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From enzhong...@pnc.com
Subject Re: [math] BOBYQAOptimizer hang
Date Fri, 03 Jan 2014 20:31:53 GMT
Gilles,

Thank you for recommending LevenbergMarquardtOptimizer. It works for our 
function. However, it seems to take much longer than BOBYQAOptimizer. Is 
this expected as it needs to compute gradient?

Andrew



From:
Gilles <gilles@harfang.homelinux.org>
To:
<user@commons.apache.org>
Date:
12/19/2013 07:03 PM
Subject:
Re: [math] BOBYQAOptimizer hang



On Thu, 19 Dec 2013 08:53:14 -0500, enzhong.fu@pnc.com wrote:
> Gilles,
>
> Thank you for answering. PowellOptimizer did not work very well for 
> our
> particular function, and other optimizers in scalar.noderiv are not 
> easily
> understandable.

It would be interesting to know what you tested, and what did not work 
as
you expected, in order to look for bugs in "PowellOptimizer".

For the other optimizer, it is perhaps an issue of setting good 
parameters
and initial guess...

> BOBYQAOptimizer usually works well. Do you have
> suggestions that we might work around this particular case so it does 
> not
> hang?

I have no idea.
If I had to debug it, I'd first trace the code execution to locate 
where it
hangs.

> We minimize LS with grid (x,y) having 9 points.

Did you consider using the "LevenbergMarquardtOptimizer"?


Best regards,
Gilles

>
> Andrew
>
>
> class LS implements MultivariateFunction {
>         private BS bs = new BS();
>         private double[] x;
>         private double[] y;
>         private double const1;
>         private int optionType;
>
>         public LS(double[] x, double[] y, int optionType, double 
> const1) {
>                 this.x = x;
>                 this.y = y;
>                 this.optionType = optionType;
>                 this.const1 = const1;
>         }
>
>         @Override
>         public double value(double point[]) {
>                 int len = x.length;
>                 double val = 0;
>                 for (int i=0; i<len; i++) {
>                         double yc = bs.f2(x[i], point[0]*const1, 
> point[1],
> point[2], optionType);
>                         val += Math.pow(yc-y[i], 2);
>                 }
>                 return val;
>         }
>
> }
>
> class BS {
>         public double blackScholes(double price, double strike, 
> double
> time, double vol, int optionType) {
>             double volrootime = Math.pow(time, 0.5) * vol;
>             double topline1 = Math.log(price/strike);
>             double topline2 = Math.pow(vol, 2)/2* time;
>             double topline = topline1 + topline2;
>             double d1 = topline / volrootime;
>             double d2 = d1 - volrootime;
>
>             double val = 0.0;
>             NormalDistribution normal = new NormalDistribution();
>             if (optionType==1)
>                 val = (price * normal.cumulativeProbability(d1) - 
> strike *
> normal.cumulativeProbability(d2));
>             else
>                 val = (strike * normal.cumulativeProbability(-d2) - 
> price
> * normal.cumulativeProbability(-d1));
>
>             return val;
>         }
>
>         public double f2(double x, double ah, double ai, double aj, 
> int
> ak) {
>                 return ah*bScore(x, ai, aj, ak);
>         }
>
>         public double bScore(double price, double strike, double vol, 
> int
> optionType) {
>                 return blackScholes(price, strike, 1, vol, 
> optionType);
>         }
> }
>
>


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