Robustness with fminimax
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Hello everyone,
I have to study robustness of my system, so my variables are uncertain and I have to add another variable to my objective function and it becomes:
original objective-function: myfun = @(x) sum (x(1:n)./polyval(p3,x(1:n)));
robust optimization : min max myfun = @(x) sum ([x(1:n) + r(1:n)]./polyval(p3,[x(1:n) + r(1:n)])); min for "x" and max for "r" .
"r" is the perturbation so it is very small,
my problem is non-convex and with non-linear constraints, and I solve the original problem with fmincon and it provides a good optimum.
For the robust optimization, I think that I can solve it by the fminimax, but I don't know how formulate it ??
Thanks,
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Respuestas (2)
Sargondjani
el 31 de Mayo de 2012
they way you present the problem now, you could just replace x(1:n) with y(1:n)=x(1:n) + r(1:n) in the orginal problem....
if instead you want to get solutions for every r then you could looop through r:
myfun=@(x,r)....
r=...
for ir=1:length(r)
my_fun_ir=@(x)myfun(x,r(ir))
%solve minimax where you store every solution as x(ir), for example
end
i hope this helps...
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