681 pages; Quantitative and Qualitative Treatments to Capital Markets; Notes on Abstract Linear Algebra (PART V)
Preview of Notes
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Toc:
Last week, we studied operators on the inner product space, investigating practically important linear maps such as self-adjoint, positive semidefinite and isometric operators, as well as techniques such as polar decomposition and singular value decomposition useful in computational algebra.
This week is the investigation of complex and real vector spaces, and we cover important topics such as Jordan forms, characteristic polynomials and more. Additionally, we went back to the previous section to include some examples on useful concepts such as the SVD on non-full rank rectangular matrices, right/left inverses, which allows us to introduce a very useful matrix known as the pseudo-inverse.
There is just one more post in the abstract linear algebra series. We will add advanced and numerical linear algebra in the future, but for now we will kick off and gain momentum in the coding series, where we implement powerful quant tools in Python - we have started this off with a post on alpha-encoding data structures:
The next post will be our regular formulaic alpha, and then we will follow this up with the post with Python code implementing the alpha encoding data structures.
Lecture Notes: (paid)