need to satisfy.

Moreover I want to do this so that the set of subsets I choose is uniformly distributed over the set of all subsets of size k that satisfy these m constraints. Not only that, but I want to do this in an efficient manner. How quickly it runs will depend on the density of solutions within the space of all possible solutions (if this is 0.0, then the algorithm can run forever).

Note that n is large enough that I cannot brute-force the problem. That is, I cannot just iterate through all k-element subsets and find which ones satisfy the m constraints.

Is there a way to do this?

What sorts of techniques are commonly used for a CSP like this? Can someone point me in the direction of good books or articles that talk about problems like this (not just CSPs in general, but CSPs involving continuous, as opposed to discrete values)?

[Note that this question is a special case.]