Dear all,
I would like to describe in my dissertation how the vensim optimization option (calibration) works in technical terms (not going into great detail, but providing transparency on this).
In particular, I am interested how the payoff function is calculated, what rule(s) decide that Vensim stops searching for better solution, what happens in the existence of several optimal local optimum and how the size of the marginal changes (in the selected parameters that may vary) are determined? I haven't found a reference where these questions are clearly answered and an explanation is provided.
Many thanks for any answers/explanations or useful references! (there has been a previous discussion on this, but the indicated reference is not available any more i.e. the webpage doesn't open).
Very best,
Sarah
Vensim optimization / Powell’s “Hill Climbing” algorithm
Re: Vensim optimization / Powell’s “Hill Climbing” algorithm
It's fairly close to the Numerical Recipes version:
http://www.nrbook.com/a/bookcpdf.php
Powell's method (and any other hill climber) will find only a single local optimum, unless you do a grid or random variation of the starting point. Then the probability of discovery of additional optima is proportional to size of their basin within the search space.
Simulated annealing does guarantee discovery of a global optimum if your cooling schedule is logarithmic, which usually requires a completely impractical amount of time. However, it can be an interesting experiment, because a stochastic search can also avoid other pathologies, like getting stuck in a narrow twisting valley. https://metasd.com/2018/05/optimization-banana-death/
http://www.nrbook.com/a/bookcpdf.php
Powell's method (and any other hill climber) will find only a single local optimum, unless you do a grid or random variation of the starting point. Then the probability of discovery of additional optima is proportional to size of their basin within the search space.
Simulated annealing does guarantee discovery of a global optimum if your cooling schedule is logarithmic, which usually requires a completely impractical amount of time. However, it can be an interesting experiment, because a stochastic search can also avoid other pathologies, like getting stuck in a narrow twisting valley. https://metasd.com/2018/05/optimization-banana-death/
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Re: Vensim optimization / Powell’s “Hill Climbing” algorithm
Sara,
I understand it has been a while since you posted (I hope your dissertation went well) but I now have a similar question. The numerical recipes reference is not producing pdfs, I can't find the specific algorithm in a book, etc. I have access to the David-Fletcher-Powell method but is a derivative based search. So, it must be different.
Did you have any luck finding information on Powell's hill climbing algorithm? If so, I would appreciate a reference or link.
Thank you,
Brian
I understand it has been a while since you posted (I hope your dissertation went well) but I now have a similar question. The numerical recipes reference is not producing pdfs, I can't find the specific algorithm in a book, etc. I have access to the David-Fletcher-Powell method but is a derivative based search. So, it must be different.
Did you have any luck finding information on Powell's hill climbing algorithm? If so, I would appreciate a reference or link.
Thank you,
Brian
Re: Vensim optimization / Powell’s “Hill Climbing” algorithm
It looks like NR has moved to http://numerical.recipes/
Any technical university library will surely have a copy.
Any technical university library will surely have a copy.
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