https://github.com/cvxgrp/n-queens
Science Score: 26.0%
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Low similarity (7.3%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: cvxgrp
- Language: Python
- Default Branch: main
- Size: 26.7 MB
Statistics
- Stars: 2
- Watchers: 3
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
$n$-Queens Constant
This repo accompanies the paper Computing Tighter Bounds on the n-Queens Constant via Newton's Method.
To get started with the code, clone this repo, and run src/compute_upper_bound.py
and src/compute_lower_bound.py to reproduce the results from the paper.
The code
requires that scipy and numpy are installed.
Both scripts can be called as:
compute_upper_bound.py <n> [--write_sol]
compute_lower_bound.py <n> [--write_sol]
Where <n> is a mandatory argument specifying the size of the chessboard used
to construct the problem. By default compute_upper_bound.py solves n=1024
and compute_lower_bound.py solves n=2048. The --write_sol flag will write
an .npz file containing the witnesses for the generated solution to the
working directory.
To verify the closed-form expressions used for the integrals, see
src/problem_formulations/generate_closed_form_xqueenon_integral.sage.
You will need Sage Math to generate the expressions.
We also provide witnesses for the bounds given in the paper, at src/witnesses.
For the upper bound problem, we provide the x for which f(x) = U_1024.
For the lower bound problem, we provide the v for which L(v) = L_2048.
To generate bounds from the witnesses run src/upper_bound_from_witness.py and
src/lower_bound_from_witness.py.
To test the scripts against your own witnesses they can be called as follows:
upper_bound_from_witness.py <path/to/witness.npz> <n>
lower_bound_from_witness.py <path/to/witness.npz> <n>
Where <n> is the size of the chessboard used to generate the witness file.
Owner
- Name: Stanford University Convex Optimization Group
- Login: cvxgrp
- Kind: organization
- Location: Stanford, CA
- Website: www.stanford.edu/~boyd
- Repositories: 102
- Profile: https://github.com/cvxgrp
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