I'm trying to understand a paper called "Supersparse Linear Integer Models for Predictive Scoring Systems" by Ustun, Tracà and Rudin, who introduce a really interesting method for generating an understandable classification system that actually challenges machine learning models for binary decisions in at least some areas. According to the authors, they use a mixed-integer problem (MIP), whose objective penalizes the training error (of which I do not what it is), the $L_0$-norm and the $L_1$-norm (of which I know the definition in regular Linear Algebra cases, but not in this instance) of its coefficients.
In their formulation, they use an optimization in the form of:
$$\min_\lambda\frac{1}{N}\sum_{i = 1}^{N}\mathbb{1}[y_1x_1^\top\lambda \leq 0] + C_0\|\lambda\|_0+C_1\|\lambda\|_1$$
which produces a classifier $\hat y = \operatorname{sgn}(x^\top\lambda)$ where $x \in \mathbb{R}^P$ is a vector of features (with $P$ being number of features), $\lambda \in \mathbb{Z}^P$ being number of coefficients and $\hat y \in \{-1,1\}$ being the predicted labels. Also within the formula, $C_0$ and $C_1$ are referred to as penalties, and the paper continues by listing an MIP with $N + 3P$ variables and $2N + 6P$ constraints, which I could also have listed, but didn't, since I only wanted to give an idea of what I will have to work with.
Now, the reason I even posted all this here, is not because I'm expecting an explanation of the research, but rather because I'd be really happy if anybody could provide me with information on what topics I will have to deal with and whether anyone with knowledge on this can tell if I should even bother trying to understand it.
I'm a second-year physics and third-year psychology student with basic knowledge in Statistics, Linear Algebra, and Analysis (we got introduced to multivariate analysis some while ago and were talking about Lagrange Multipliers and more general integrals (Lebesgue, Darboux) in the last lessons).
Regarding the paper, I now started out by looking at optimization problems, for which I found some very easy and comprehensible examples, and then looked at examples of Linear-Programming Problems, which I understand to be optimization problems in which the function domain has been constrained, for example by some inequalities.
I really want to apologize for the vast and general character of my question and understand that there probably is no simple solution to this and do not want to upset anyone with it, but I'm rather just looking for general information on whether these types of problem are graspable for someone with basic understanding in math and what type of material I should read/watch/work out. Any help is greatly appreciated.
If this is the wrong forum to ask this question, please let me know.