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To solve CCP problems, the stochastic optimization problem is often relaxed into an equivalent deterministic problem. There are different approaches depending on the nature of the problem:
* '''Linear CCP''': For linear systems, the feasible region is typically convex, and the problem can be solved using [[linear programming]] techniques.
* '''Nonlinear CCP''': For nonlinear systems, the main challenge lies in computing the probabilities and their gradients. These problems often require [[
* '''Dynamic Systems''': Dynamic systems involve time-dependent uncertainties, and the solution approach must account for the propagation of uncertainty over time.<ref name=pu/>
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