OSE Lab: Computational Methods Section Materials

This directory in the repository contains all the materials for the computational methods section of the OSE Lab Boot Camp.

Prerequisite and tutorial resources

We expect students in the Boot Camp to be jumping into Python at a level beyond an absolute beginner. Some Great resources for getting to that point include the following Jupyter Notebooks in the Tutorials folder of this repository.

1. PythonReadIn.ipynb. This Jupyter notebook provides instruction on basic Python I/O, reading data into Python, and saving data to disk.
2. PythonNumpyPandas.ipynb. This Jupyter notebook provides instruction on working with data using NumPy as well as Python's powerful data library pandas.
3. PythonDescribe.ipynb. This Jupyter notebook provides instruction on describing, slicing, and manipulating data in Python.
4. PythonFuncs.ipynb. This Jupyter notebook provides instruction on working with and writing Python functions.
5. PythonVisualize.ipynb. This Jupyter notebook provides instruction on creating visualizations in Python.
6. PythonRootMin.ipynb. This Jupyter notebook provides instruction on implementing univariate and multivariate root finders and unconstrained and constrained minimizers using functions in the scipy.optimize sub-library.

We also recommend the "Intro to Python" lab from Brigham Young University's Math Department's Applied and Computational Math Emphasis (ACME) as well as the "An Introductory Example" and "Python Essentials" lectures from QuantEcon.

Schedule

The computational methods lab sessions for the OSE Lab will usually be held from 8:00-11:50am, Tuesday and Thursday in Saieh Hall, Room 247. The lab files in the schedule under the "Materials column" that start with "ACME" come from the Brigham Young University Math Department's Applied and Computational Math Emphasis (ACME program). These computational labs are open source. We cover only a subset of these excellent applied math Python labs, which are available in their entirety at http://www.acme.byu.edu/2017-2018-materials/. We highly recommend that you take time after the Boot Camp to work through some of the other labs that are available to you.

Week 1

Date	Day	Topic	Instructor	Materials	Problem Set
7-1	M 8-10a	Numerical Differentiation	Jan Ertl	ACME: Numerical Differentiation	Comp Problem Set 1a
7-2	T 8a-12p	Python Intro		ACME: Intro to NumPy	Comp Problem Set 1b
				ACME: Standard Library	due M, 7-8, 11pm
				ACME: Data Visualization
				ACME: Intro to Matplotlib
7-3	W 8-10a	Numerical Integration	Jan Ertl	Evans integration notebook
7-4	Th			No Class - Holiday
7-5	F 8a-12p	Newton's method, Python		ACME: Newton's Method
				ACME: Object Oriented Programming
				ACME: Exceptions and File I/O

Week 2

Dynamic Structural Economics Summer School by the Econometric Society

• Schedule, July 8, 9, 12, 13, 14

Dynamic Structural Economics Conference by the Econometric Society, ""

• Schedule, July 10, 11

Week 3

Note that Simon Scheidegger has created a separate high performance computing repository for the OSE Lab at https://github.com/sischei/OSE2019.

Date	Day	Topic	Instructor	Materials	Problem Set
7-15	M
7-16	T 8a-12p	Sparse grids	Simon Scheidegger	Sparse Grids intro	Comp. Prob. Set 2a
				Accessing supercomputer	due M, 7-22, 11pm
				Sparse grids tools and code
				Sparse grids dynamic programming
7-17	W
7-18	Th 8a-12p	High performance computing	Simon Scheidegger	HPC intro	Comp. Prob. Set 2b
				C++ primer	due M, 7-22, 11pm
				Parallel proc, OpenMP
				3 Projects
7-19	F

Week 4

Note that Simon Scheidegger has created a separate high performance computing repository for the OSE Lab at https://github.com/sischei/OSE2019.

Date	Day	Topic	Instructor	Materials	Problem Set
7-22	M
7-23	T 8a-12p	OpenMP and MPI	Simon Scheidegger	OpenMP II	Comp. Prob. Set 3a
				MPI 1	Comp. Prob. Set 3b
				MPI 2	due M, 7-29, 11pm
7-17	W
7-18	Th 8a-12p	High performance computing	Simon Scheidegger	MPI 3	Comp. Prob. Set 3c
				Hybrid Parallel 1	due M, 7-29, 11pm
				Hybrid Parallel 2	due M, 7-29, 11pm
				High Throughput
				Advanced HPC topics
7-19	F

Week 5

Date	Day	Topic	Instructor	Materials	Problem Set
7-29	M
7-30	Tu
7-31	W
8-1	Th 8-noon	Pandas	Rebekah Dix, Jan Ertl	Pandas 1	Comp. Prob. Set 4
				Pandas 2	due M, 8-5, 11pm
				Pandas 3
				Pandas 4
8-2	F 8-noon		Rebekah Dix, Jan Ertl	ACME: Conditioning and Stability
				ACME: Iterative Solvers
				ACME: Quasi-Newton Method

References

• Humpherys, Jeffrey, "Computational Labs for Foundations of Applied Mathematics" (2017).