Even if I set grad[i] = 0.1 in the objective function and the constraint
functions -- of course only to observe the behavior -- again the
optimization routine calls myfun only once and then it fails with the
"nlopt roundoff-limited" exception -.- .
Regards,
Tobias
Am 22.01.2014 17:19, schrieb Julius Ziegler:
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On 01/22/2014 05:14 PM, Julius Ziegler wrote:
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
Corr.!
I was a bit wrong here. In the denominator is actually the second
derivative (of the objective function).
However, if you return a gradient 0 (for the objective function), the
optimizer will sure go into some termination routine, because it
thinks that it has arrived at a stationary point.
Might be less problematic for the constraint gradients, but I would
not bet...
Julius
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:
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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
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Tobias Schmidt, B.Sc.
Student and research assistant
Automation Lab.
Institute for Computer Engineering
Heidelberg University
B6, 26, B0.19
D-68131 Mannheim
Germany
Tel. +49-621-181-2664
URL: http://proaut.ziti.uni-heidelberg.de
URL: http://www.ziti.uni-heidelberg.de
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