TroubleShoot FMinSearch with subscript assignment mismatch

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Eric Diaz
Eric Diaz 2016-1-15
评论： Eric Diaz 2016-1-15
I keep getting the following error:
Subscripted assignment dimension mismatch.
Error in fminsearch (line 190)
fv(:,1) = funfcn(x,varargin{:})
when I try to run the following optimization
objective = @(p) log(besseli(0,(((p(1)*exp(-xData/p(2))).*yData)./Variance),1)) - (((p(1)*exp(-xData/p(2))).^2) ./ (2.*Variance));
% unconstrained nonlinear optimization
parEst = fminsearch(objective,p0);
p0 is [1,2]. Variance is a predefined scalar. xData and yData are both [1,8].
Please help me as I have tried reading the help documents for fminsearch and for anonymous functions, but can't figure it out.

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采纳的回答

Torsten
Torsten 2016-1-15
A scalar must be returned to fminsearch from "objective" ; you return a vector.
Best wishes
Torsten.
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Torsten
Torsten 2016-1-15
You will have to supply the sum of squared differences of your model expression and your experimental data. But I must admit, since your function involves both xtata and ydata, I don't know what is yi_model and what is yi_experiment.
Best wishes
Torsten.
Eric Diaz
Eric Diaz 2016-1-15
Initially, I tried to break down my objective function, however I ran into issues with not having my p (parameter vector) variable defined as Matlab executed the term1 definition. So, I just put my model straight into my objective function in my previous post.
This is how I would like to write my code, as it reads more clearly. Please note that I put the sum function around the objective function below. Is the correct way to do the sum? Also, do you know a way around not having the parameter vector defined?
signalModel = p(1)*exp(-xData/p(2));
term1 = (signalModel.*yData)./Variance;
term2 = (signalModel.^2) ./ (2.*Variance);
objective = @(p) sum(log(besseli(0,term1,1)) - term2);

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更多回答（1 个）

John D'Errico
John D'Errico 2016-1-15
This happens over and over again. fminsearch is an OPTIMIZER. It does not do nonlinear regression. There is a difference.
An optimizer finds the minimum value of a general function of multiple parameters. So a SCALAR valued function of one or more variables.
A nonlinear regression can be turned into an optimization by forming the sum of squares of residuals, and minimizing that as an objective.
But if you just throw a vector into a tool like fminsearch, expect it to fail. fminsearch has no idea what you want to do. Computers cannot read your mind.
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Eric Diaz
Eric Diaz 2016-1-15
Thanks for your input John. I think what I need to do is just figure out the correct way to do that summation over the time points.

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