| While I can't give the exact prerequisites, I know that all of the things that appear in the paper relate to: (1) Linear Algebra (2) Optimization Theory (Convex Analysis, non-convex optimization) [0], [2] (3) Probability Theory and Statistics (Measure Theory, Multivariate Statistics) [1], [3], [4], [5] (4) Analysis, to a lesser extent. (2) and (3) are the most important. I would give more references, but my background is too theoretical (and my field is Numerical Analysis of PDE). From the classes I took in college, three or four on each of (1-4), a person with a similar background can recognize the tools without much digging. Maybe some folks here can provide some insights into books that center on applications. So I'm trying not to diverge into too much theory (i.e. for measures, [4] instead of Folland). There also seems to be good use of Analysis techniques in the paper, see theorem 2.1. I love that the paper references the Moore-Penrose pseudo-inverse, an object of study in both statistics and optimization for which I had to give a lecture for a course. [0] https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf Convex Optimization, Boyd and Vandenberghe [1] An Introduction to Multivariate Statistical Analysis, Anderson [2] Convex Analysis and Monotone Operator Theory in Hilbert Spaces, Bauschke-Combettes [3] Theory of Multivariate Statistics, Bilodeau-Brenner [4] The Elements of Integration and Lebesgue Measure, Bartle [5] Probability: Theory and Examples, Durrett |