I'm studying Support Vector Machines in the machine learning course, I'm a computer scientist, I've quite understood how SVM are designed thanks also to 16. Learning: Support Vector Machines - MIT.
What I'm not understanding is the transition from the optimization problem of the Lagrangian function to its implementation in any programming language, in this moment I'm forgetting about already existing implementation because I want to understand this thing.
Basically, what I need is to understand how to built from scratch the decistion function, given a training set. The clue of the question is how do I find Lagrange multipliers in order to know which points are to be considered to define support vectors and the decision function?
Does anyone can explain this to me?