Can I see the parameters that fminunc generate in each try of the optimization search?

2014 5 14
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更新时间：2014 5 15
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Hi,
I am using fminunc to optimize a likelihood function which inside has other functions which makes the problem a bit complicated.
When I try the values of all the matrices inside this likelihood with the initial parameters all is correct. With this I mean that I have correct covariance matrices, no zero determinants etc.
However, when I start the optimization process at a certain parameter values I get a singular matrix in one of the equations inside the likelihood.
Therefore, what I would like to do is to observe the vector of parameters being chosen by the fminunc in the iterations.
Is there a way to do this?
Thanks in advance.
Barbara

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Alan Weiss
Alan Weiss 2014-5-14

0 个投票

You can write an output function to obtain almost everything you want. However, fminunc takes finite difference steps that the output function will not report; output functions get called at each iteration, not at each step in the process.
If what you really want is to have the solver continue past a singularity, you might try using a different solver. The fmincon interior-point and sqp algorithms are robust to some failures in function evaluation. You need a constraint to run fmincon; feel free to use lb = -Inf, which fmincon accepts as a constraint but which has no bearing on the solution.
Good luck,
Alan Weiss
MATLAB mathematical toolbox documentation

更多回答（2 个）

Barbara
Barbara 2014-5-15

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Dear Alan,
so I have dived into this issue and I found that it is quite simple to get the outputs of each iteration. For the case of the likelihood values at each iteration inside the fminsearch (for instance) (it is a simple change of definition for the parameters case) things goes like this:
//create empty matrix global_likelihood_vector = [];
//Settings and call to "fminsearch" ... ...
function [current_likelihood] = myLikelihood(x);
//Calculate likelihood
current_likelihood = ...
//Concatenate current likelihood to vector
//of all likelihoods
global_likelihood_vector = [global_likelihood_vector ; current_likelihood];
end;
And thats it :)

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