58 A Novel Method for Unconstrained Multivariate Optimization Based on Fletcher Reeves Theory
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There are some methods for optimization problems, they differ in the way the reach the optimum, among these methods, those which are based on the function's gradient have a great advantage as they find the fastest way to reach de objective, here we show three methods that base on this principle, two of them are part of our course, and a third one which we would like to propose, as it turns out to be very effective.
Through this research we achieve to implement and built a serial of algorithms that recreate the steps from mathematical structures design for solving the many challenging optimization issues that are found in an engineering career. Based on our theory, seen on this course, and several extra sources we were provided with tools strong enough to understand and rebuilt such logic. The processes and results are exposed in this journal. Not only are we going to solve a proposed example, but also we're going to show how three different methods based on the same primitive concept can differ in quality, accuracy and speed.