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License: Apache License 2.0
Files for 2017 inverse modeling course at UvA
License: Apache License 2.0
this is one of the most relevant topics for students, but currently we cover it only with lectures.
manual calibration (respsurf.m)
Gauss-Newton and Levenberg-Marquardt (Run_Optimization.m)
affects exercises 6 through 12
code:
Currently it's 1-D and that makes it more difficult to see that it is just another benchmark function.
currently we aggregate two sources of information into one and optimize on that, but it's better to also show how to make a pareto front
manual calibration assumes d=60 (text, mancal_sluginj.m, respsurf.m)
Gauss-Newton and Levenberg-Marquardt assume d=10 (Exercise1.m)
affects exercises 6 through 12
I think they are the optima (and standard deviation) of T and S calculated based on SSR residuals (as in Burt & Barber example), but it's not mentioned anywhere that I can see.
The caption talks about the standard deviation being 0.01 [m], which is information that you need to calculate the magic numbers.
for finding just the optimum, it is kind of OK, but for the metropolis part, the incorrect sampling really affects the shape of the distribution. This can be avoided by setting F equal to 0 (disabling the first distance, dist1
).
We should update the text, snippets, and pictures in the syllabus, as well as the scripts in /exercises and in /solutions
Currently, there are some differences between our DiffEvo version (which is based on Jasper's homework2 assignment), and what Storn and Price describe. For example,
inverse-modeling-2017/syllabus/tex/chapters/introduction.tex
Lines 13 to 14 in a346aa0
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