Introduces renormalized Hermite Polynomials #108
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@@ Coverage Diff @@
## master #108 +/- ##
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+ Coverage 97.74% 97.95% +0.21%
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Files 12 12
Lines 885 978 +93
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+ Hits 865 958 +93
Misses 20 20
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This PR addresses #86 |
| for (int i = 1; i < n; i++) | ||
| x[i] = x[i-1]*base; | ||
| eg::Matrix<T, eg::Dynamic, eg::Dynamic> R = eg::Map<eg::Matrix<T, eg::Dynamic, eg::Dynamic>, eg::Unaligned>(R_mat.data(), dim, dim); | ||
| eg::Matrix<T, eg::Dynamic, eg::Dynamic> y = eg::Map<eg::Matrix<T, eg::Dynamic, eg::Dynamic>, eg::Unaligned>(y_mat.data(), dim, dim); |
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Just out of curiousity, do these two variables R and y need to be eigen matrices? I scanned through the code below, and couldn't see a spot that uses eigen methods (unless I missed it)
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You are correct. Eigen is not used here, at all.
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Might get even faster then by avoiding this conversion 😆
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This might be worth checking...
Co-Authored-By: Josh Izaac <josh146@gmail.com>
Co-Authored-By: Josh Izaac <josh146@gmail.com>
Co-Authored-By: Josh Izaac <josh146@gmail.com>
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Hey Josh --- The new functions reuse the tests from the old functions so no new test are needed. IN a way I am just replacing existing functionality with much better implementations. |
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| for (int i = 0; i < res; i++) | ||
| expected_re[i*res+i] = pow(0.5, static_cast<double>(i)/2.0); | ||
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| out = libwalrus::hermite_multidimensional_cpp(B, d, res, renorm); | ||
| out = libwalrus::renorm_hermite_multidimensional_cpp(B, d, res); |
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here we test the new renorm_hermite_multidimensional_cpp
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Exactly! For example all the test in |
This PR removes the extra parameter renorm from the
hermite_multimendional_cppand instead introduce a newrenorm_hermite_multimendional_cpp. These new polynomials are given by rH^B_n(y) = H^B_n(y)/\sqrt(n!). Instead of calculating the ratio of H^B_n(y) and \sqrt(n!) the renormalized polynomials use a modified recursion relation that avoids overflows. At the same the renormalized polynomials are faster to calculate than the regular polynomials + dividing by factorials thus speeding upstate_vector,density_matrix,fock_tensorand all the functions infock_gradients.