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Topic: optimization problems with fmincon
Replies: 3   Last Post: Oct 21, 2005 5:14 PM

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Marcelo Marazzi

Posts: 24
Registered: 8/29/05
Re: optimization problems with fmincon
Posted: Oct 21, 2005 5:14 PM
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Is the objective smooth (continuous and differentiable)?
If it's not, that could be the reason why fminsearch works
better - unlike fminsearch, fmincon is meant for smooth
problems.


Lukas Barchewitz wrote:
> Thanks John!
>
> I made it run and introduced your help. Unfortunately, fmincon is not
> converging at the same x when using instead fminsearch where an
> unconstrained problem can be introduded. There is a clear optimum if I
> introduce the first paramter (parable), so I used fmincon to assess the
> quality of the fmincon and introduce more variables, which will lead to
> a real optimization problem because there is no obvious maximum for the
> function. The function is highly non-linear and cannot be shown in one
> line only.
>
> Why are results from fmincon and fminsearch not the same?
>
> I tried to introduce one more varibles to fmincon
>
> by x0=[0.7, 15];
> lb=[0.4; 10];
> ub=[0.8; 25]; is this right when I try to
>
> set 0.4<x(1)<0.8 and 10<x(2)<25?

Yes, that's right.

-marcelo

>
> and the x-vector is practically not moving from the starting conditions
> (I have tried is, for different starting conditions) If I check it by
> hand by changing the first variable and then the ohter I can find an
> optimum at x(1)=0.67 and x(2)=25;
>
> what's wrong?
>
> Thanks
> Lukas
>
> John D'Errico schrieb:
>

>> In article <dj7sj9$r60$1@newsserver.rrzn.uni-hannover.de>,
>> Lukas Barchewitz <barchewitz@ifs.uni-hannover.de> wrote:
>>
>>

>>> Hi!
>>>
>>> I have got a nonlinear function to be minimized. It works all well
>>> with the fminsearch function but fmincon does not converge at the
>>> same x value and I cannot introduce my options into the command line.
>>> At this point I want to try it with one variable where 0.4<x<0.8 to
>>> the function etaisopt, where x is constrained.
>>>
>>> A=[-1; 1];
>>> b=[0.4; 0.8];
>>>
>>> A=[1];
>>> b=[0.75];
>>>
>>> x0=0.7;
>>> Aeq=[];
>>> beq=[];
>>> lb=[];
>>> ub=[];
>>> nonlcon=[];
>>> options =
>>> optimset('Display','iter','MaxFunEvals',50,'MaxIter',20,'MaxSQPiter',10,'GradO
>>>
>>> bj','on');
>>> [x,fval,exitflag,output,lambda,grad] =
>>> fmincon(@etaisopt,x0,A,b,Aeq,beq,lb,ub,nonlcon,options)
>>>
>>> It does not work prompting
>>> ??? Error using ==> fmincon
>>> FMINCON cannot continue because user supplied objective function
>>> failed with the following error:
>>> One or more output arguments not assigned during call to
>>> 'C:\Dokumente und Einstellungen\barchewitz\Eigene
>>> Dateien\MatLab\Kennfeldregression\K29\etaisopt.m (etaisopt)'.
>>>
>>> Error in ==> etaisoptscript at 20
>>> [x,fval] = fmincon(@etaisopt,x0,A,b,Aeq,beq,lb,ub,nonlcon,options)
>>>
>>> what has not been assigned??
>>> I would like to use fmincon because I want introduce 4 more variables
>>> which are constrained.
>>>
>>> Who can help
>>> Lukas

>>
>>
>>
>> You don't include the call to etaisopt, so its hard to
>> tell what is wrong. Apparently you have not defined
>> the output argument to etaisopt.
>>
>> I will comment that if you have only bound constraints,
>> then you do not need to use A and B, which allow for
>> general inequality constraints. You only need to use
>> LB and UB. An advantage of use of the explicit bound
>> constraints in LB and UB is the largescale optimizer
>> now becomes an alternative.
>>
>> I'll also note that a single parameter optimization with
>> bound constraints will be better handled by fminbnd. It
>> sounds like you will be adding unknowns, so fmincon is
>> necessary then.
>>
>> HTH,
>> John D'Errico
>>
>>

>



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