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Scientific Computing in Finance

Math-GA 2048, New York University, Spring 2015

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Course Materials

The lecture slides are in HTML format, which requires javascript enabled browser to render properly. All lecture slides are generated using IPython notebook, click the link ipynb to view the source IPython notebook. You can also download all the source ipython notebooks and related Python codes from http://github.com/yadongli/nyumath2048

Lecture slides and homework will be published weekly before the Wednesday lecture.

Lecture Materials
1. Introduction lecture slides (ipynb), homework
2. Linear Algebra I lecture slides (ipynb), homework
3. Linear Algebra II lecture slides (ipynb), homework
4. Rootfinding & Interpolation lecture slides (ipynb), homework
5. Deltas and Hedging lecture slides (ipynb), homework
6. Monte Carlo lecture slides (ipynb), homework
7. Variance Reduction lecture slides (ipynb), no homework
8. Optimization lecture slides (ipynb)
9. Linear programming lecture slides (ipynb), homework
11. ODE lecture slides (ipynb)
12. PDE I lecture slides (ipynb)
13. PDE II lecture slides (ipynb), homework
10. Entropy & Allocation lecture slides (ipynb), homework

Python setup

Python is the primary programming tool for this class. The following is a step by step instruction on how to set up the right Python environment for the class.

Python setup instructions

How to save/print slides as PDF

Similar steps should also work in other browsers, as long as you already have a PDF printer. The latest Chrome browser supports "Save as PDF" out of box.

Disclaimer

All course materials and codes are provided as-is, without any warranty for completeness or correctness.

Authors

@yadongli @hcheng66

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