Deterministic global optimization
Deterministic global optimization methods require ways to rigorously bound function values over regions of space.For this reason, it is common for problems in deterministic global optimization to be represented using a computational graph, as it is straightforward to overload all operators such that the resulting function values or derivatives yield interval (rather than scalar) results.The reason is that, with the rise of interior-point algorithms, it is possible to efficiently solve very large problems (involving hundreds of thousands or even millions of variables) to global optimality.Efficient algorithms for solving complex problems of this type are known and are available in the form of solvers such as CPLEX.The order of magnitude that a modern solver can be expected to handle in reasonable time is roughly 100 to a few hundreds of non-linear variables.