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Topic: Which optimization method to use?
Replies: 5   Last Post: Dec 2, 2013 9:52 AM

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Tikkuhirvi Tietavainen

Posts: 98
Registered: 4/22/08
Re: Which optimization method to use?
Posted: Mar 28, 2013 11:23 AM
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Alan_Weiss <aweiss@mathworks.com> wrote in message <kj1dck$hh5$1@newscl01ah.mathworks.com>...
> On 3/27/2013 1:09 PM, Aino wrote:
> > Hello all.
> >
> > I have a function that includes a simulation. The input of the
> > simulation is seven parameters. I need to vary five of the seven
> > parameters to find the combination that would minimize the final
> > output (error that is calculated between a measured signal and the
> > simulated signal). I have somewhat a good initial guess for the five
> > parameters and I know that they should all be positive. I need to do
> > this optimization several times, so computational cost is an issue too.
> >
> > I tried the optimization with "fminsearch". It worked quite ok, but
> > two problems still occurred. First, one of the five parameters had
> > negative values. This particular parameter is possible to just leave
> > out. Second, it would seem that there are several local minimum that
> > are all feasible when considering the parameter values :( and
> > "fminsearch" didn't always find the global minimum. Also, I think that
> > the output error includes "noise", or maybe one/several parameters are
> > not always relevant.
> > I am now trying out "fminsearchbnd" (File Exchange) without the fifth
> > parameter and with lower bounds [0,0,0,0]. However, I was wondering if
> > "fmincon" would be more appropriate for me? I am not sure how to
> > implement it, though. Is there possibly even better (more robust, not
> > too time-consuming) optimization method for me?
> >
> > Thanks,
> > Aino

>
> You might be interested in the documentation on optimizing simulations:
> http://www.mathworks.com/help/optim/ug/optimizing-a-simulation-or-ordinary-differential-equation.html
>
> fmincon is indeed an appropriate solver for your problem. It is part of
> Optimization Toolbox. You should use the interior-point or sqp
> algorithms; see
> http://www.mathworks.com/help/optim/ug/writing-constraints.html#br9p_ry
>
> Alan Weiss
> MATLAB mathematical toolbox documentation


Thank you for your answer!

I am now testing the "fmincon" with "sqp". I still have some questions about the optimization.. First, I don't know exactly the whole parameter space (therefore I use only the "0" lower bounds and "Inf" higher bounds) and with bad parameters the simulation stops and gives something rather arbitrary for the error. I tried to put "NaN" as the error when the simulation stops, but "sqp" algorithm doesn't seem to know how to deal with the "NaN". Did I misunderstand the last part of your first link, about returning "NaN"? I get the following error statement:

"Objective function is undefined at initial point. Fmincon cannot continue."

Also, I would be happy with an error smaller than 0.05, is that what "TolFun" is for? I didn't see any difference between TolFun=0.1 and TolFun=0.05. Also, to speed the optimization, I would like to have a minimum step size for the input parameters. "DiffMinChange"? Setting DiffMinChange=0.01 lead to slower computing but also smaller error. Also, the parameters have a very different magnitude, is it possible to tweak the step size separately for each parameter? I have to say I'm a bit lost with this optimization.

-Aino



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