LevenbergMarquardt
- To: mathgroup at smc.vnet.net
- Subject: [mg86539] LevenbergMarquardt
- From: "Ausman, Kevin" <ausman at okstate.edu>
- Date: Thu, 13 Mar 2008 04:33:38 -0500 (EST)
I want to thank everyone who replied to my earlier questions. I think I
am getting closer to solving my problem. Unfortunately, I have run into
another issue.
Here is what I am trying to do:
result=FindMinimum[Sum[residFunc[xptData,testInstFunc,0,t,numRateConst,
rateConst/.{kauto=AEk1float,icauto=AEic1float},numInitConc,ic/.{kauto=AEk
1float,icauto=AEic1float},ec/.{kauto=AEk1float,icauto=AEic1float},rateEq,
finalTime][[i]]^2,{i,1,Length[xptY]}],{{k1float,6000},{ic1float,0.003}},Method=AELevenbergMarquardt]
The problem is that without specifying LevenbergMarquardt as the method
I get an error that the gradient is effectively zero, but when I do
specify Levenberg Marquardt it says that my function isn't of the
appropriate form:
FindMinimum::notlm : The objective function for the method
LevenbergMarquardt must be in a least-squares form:
Sum[f[i][x]^2,{i,1,n}] or Sum[w[i] f[i][x]^2,{i,1,n}] with positive
w[i].
It looks to me as though I have a sum of squares, and I know from
experience (programming in Fortran many years ago) that
LevenbergMarquardt is the way to go for this particular problem.
The parameters for residFunc are some experimental data and some fitting
paramters (typically in lists), and it returns a one-dimensional list of
numbers.
Help?
Kevin Ausman
- Follow-Ups:
- Intermediate Evaluation in FindMinimum
- From: "Ausman, Kevin" <ausman@okstate.edu>
- Intermediate Evaluation in FindMinimum