---
title: Numerical Methods
author: Colton Grainger
revised: 2018-01-19
status: incomplete
---

This page accompanies [Math 428: Numerical Methods](http://www.webpages.uidaho.edu/~barannyk/Teaching/Math428.html) as taught by Lyudmyla Barannyk (Spring 2018 through the University of Idaho). I dragged this course well into the summer. [Notebook here](https://nbviewer.jupyter.org/github/coltongrainger/fy18num/).

Course text

- Brian Bradie's [A Friendly Introduction to Numerical Analysis](https://www.amazon.com/Friendly-Introduction-Numerical-Analysis/dp/0130130540).

More links

- Bruce E. Shapiro's [Scientific Computation](http://calculuscastle.com/pythonbook.html) 
- Philip N. Klein's [Coding the Matrix](http://codingthematrix.com/)
- [Hal Abelson](https://en.wikipedia.org/wiki/Hal_Abelson)'s [Structure and Interpretation of Computer Programs](https://mitpress.mit.edu/sicp/full-text/book/book.html)
- Barannyk's [Supplement on Numerical Linear Algebra](https://web.archive.org/web/20180401003517/http://www.webpages.uidaho.edu/~barannyk/Teaching/math471_NumLinAlgebra.pdf)
- Demanet, Laurent. [“Syllabus  Introduction to Numerical Analysis  Mathematics  MIT OpenCourseWare”](https://ocw.mit.edu/courses/mathematics/18-330-introduction-to-numerical-analysis-spring-2012/syllabus/). Retrieved April 1, 2018.
- [“List of numerical analysis topics - Wikipedia”](https://en.wikipedia.org/wiki/List_of_numerical_analysis_topics#Numerical_linear_algebra). 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](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-001-structure-and-interpretation-of-computer-programs-spring-2005/index.htm). 

I loaded joeltg's [mit-scheme-kernel](https://github.com/joeltg/mit-scheme-kernel) into a jupyter notebook for [REPL](https://en.wikipedia.org/wiki/Read%E2%80%93eval%E2%80%93print_loop)'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](http://www.paulgraham.com/hundred.html)
- Philip Guo's [How I learned programming](http://www.pgbovine.net/how-i-learned-programming.htm)
- Matt Might's [quick pitch](http://matt.might.net/articles/best-programming-languages/#scheme) for Scheme
- Matthew Butterick's [presentation at racketcon](https://www.youtube.com/watch?v=IMz09jYOgoc) on [Pollen](http://docs.racket-lang.org/pollen/), a typesetting system similar to XML, built on [Racket](https://en.wikipedia.org/wiki/Racket_(programming_language)) 
- If Pollen can export to XML, can it be configured to generate interactive manuscripts with [PreTeXt](http://mathbook.pugetsound.edu/index.html)?