836 pages; Applications of Convex Optimization; Quantitative and Qualitative Treatments to Capital Markets
..preview..
..
In the last market notes, we studied duality in convex optimization:
This week, we take a look at the applications of convex optimization, looking at problem instances of norm approximation, least-norm, regularised and robust approximation problems, as well as parametric and non-parametric estimation methods.
We will add a few more notes on the algorithms for convex optimization, and then bring this topic to a close. We will continue with the beginner to advanced programming series for Python quant devs, and then try to implement portfolio optimization methods.
Table of Contents and Preview:
Full Notes (paid):