SelfAvoidingWalk

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SelfAvoidingWalk

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Spring 2018 Monte Carlo project at ENSAE: simulation of self-avoiding random walks

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# Spring 2018 Monte Carlo project at ENSAE

This repository includes the code, report and slides for the final project of the course *Simulation and Monte Carlo*.
I was tasked with simulating self-avoiding random walks. The report starts with a quick survey on the properties of such walks, and then confronts rejection sampling, weighted importance sampling and the pivot algorithm. All the simulations were run in the 2-dimensional integer lattice, although the methods adapt readily to higher dimensions.

## Files description
* `SAW.ipynb` contains all the explanations, results and code (in English)
* `slides.pdf` contains the oral presentation (in French)

## Disclaimer
If you want to look at the notebook, please refrain from using the integrated Github viewer, as the HTML and Latex code do not render properly.

If you want to run the code, make you sure you have the latest version of `matplotlib` and `numpy`installed (2.2.2 and 1.14 respectively at the time of writing).

## Final grade
19/20

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