Pointer is the worlds most powerful optimization engine library, combining the best optimization methods and techniques available today, in a single product.

Automatic Optimization

Pointer The meta-optimizer is a specialized genetic algorithm that automatically controls the tweaking parameters of a set of core algorithms. The core algorithms that Pointer uses are: Linea simplex, NLPQL, Nealder & Mead downhill simplex, Genetic algorithms based on evolution, Monte-Carlo. All these algorithms have control parameters (such as initial step sizes) that need to be set properly in order to operate most efficiently.

Usually it is not possible for the user to identify the design space topology in such a way that he/she can select the proper algorithms and settings. A real-life example is shown to the left. The topology is that of the drag on an airfoil as a function of is shape. From experience, the user assumed that the drag was a smooth function of the geometry. From the starting point, a small step was selected to calculate the gradients accurately. Because the topology of the drag has surface waves the steepest downhill slop was considered to be along this wave. If he had picked al larger minimum step size he would have concluded that the direction normal to the waves has the relevant steepest downhill slope.