FAQFAQ   SearchSearch   MemberlistMemberlist   UsergroupsUsergroups 
 ProfileProfile   PreferencesPreferences   Log in to check your private messagesLog in to check your private messages   Log inLog in 
Forum index » Science and Technology » Math » num-analysis
Optimization of noisy functions
Post new topic   Reply to topic Page 1 of 1 [3 Posts] View previous topic :: View next topic
Author Message
John Herman
science forum beginner


Joined: 11 Jul 2005
Posts: 28

PostPosted: Sat Jul 15, 2006 2:33 pm    Post subject: Optimization of noisy functions Reply with quote

I'm trying to sort out a model matching problem with noisy data using
optimization . I've been looking at several methods. I went out to the
decision tree on plato and looked at what is available. The most promising is
probably SNOBFit but I'm limited to the C prrogramming language (f2c is
possible) by the platform. Peter says NEWUOA really isn't useful for
optimizing with noisy data. Implicit Filtering looks sort of like Truncated
Newton with numerical derivatives. I'm at a loss for where to go from here.

The other possibility is to move to curve fitting.
Back to top
Peter Spellucci
science forum Guru


Joined: 29 Apr 2005
Posts: 702

PostPosted: Mon Jul 17, 2006 3:24 pm    Post subject: Re: Optimization of noisy functions Reply with quote

In article <T07ug.28362$uy3.9276@tornado.socal.rr.com>,
John_W_Herman@yahoo.com (John Herman) writes:
Quote:
I'm trying to sort out a model matching problem with noisy data using
optimization . I've been looking at several methods. I went out to the
decision tree on plato and looked at what is available. The most promising is
probably SNOBFit but I'm limited to the C prrogramming language (f2c is
possible) by the platform. Peter says NEWUOA really isn't useful for
optimizing with noisy data. Implicit Filtering looks sort of like Truncated
Newton with numerical derivatives. I'm at a loss for where to go from here.

The other possibility is to move to curve fitting.

you want nonlinear least squares fitting? why not LEVMAR or gaussfit ?
if derivatives (of the _model_!) should be available. I understand your
problem now as "fit model to noisy _data_ , not a noisy function evaluation?
Newuoa uses _interpolation_ of the function values, (for you?
the sum of squared deviations between model and data) hence, if the function
evaluation itself is noisy, this will promote this noise and possibly produce
lots of spurious local minima.
but if the function evaluation is noise free , it is a viable approach to
derivative free minimization.

hth
peter
Back to top
John Herman
science forum beginner


Joined: 11 Jul 2005
Posts: 28

PostPosted: Fri Jul 21, 2006 12:13 pm    Post subject: Re: Optimization of noisy functions Reply with quote

Thank you, Peter.

In article <e9ga3t$frk$1@fb04373.mathematik.tu-darmstadt.de>,
spellucci@fb04373.mathematik.tu-darmstadt.de (Peter Spellucci) wrote:
Quote:

In article <T07ug.28362$uy3.9276@tornado.socal.rr.com>,
John_W_Herman@yahoo.com (John Herman) writes:
I'm trying to sort out a model matching problem with noisy data using
optimization . I've been looking at several methods. I went out to the
decision tree on plato and looked at what is available. The most promising
is
probably SNOBFit but I'm limited to the C prrogramming language (f2c is
possible) by the platform. Peter says NEWUOA really isn't useful for
optimizing with noisy data. Implicit Filtering looks sort of like Truncated
Newton with numerical derivatives. I'm at a loss for where to go from here.

The other possibility is to move to curve fitting.

you want nonlinear least squares fitting? why not LEVMAR or gaussfit ?
if derivatives (of the _model_!) should be available. I understand your
problem now as "fit model to noisy _data_ , not a noisy function evaluation?
Newuoa uses _interpolation_ of the function values, (for you?
the sum of squared deviations between model and data) hence, if the function
evaluation itself is noisy, this will promote this noise and possibly produce
lots of spurious local minima.
but if the function evaluation is noise free , it is a viable approach to
derivative free minimization.

hth
peter
Back to top
Google

Back to top
Display posts from previous:   
Post new topic   Reply to topic Page 1 of 1 [3 Posts] View previous topic :: View next topic
The time now is Wed Jun 28, 2017 2:04 am | All times are GMT
Forum index » Science and Technology » Math » num-analysis
Jump to:  

Similar Topics
Topic Author Forum Replies Last Post
No new posts Generating function for Mathieu functions cosmicstring@gmail.com Math 1 Fri Jul 21, 2006 8:39 am
No new posts Entire functions, polynomial bounds david petry Math 2 Thu Jul 20, 2006 11:09 pm
No new posts Are Bessel Functions Transcendental? John Schutkeker Math 28 Tue Jul 18, 2006 2:24 am
No new posts scalar functions on non-diagonalizable matrices xyz91234@yahoo.com Math 6 Mon Jul 17, 2006 5:37 pm
No new posts Matrix functions via EVD decomposition ~Glynne num-analysis 7 Sat Jul 15, 2006 5:51 am

Copyright © 2004-2005 DeniX Solutions SRL
Other DeniX Solutions sites: Electronics forum |  Medicine forum |  Unix/Linux blog |  Unix/Linux documentation |  Unix/Linux forums  |  send newsletters
 


Powered by phpBB © 2001, 2005 phpBB Group
[ Time: 0.0186s ][ Queries: 16 (0.0038s) ][ GZIP on - Debug on ]