Numerical Methods
 date: 20180113
 revised: 20180119
 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.
Course text
 Brian Bradie’s A Friendly Introduction to Numerical Analysis.
More links
 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 mitschemekernel 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.)
goals
 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
see also
 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?