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| author | Paweł Redman <pawel.redman@gmail.com> | 2026-06-14 18:52:46 +0200 |
|---|---|---|
| committer | Paweł Redman <pawel.redman@gmail.com> | 2026-06-14 18:52:46 +0200 |
| commit | 8ce3589d02e14322fc9aa7fe647a6f4f66a4eb59 (patch) | |
| tree | 6e3b0989f171c7c14d8d6f3e432ea0571d1e2160 /site/content/gsoc/convergence_problems.typ | |
| parent | 89a67854f5af72836498a9412f759d4337fad32d (diff) | |
Fix typos in migrated GSoC posts
Co-authored-by: Cursor <cursoragent@cursor.com>
Diffstat (limited to 'site/content/gsoc/convergence_problems.typ')
| -rw-r--r-- | site/content/gsoc/convergence_problems.typ | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/site/content/gsoc/convergence_problems.typ b/site/content/gsoc/convergence_problems.typ index 320261a..8be6671 100644 --- a/site/content/gsoc/convergence_problems.typ +++ b/site/content/gsoc/convergence_problems.typ @@ -10,7 +10,7 @@ caption: [Solvers tend to get attracted to spectra like this and get stuck], ) -I've been trying to write code that find parameters to the reflectivity model I mentioned in #link-self("gsoc/nondimensionalization")[an earlier post]. The objective is to minimize the difference between the color given the model and the target. It turns out the optimization is rather difficult and all SciPy solvers (including global optimization algorithms) frequently fail. At the moment I only have a loose idea of what's going on. +I've been trying to write code that finds parameters to the reflectivity model I mentioned in #link-self("gsoc/nondimensionalization")[an earlier post]. The objective is to minimize the difference between the color given the model and the target. It turns out the optimization is rather difficult and all SciPy solvers (including global optimization algorithms) frequently fail. At the moment I only have a loose idea of what's going on. Because of the sigmoid function used in the model, very large coefficients create spectra resembling a rectangular or a step function (like on the picture). Then, it's always possible to adjust the coefficients to make the spectrum even steeper without changing its overall shape. After doing this enough times, coefficients are so large that varying them barely changes the spectrum, a situation solvers treat as having found a good minimum. I suppose it's because the gradient becomes very small, but a better-suited algorithm would look at the near-zero Hessian and notice it's moving along an asymptote (a bad minimum here). I'm also not sure why solvers are (sometimes) attracted to big numbers in the first place. |