[Insight-users] Re: [Insight-developers] a small bug in itkConjugateGradientOptimizer

[Ashish Poddar]

Hi,
 Most of the optimizers which I have came across help in finding the local 
minima around the given initial approximation. But in that case I mostly end 
up in a wrong place. Is there any algorithm which helps to scan the global 
space somehow and help in determining the global minima.
 Other problem that I am facing with the conjugate gradient method is that 
the scales do not work for conjugate gradient optimizer. The scales are only 
taken into account in the very first initialization step and are never 
considered again in any of the latter iterations. I want to fix some of the 
parameters by setting the scale to some extreme value (I used to set 100 or 
something for regular step gradient descent optimizer and it used to serve 
the purpose very conveniently).
 any help will be highly appreciated,
with regards,
Ashish.

 On 5/18/05, Luis Ibanez <luis.ibanez at kitware.com> wrote: 
> 
> 
> Hi Ashish,
> 
> The Conjugate Gradient method is only convenient when the cost
> function has smooth second derivatives. If your cost function
> is noisy, is is unlikely that this optimizer will behave nicely.
> 
> Note that is is common to find that Image Metrics are rather
> noisy functions.
> 
> 
> 
> Regards,
> 
> 
> Luis
> 
> 
> 
> --------------------
> 
> Ashish Poddar wrote:
> 
> > Hi,
> >
> > I also am right now struggling with the initialization options for the
> > Conjugate Gradient for which I could not find any examples. While
> > searching I came across an example for Levenberg Marquardt Optimizer
> > which seems to be having similar interface as that of conjugate
> > gradient optimizer. However the similar initialization did not worked
> > for Conjugate gradient. If someone can point out any reference for
> > Conjugate Gradient, it would be great.
> >
> > Earlier I was using regular step gradient descent optimizer with these
> > parameters:
> > Transform - Centered Affine
> > Scale for first 9 parameters - 1.0
> > Scale for next 6 parameters - 0.0001
> > Number of Iterations - 400
> > Minimum Step Length - 0.0001
> > Maximum Step Length - 0.005
> >
> > Any help will be highly appreciated,
> > with regards,
> > Ashish.
> >
> >
> >
> > On 5/17/05, Ashish Poddar <ahpoddar at gmail.com> wrote:
> >
> >>Hi Luis,
> >>
> >>Thank you for the quick action. probably similar change is required
> >>for Levenberg Marquardt Optimizer too.
> >>
> >>with regards,
> >>Ashish.
> >>
> >>On 5/16/05, Luis Ibanez <luis.ibanez at kitware.com> wrote:
> >>
> >>>Hi Ashish,
> >>>
> >>>Thanks for pointing this out.
> >>>
> >>>You are right, the GetValue() method should be const.
> >>>
> >>>A fix has now been committed to the CVS repository.
> >>>
> >>>Please let us know if you encounter any other problem.
> >>>
> >>> Thanks
> >>>
> >>> Luis
> >>>
> >>>----------------------
> >>>Ashish Poddar wrote:
> >>>
> >>>>hi,
> >>>>
> >>>>I am not sure whether it qualifies as a bug or not, but surely affects
> >>>>the re-usability and pluggability model of ITK Library.
> >>>>
> >>>>the GetValue() function in ConjugateGradientOptimizer class currently 
> is
> >>>>
> >>>>MeasureType GetValue();
> >>>>
> >>>>but in case of RegularStepGradientDescentOptimizer class its defined 
> by macro as
> >>>>
> >>>>MeasureType GetValue() const;
> >>>>
> >>>>which is an interface mis-match... This I encountered when i replaced
> >>>>regular step gradient descent optimizer by conjugate gradient
> >>>>optimizer. In the observer I was using a const reference of the
> >>>>optimizer and displaying the value (just the example which is
> >>>>available for the same nothing new =D)...
> >>>>
> >>>>with regards,
> >>>>Ashish.
> >>>>
> >>>
> >>>
> >>--
> >>Ashish Poddar
> >>Have an acceptable reason for accepting anything.
> >>Y:ashish_poddar | MSN:ashish_poddar at yahoo.com
> >>
> >
> >
> >
> 
> 
> 
> 


-- 
Ashish Poddar
Have an acceptable reason for accepting anything.
Y:ashish_poddar | MSN:ashish_poddar at yahoo.com
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