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On 01/22/2014 05:07 PM, Tobias Schmidt wrote:
>> It looks like you are basing the difference quotient on the last
>> value of x passed into the function ("x_pred"). I do not know if
>> this is a good idea.
>
> I assume that the step size is small enough to calculate the
> difference quotient backwards. Maybe this assumption is a bad idea
> (it was the first I had). Btw., I corrected the wrong calculation
> of the differences, but it does not run anyway. Your advice looks
> smart; I will try it that way.
I think the problem is that the gradient you return on the first
iteration - when there is no valid x_pred - is 0 (is that so? I have
not checked in depth).
Now imagine you are doing a Newton step based on a 0 gradient. You
would require an infinite step in some direction (the gradient is
actually in the denominator). Just look at the nice animation for the
1d-case here (and keep in mind that in Newton type methods, we are
actually looking for the root of the gradient):
http://en.wikipedia.org/wiki/Newton%27s_method
Best,
Julius
>> Another thing: is the equivalent of your objective function
>> myfun available in matlab? Have you checked if it is implemented
>> correctly?
>
> Yes, the objective function is equivalent to the objective function
> in the matlab code.
>
> I guess that at least one of the constraint functions causes the
> fault, but I do not know why!
>
> Regards, Tobias
>
> Am 22.01.2014 16:18, schrieb Julius Ziegler: On 01/22/2014 04:09
> PM, Tobias Schmidt wrote:
>>>> Oh, you're right. I forgot that. But even if I comment the
>>>> zeroconfun constraint, the fault occurs. The fault occurs if
>>>> _any_ of my constraint functions is added. Without these
>>>> functions the optimization returns successfully.
>
> Your way of computing the gradients looks strange. It looks like
> you are basing the difference quotient on the last value of x
> passed into the function ("x_pred"). I do not know if this is a
> good idea.
>
> Why dont you just calulate it like this (this is not working
> c-code, just for explanation). Use h "small", where small is
> dependent on your domain knowledge.
>
> const h = 0.0001;
>
> f0=myfun(x);
>
> for all i: x1=x; x1[i]+=h; f1=myfun(x1); grad[i]=(f1-f0)/h;
>
> return f0
>
> Another thing: is the equivalent of your objective function myfun
> available in matlab? Have you checked if it is implemented
> correctly?
>
> Best, Julius
>
>>>> Regards, Tobias
>>>>
>>>>
>>>> Am 22.01.2014 15:30, schrieb Julius Ziegler: On 01/22/2014
>>>> 02:59 PM, Tobias Schmidt wrote:
>>>>>>> Sure! Thank you for investing your time ;-).
>>>>
>>>> You are not computing the gradient of zeroconfun, but it
>>>> looks like you are using it as a constraint. Have you
>>>> overlooked it?
>>>>
>>>> Best, Julius
>>>>
>>>>>>> Regards, Tobias
>>>>>>>
>>>>>>> Am 22.01.2014 14:23, schrieb Julius Ziegler: Could you
>>>>>>> attach your updated code, please?
>>>>>>>
>>>>>>> Best, Julius
>>>>>>>
>>>>>>> On 01/22/2014 01:45 PM, Tobias Schmidt wrote:
>>>>>>>
>>>>>>> _______________________________________________
>>>>>>> NLopt-discuss
>>>>>>>
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>>>>>>> NLopt-discuss
>>>>>>>
>>>>>>>
>>>>>>>>
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>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>>
>
>>>>>>>>
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>>>>>
>>>>>
>>>>>>>
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>
>>>>>
>>
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>
>
>
- --
Dipl.-Inform. Julius Ziegler <[email protected]>
Institut für Mess- und Regelungstechnik
Karlsruher Institut für Technologie
Department of Measurement and Control
Karlsruhe Institute of Technology
Engler-Bunte-Ring 21
76131 Karlsruhe
Tel. +49 721 608 47146
- --
Dipl.-Inform. Julius Ziegler <[email protected]>
Institut für Mess- und Regelungstechnik
Karlsruher Institut für Technologie
Department of Measurement and Control
Karlsruhe Institute of Technology
Engler-Bunte-Ring 21
76131 Karlsruhe
Tel. +49 721 608 47146
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