Non derivative methods

Optimization methods that do not require any derivative of the function are not usually applied to stationary points in molecular systems. Although they are generally easy to implement, their convergence properties are rather poor. They may work well in special cases when the function is quite random in character or the variables are essentially uncorrelated. Some examples of these methods are the Simplex, Genetic Algorithms, Neural Networks and Simulated Annealing.