SigOpt works best at optimizing systems where evaluating a single outcome is time-consuming and expensive. SigOpt can effectively optimize machine learning systems, complex simulations, and in-depth manufacturing and industrial processes.
If evaluating a single system configuration is cheap enough to do 100,000s or millions of times, then SigOpt may be overly robust for the problem; in these cases, algorithms like traditional Design of Experiments, simulated annealing, particle swarm, or genetic algorithms may be more suitable.
SigOpt has the largest impact on problems with 2-20 parameters and expensive measurement processes. If your function has hundreds or thousands of parameters, we recommend employing dimensionality reduction techniques and then using SigOpt, or using one of the alternative techniques suggested above if you can perform millions of evaluations to find an optimum.