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Topic: fminunc + transformation
Replies: 5   Last Post: Apr 12, 2013 1:37 PM

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Saad

Posts: 25
Registered: 11/26/12
Re: fminunc + transformation
Posted: Apr 4, 2013 3:47 AM
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Alan_Weiss <aweiss@mathworks.com> wrote in message <kjhqkj$ioi$1@newscl01ah.mathworks.com>...
> On 3/30/2013 5:26 PM, Saad wrote:
> > Dear all,
> >
> > I am replicating a paper who recommends using fminunc to do a
> > "constrained" optimization through a transformation. I have tried to
> > use directly fmincon (with different algorithms) but the function
> > doesnt optimize, thats the reason I would like to follow the paper
> > advice and use a transformation as follows:
> >
> > C_bar=lamda*(exp(C)/1+ exp(C)) where lamda is a constant, C is the
> > unconstrained variable and C_bar is the constrained variable. I would
> > really appreciate if you could show me how I could use the
> > transformation in matlab. Do I have to create a seperate function? How
> > can I link it to the optimizer please?
> >
> > Here is my code
> >
> > C=[1; 1; 1 ; 1; 1; 1; 1; 1; 1];
> > options=optimset('Diagnostics','on','Display','iter','TolX',0.001,'TolFun',0.001,'LargeScale','off','HessUpdate','bfgs');
> >
> > [beta,fval,exitflag,output,grad,hessian] =fminunc(@mll,C,options)
> >
> > Thanks a lot for your help
> >
> > Best Regards
> >
> > S

>
> It looks to me as if C_bar is a multidimensional variable constrained to
> be between 0 and 1. Is that right?
>
> You can certainly include this transformation in a MATLAB statement:
>
> C_bar = lamda.*(exp(C)./(1+exp(C));
>
> The ./ and .* statements mean componentwise division and multiplication.
>
> However, I think you could avoid this complicated and (it seems to me)
> fragile transformation by simply setting bounds on fmincon and using the
> interior-point or sqp algorithms, which respect bounds.
> http://www.mathworks.com/help/optim/ug/writing-constraints.html#br9p_ry
>
> Good luck,
>
> Alan Weiss
> MATLAB mathematical toolbox documentation


Hi Alan

Thank you for your reply. I did try fmincon with different algorithms (interior-point and sqp algorithms) but could not locate the minimum. I also tightened the tolerance but still could not minimize (for info i am working on a kalman filtering problem). I think the issue is that I am not able to find a good initial guess. Any thoughts on how to handle that? Thanks a lot

Best

S



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