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| author | Jaron Kent-Dobias <jaron@kent-dobias.com> | 2021-10-25 14:24:49 +0200 |
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| committer | Jaron Kent-Dobias <jaron@kent-dobias.com> | 2021-10-25 14:24:49 +0200 |
| commit | a6b68b216a35268bb0bc6bf853ba38b7f1e3db0e (patch) | |
| tree | c5ce61bcb3ba70c9ab4e1f8b65637478f12fbf96 | |
| parent | d0d604e5fa0d06fc4dc899f3af9ede3ec9421b80 (diff) | |
| download | paper-a6b68b216a35268bb0bc6bf853ba38b7f1e3db0e.tar.gz paper-a6b68b216a35268bb0bc6bf853ba38b7f1e3db0e.tar.bz2 paper-a6b68b216a35268bb0bc6bf853ba38b7f1e3db0e.zip | |
More fully describe the fit.
| -rw-r--r-- | ising_scaling.tex | 4 |
1 files changed, 3 insertions, 1 deletions
diff --git a/ising_scaling.tex b/ising_scaling.tex index 035c190..594588b 100644 --- a/ising_scaling.tex +++ b/ising_scaling.tex @@ -564,7 +564,9 @@ parametric form evaluated at the same points, $\theta=0$ and $\theta=\theta_0$, weighted by the uncertainty in the value of the known coefficients or by a machine-precision cutoff, whichever is larger. We also add the difference between the predictions for $A_\mathrm{YL}$ and $\xi_\mathrm{YL}$ and their -known numeric values, again weighted by their uncertainty. +known numeric values, again weighted by their uncertainty. In order to +encourage convergence, we also add to the cost the weighted coefficients +$j!g_j$ and $j!G_j$. A Levenberg--Marquardt algorithm is performed on the cost function to find a parameter combination which minimizes it. As larger polynomial order in the |