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
2014 年 4 月 18 日
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
2014 年 4 月 18 日
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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