- date: 2018-01-13
- revised: 2018-01-19
- status: incomplete
This page accompanies Math 428: Numerical Methods as taught by Lyudmyla Barannyk (Spring 2018 through the University of Idaho). I dragged this course well into the summer. Notebook here.
- Brian Bradie’s A Friendly Introduction to Numerical Analysis.
- Bruce E. Shapiro’s Scientific Computation
- Philip N. Klein’s Coding the Matrix
- Hal Abelson’s Structure and Interpretation of Computer Programs
- Barannyk’s Supplement on Numerical Linear Algebra
- Demanet, Laurent. “Syllabus Introduction to Numerical Analysis Mathematics MIT OpenCourseWare”. Retrieved April 1, 2018.
- “List of numerical analysis topics - Wikipedia”. English Wikipedia. Retrieved April 1, 2018.
Algorithm design and interpretation
Here’re notes from a survey of SICP for designing and interpreting algorithms.
I referenced the open courseware version of MIT 6.001.
I loaded joeltg’s mit-scheme-kernel into a jupyter notebook for REPL‘n.
(Seems that SICP is a canonical starting point for folks with some math background. I’m thinking about the course as a complement to numerical analysis. I want to have a high level view of computing if I’m going to grind away implementing numerical methods.)
- precise vocabulary to describe algorithms
historical context of Python as a descendent lisp dialect?
- I’d heard of Scheme as both a parent and child language; I think I will focus on the features that are implemented also in Python. Will I discover that Python is a descendant or cognate?
- prescriptive knowledge
- Paul Graham’s A Hundred Year Language
- Philip Guo’s How I learned programming
- Matt Might’s quick pitch for Scheme
- Matthew Butterick’s presentation at racketcon on Pollen, a typesetting system similar to XML, built on Racket
- If Pollen can export to XML, can it be configured to generate interactive manuscripts with PreTeXt?