How can I solve a problem using Constrained Nonlinear Regression?

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Hi there,
I would like to perform contrained nonlinear regression. The scenerios for constraints are:
  1. Sum of parameters = n1 and each parameter < n2 (i.e. b1 + b2 + b3 = 2 and b1, b2, b3 < 1)
  2. Sum of parameters = n1 and each parameter ≤ n2 (i.e. b1 + b2 + b3 = 2 and b1, b2, b3 ≤ 1)
  3. Sum of parameters = n1 and n2 < each parameter < n3 (i.e. b1 + b2 + b3 = 2 and 0.5 < b1, b2, b3 < 1.5)
  4. Sum of parameters = n1 and n2 ≤ each parameter ≤ n3 (i.e. b1 + b2 + b3 = 2 and 0.5 ≤ b1, b2, b3 ≤ 1.5)
  5. Sum of parameters <, ≤, >, ≥ n1 or n1 <, ≤ sum of parameters <, ≤ n2 (i.e. b1 + b2 + b3 <, ≤, >, ≥ 2 or 0.5 <, ≤ b1 + b2 + b3 <, ≤ 1.5)
  6. Or any other alternative, if there is any remaining :)
I know that Matlab provides lsqlin for constrained linear LSQ and lsqnonline for nonlinear case. Yet, I couldn't find how to introduce summation contraint into lsqnonlin, like Aeq and beq in lsqlin.
I would be more than happy, if someone can help.
Cheers,
M
  1 comentario
Bruno Luong
Bruno Luong el 14 de Dic. de 2020
No optimizer can handles strict inequalities such as < and >. Simply because it is "ill posed" minimization.
Just think about this simple example:
What is is minimum of x with the constraint x > 0?
Such probem has solution.

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John D'Errico
John D'Errico el 14 de Dic. de 2020
Editada: John D'Errico el 14 de Dic. de 2020
Sorry. lsqnonlin cannot handle general equalty or inequality constraints. Only bound constraints, and while you do have bound constraints, you also have an equality constraint on the sum.
That means you will need to use a more general optimizer, probably FMINCON. It can handle any of the constraint classes you mention.
Just pass it the sum of squares of residuals that you compute as an objective.
  4 comentarios
Alan Weiss
Alan Weiss el 18 de Dic. de 2020
Alan Weiss
MATLAB mathematical toolbox documentation
Melanie VT
Melanie VT el 20 de Dic. de 2020
I made a misinterpretation beforehand. Problem solved. Thank you both. Really grateful...

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