Optimization of multivariable function
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Hi everybody!
I'm having some trouble trying to optimize a function. I want to minimize the function F defined as:
where Aexp is a vector containing experimental data and Asim is a vector of simulated data. The true problem comes when defining the simulation function:

So the optimization needs to be carried out changing a1, sigma and a2 values in order to make F minimum.
However I'm really stuck as I have been using symbolic functions but turns out the result is always 1. I don't know any other way to use integral fucntions, even if this one does not look like working.
Any ideas? Thanks!
5 comentarios
Milankumar Padhiyar
el 17 de Abr. de 2020
can you show us how you defined the objective function using symbolic variables ?
Blanca Castells
el 18 de Abr. de 2020
Blanca Castells
el 20 de Abr. de 2020
Editada: Blanca Castells
el 20 de Abr. de 2020
Walter Roberson
el 20 de Abr. de 2020
With that sigma, a1, a2, then the results of Asim are not exactly 0, but they are smaller than 10^(-7000) so double() converts them to 0.
You can, by the way, rewrite:
sigma=5;
a1=1000000;
a2=126;
iarray=linspace(150,400,30);
i1=iarray(1);
syms x y I2
fun1(x,y)=exp(-x/y);
Int1 = int(fun1, y, [i1, I2]);
fun2 = exp((-a1/5)*Int1-(((x-a2)^2)/(2*sigma^2)));
Int2 = int(fun2, x, [0 inf]);
coef=1/(sigma*(2*pi())^0.5);
AAsim = coef*Int2;
aasim = subs(AAsim, I2, iarray);
asim = double(aasim); %fails, values too small for MuPAD to work with
Blanca Castells
el 20 de Abr. de 2020
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