# Implement parameter constraint for surrogateopt

2 views (last 30 days)
Elsa Bunz on 27 Oct 2021
Commented: Elsa Bunz on 29 Oct 2021
Hello,
I want to run an optimisation using surrogateopt and I have the constraint, that certain parameters can't be smaller than others. So, I guess for other methods my constraint function would look like this (?):
params = [param1, param2, param3, param4]
function [c, ceq] = simple_constraint(params)
c = [params(2)-params(1);
params(4)-params(3)];
ceq = [];
end
As far as I understood in surrogateopt the constraints are set in the objective function.
What is the best way to implement these parameter constraints which are independent of the objective function value?
Just setting and arbitrary high value as value of the objective function? So, something like this:
function f = objFun_surrogateopt(param)
if params(2)> params(1) || params(4) > params(3)
f.Fval = 1000;
else
f.Fval = objFun(param);
end
f.Ineq = [params(2)-params(1);
params(4)-params(3)];
end
Or is there a smarter and more efficient way?
I'm looking forward to any hint on how to improve this!

Alan Weiss on 29 Oct 2021
The answer depends on your MATLAB version. As the Release Notes show, linear constraints were introduced in R2021a, nonlinear constraints were initroduced in R2020a.
• With R2021a or later, param(2) >= param(1) is equivalent to the linear constraint
A = [1 -1 0 0 0];
b = 0; % This means x(1) - x(2) <= 0, or x(1) <= x(2)
• With R2020a or R2020b, represent the constraint as a nonlinear inequality constraint:
function F = objcon(x)
F.Ineq = x(1) - x(2);
F.Fval = % your objective function here
end
Alan Weiss
MATLAB mathematical toolbox documentation
Elsa Bunz on 29 Oct 2021
Okay, thanks a lot for the clarification!

R2020b

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