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35 lines
1.8 KiB
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35 lines
1.8 KiB
Markdown
# ShfitedPowGMRES
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> Report of the project: [view](https://github.com/lukefleed/ShfitedPowGMRES/blob/main/tex/main.pdf) / [download](https://github.com/lukefleed/ShfitedPowGMRES/raw/main/tex/main.pdf)
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This repository contains the code of my attempt to replicate the results obtained in `[1]`. The scripts are all written in python and are heavily build around the libraries SciPy and NumPy. To install all the required packages with `pip` run the following command in terminal
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```bash
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pip install -r requirements.txt
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```
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At the moment, the standard and shifted power method to compute the PageRank with multiple damping factors are fully implemented (as described in `[1]`). To run the program we need to execute the `main.py` file. It takes as input two arguments:
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- `--dataset`: the options are `BerkStan` and `Stanford`. This commands selects the web-graph to run the algorithms on.
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- `--algo`: the options are `power`, `shifted`, `both`. If you choose the last option, it will first run the standard power method and then the shifted one.
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Here an example of what's described above.
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```bash
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sudo chmod +x main.py
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```
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Now we can run the program
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```bash
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./main.py --dataset Stanford --algo both
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```
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## Under development
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In the `testing/` folder there are two python notebook that contains the attempt on replicating the results obtained in `[1]` for the shifted GMRES method. The implementation of the Arnoldi process is fully working. On the other hand, there are several problems on the shifted GMRES algorithm that I can't figure out.
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## References
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`[1]` _Zhao-Li Shen, Meng Su, Bruno Carpentieri, and Chun Wen. Shifted power-gmres method accelerated by extrapolation for solving pagerank with multiple damping factors. Applied Mathematics and Computation, 420:126799, 2022_ |