Kas Posted October 29, 2011 Report Share Posted October 29, 2011 Is there a way of speeding up the attached VI. Its just unrealisticly slow. It can't even estimate 1 point, let alone 1000. I also posted this message in NI Forum, but no replies there. This issue is related to the following post. http://forums.ni.com...ht/true#M611414 Thanks Kas New folder.zip Quote Link to comment
Kas Posted October 30, 2011 Author Report Share Posted October 30, 2011 Found the Attached solution. I made it much easier to follow and run the attached VI. However, this simplex method keeps giving me the "maximum Iteration exeeded" error. I choose the initial values quite close to the original. One main thing that I am not sure about this algorithm is that how would the algorithm know if the 2 variables are the correct values. On the Lev-Mar algorithm, you have a dependant input of "Y" at which the 2 variables are compared against the f(x) which is Y. but I don't see this input in Simplex algorithm. Kas Quote Link to comment
asbo Posted October 30, 2011 Report Share Posted October 30, 2011 Even with the linked NI thread, you're going to need to explain a lot more about what you're doing or break a simpler example before people start diving into help. I understand that you're heavily invested in this problem, but even people with the know how to help aren't getting paid and have no vested interested to spend time on this. In the first ZIP, at least, I don't remember seeing much in the way of commenting. Quote Link to comment
Kas Posted October 30, 2011 Author Report Share Posted October 30, 2011 Hello asbo. You're right about what you said. Attached is the modified Zip file that includes a published paper from where this algorithm comes from and the "Main.VI" contains some comments where I tried to explain what I'm doing and why. Just for referance, the Model Function "Nelder-Mead" VI is correct. I've compared it against some previous script code and the results for chosen values of "n" and "k" were correct. Further more, this worked when I used the "Lev-Mar" fitting method (follow the lonk to the NI forum) but I had to abandon that algorithm since it depends on the gradiant or derivitive of the Model Function. In my case, this is not possible because my model function is based on Discrete values and no derivitive can be precisely found. As such, only the Simplex methodology is the option, where it does not depend on the derivitives. Thank you Kas Quote Link to comment
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