Suppose I have an objective function f(x) that is non-convex (x can be a vector), but once I add constraints it becomes a convex problem. To show what i mean, consider this trivial example: let f(x) = cos(x). Clearly, cos(x) is not convex, but if i only consider x in [0, pi/2], then the function is convex when restricting x to these values.
CVXPY does not accept such a problem because it does not satisfy DCP rules. One option, in the previous example, is to minimize f(x) + g(x) where g is an indicator function such that: g(x) = 0 for x in [0, pi/2] and g(x) = +infinity otherwise, but I don't know how to implement that in CVXPY. What can i use in Python to take advantage of the "convexity" of the problem?
Thanks.
