Summary

In this chapter, we learned how to use different numerical optimization techniques to improve the solutions offered by a simulation model. We started by introducing the basic concepts of numerical optimization, defining a minimization problem, and learning to distinguish between local and global minimums. We then moved on and looked at the optimization techniques based on gradient descent. We defined the mathematical formulation of the technique and gave it a geometric representation. Furthermore, we deepened our knowledge of the concepts surrounding the learning rate and trial and error. By doing this, we addressed a practical case in order to reinforce the concepts we learned by solving the problem of searching for the minimum of a quadratic function.

Subsequently, we learned how to use the Newton-Raphson method to search for the roots of a function and then how to exploit the same methodology for numerical optimization. We also analyzed a practical case for this technology...