Timeline for Does this problem have an “exact” solution?
Current License: CC BY-SA 4.0
10 events
when toggle format | what | by | license | comment | |
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Jul 17, 2021 at 5:01 | comment | added | stats_noob | can you please take a look at this question later if you have some time? stackoverflow.com/questions/68386199/… thank you | |
Jul 17, 2021 at 4:52 | vote | accept | stats_noob | ||
Jul 17, 2021 at 4:52 | comment | added | stats_noob | btw: why did you include a penalty term? | |
Jul 17, 2021 at 4:52 | comment | added | stats_noob | @bdeonovic: thank you so much for your answer! I have attempted to solve this question using several different optimization algorithm - tomorrow I will post the code for all of these! thanks so much! | |
Jul 14, 2021 at 17:33 | comment | added | bdeonovic |
Oh, I initially added Inf to the objective if the minimum number of points was not in each region but optim requires the initial parameters to have finite objective so penalty was just a more tapered approached to getting minimum number of points in each region
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Jul 14, 2021 at 17:08 | comment | added | Sycorax♦ |
Oh, I didn't mean my comment to suggest that you did anything wrong (I meant to +1 earlier), or even should attempt to solve it differently, I just meant to be explicit about the role of penalty .
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Jul 14, 2021 at 17:06 | comment | added | bdeonovic | I used a single-objective optimization since it was easy to implement (essentialy this is linear scalarizing of a multi-objective optimization with equal weights. If you have some reason to favor one region of the others you can weight them different in the objective function. | |
Jul 14, 2021 at 16:29 | comment | added | Sycorax♦ | Perhaps you could clarify that because this is a multi-objective optimization task, there is no single best answer -- instead, one must define a Pareto frontier, as you have done. | |
Jul 14, 2021 at 16:05 | history | edited | bdeonovic | CC BY-SA 4.0 |
added 272 characters in body
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Jul 14, 2021 at 15:41 | history | answered | bdeonovic | CC BY-SA 4.0 |