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Re: fminunc + transformation
Posted:
Apr 4, 2013 8:12 AM


On 4/4/2013 3:47 AM, Saad wrote: > > > 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 interiorpoint or sqp algorithms, which respect bounds. >> http://www.mathworks.com/help/optim/ug/writingconstraints.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 (interiorpoint 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
Well, assuming that you have upper and lower bounds on all components, why not try a lot of random initial points?
x0 = lb + rand(size(lb)).*(ub  lb);
There are more suggestions here (written for Global Optimization Toolbox, but you can use the suggestions anyway): http://www.mathworks.com/help/gads/refiningthestartpoints.html#bsfjxr9 http://www.mathworks.com/help/gads/isolatedglobalminimum.html
Alan Weiss MATLAB mathematical toolbox documentation



