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Regression of a vector in a optimization problem

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Ronny Rives
Ronny Rives on 31 Jul 2019
Commented: Matt J on 13 Aug 2019
Hello everyone,
I need to fit experimental data to an analytical solution. The analytical solution has the form:
  • C(z,t) = C_eq*f(z,t,D)
where f(z,t,D) is a known function of time (t) and position (z), and D and C_eq are parameters to regress.
I have already determined D and C_eq using the routine fminsearch. However, I would like to consider that C_eq does not necessarily have to be constant and can change over time.
My question is whether it is possible to regress C_eq as a vector instead of a constant? In this case, which routine is the most appropriate?
P.D: parameter D could also be considered as a vector if necessary.
Thanks in advance.
  5 Comments
Torsten
Torsten on 1 Aug 2019
Use "lsqcurvefit" with the parameter vector x = (C_eq(1),C_eq(2),...,C_eq(n)).

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Accepted Answer

Matt J
Matt J on 13 Aug 2019
As the others have said, all regression routines in the Optimization Toolbox allow you to represent the unknown variable in vector form. However, fminspleas might work especially well for your problem
since you only have one parameter that is intrinsically non-linear.

More Answers (1)

Sai Bhargav Avula
Sai Bhargav Avula on 13 Aug 2019
As mentioned by Torsten, lsqcurvefit can be used to obtain a vector as a result of the regression. But those values use the entire data for getting the output values . For you particular case you should segment the data based on time stamps and perform lsqcurvefit in a for loop.
  1 Comment
Matt J
Matt J on 13 Aug 2019
I don't think a loop would be appropriate here, actually, because as I understand it, the parameter D is shared by all time blocks.

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