Kevin Deldycke

See: * https://www.chessprogramming.org/Magic_Bitboards * https://news.ycombinator.com/item?id=16860317

A dynamic programming solution to the n-queens problem

3

http://www.cs.cornell.edu/~rdz/Papers/RZ-IPL92.pdf https://news.ycombinator.com/item?id=13276975

See: https://github.com/rmcgibbo/python-appveyor-conda-example

Looks like there is a way to auto-deploy releases to PyPi, as per: * https://github.com/jaraco/skeleton/blob/746dd7999f9db23276144ee2160920bd01ed860c/.travis.yml#L13-L23 * https://docs.travis-ci.com/user/deployment/pypi/

...to gather software and hardware [metadata in benchmark](https://github.com/kdeldycke/chessboard/blob/v1.5.3/chessboard/benchmark.py#L89-L112). See: https://boltons.readthedocs.io/en/latest/ecoutils.html#profile-generation

This is a big feature that is unlikely to be addressed soon, as Numpy doesn't support PyPy yet. Still, work seems in progress. See: - http://buildbot.pypy.org/numpy-status/latest.html - https://bitbucket.org/pypy/numpy - https://github.com/rvianello/conda-pypy

Exploit symetries

1

All pieces are moveable in the same fashion in the 4 quadrants. Exploit vertical and horizontal symmetries to reduce the search space.

See: - https://en.wikipedia.org/wiki/Space-filling_curve - https://en.wikipedia.org/wiki/Hilbert_curve - https://en.wikipedia.org/wiki/Z-order_curve

Current solver only works on a single core. Leverage all cores from the CPU.