Robustness with fminimax

1 次查看（过去 30 天）
Mouloud
Mouloud 2012-5-31
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,

请先登录，再回答此问题。

回答（2 个）

Sargondjani
Sargondjani 2012-5-31
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...
Mouloud
Mouloud 2012-5-31
think you for your answer,
but, I want to solve the minimax problem for overall profile, the objective is to find a robust solution i.e:
when the "x" makes a small variation "r", r is smaller then 0.5 for example , the solution is robust.
so it is a global optimization and I have to take into account the perturbation "r" for al instant n=length(x)

类别

在 Help Center 和 File Exchange 中查找有关 Multiobjective Optimization 的更多信息

标签

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by