A few days ago, I was asked what the variational method is, and I found my previous post, Variational Method for Optimization, barely explain some basic of variational method. Thus, I would do it in this post. Data concerned in machine learning are ruled by physics of informations. It sounds quite abstract, so I will present an example of dynamic mechanics. Let us consider a ball thrown with velocity v=($v_x$, $v_y$) at x = (x, y), and under the vertical gravity with constant g.
I announce over and over that the chronicle ordering of the post are irrelevant for beginners’ favor. There are many blanks I skipped. I would fill the holes later. Variational method During my physics coursework and researches, I used this method countlessly. I even had a book of the name. It is quite simple, but also as big topic as being a book. Simply put, it is a technique to find equations and solutions (sometimes approximate solutions) by extremizing functionals which is mainly just integrals of fields, and treat the functions in the integral, as parameters.