Reduce memory required for Fourier transform. #95
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pulquero
commented
Mar 19, 2021
| @@ -171,7 +171,7 @@ def compute_fourier_basis(self, n_eigenvectors=None): | |||
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| # TODO: handle non-symmetric Laplacians. Test lap_type? | |||
| if n_eigenvectors == self.n_vertices: | |||
| self._e, self._U = np.linalg.eigh(self.L.toarray()) | |||
| self._e, self._U = linalg.eigh(self.L.toarray(order='F'), overwrite_a=True) | |||
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order='F' as lapack is fortran ordered, and re-use space for eigenvectors.
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Thanks! Does the Fortran order make the EVD faster?
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It should avoid the matrix being transposed from C order to F order when it is pushed out to lapack.
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Makes sense, thanks. At some point we should do a proper benchmark and find the best settings for the partial and full EVDs.
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Rebased to fix CI. |
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Thanks for the improvement! |
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Fixes #94 .