Imposing constraint on gradient coefficients in NonLinear optimization
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I'm trying to solve a non linear 6 dimensional optimization problem, using a gradient which I calculate (6 dimensional).
I would like to influence the gradient coefficients in such a way that Coeff(1) == Coeff(2) == Coeff(3) and Coeff(4)==Coeff(5)==Coeff(6)
Is this possible?
Thanks! Jonathan
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Alan Weiss
el 18 de Abr. de 2014
I suppose that you could try to minimize the sum of the squares of the differences between various coefficients:
(Coeff(1) - Coeff(2))^2 + (Coeff(1) - Coeff(3))^2 + (Coeff(4) - Coeff(5))^2 + (Coeff(4) - Coeff(6))^2
You might want to use lsqnonlin and have your vector be (coeff(1) - coeff(2), coeff(1) - coeff(3), ...)
Alan Weiss
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Sean de Wolski
el 18 de Abr. de 2014
Why do you want that additional constraint? You might be able to get it by bounding with DiffMinChange and DiffMaxChange in the options:
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