The best way to write a large number of constraints for an optimization problem

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Hi All,
I have an optimization problem which have a larger number of variables, probably 10000 optimization variables and a large number of constraints. The optimization problem is mixed integer convex problem. What is the best way to define the large number of constraints?. Can I use loops like the ones used in CVX to define constraints?. I have matlab 2013a.
Thank you

Accepted Answer

Matt J
Matt J on 6 Nov 2019
Edited: Matt J on 6 Nov 2019
If they are linear constraints, they will be expressed using matrix-vector multiplication
A*x<=b
Aeq*x<=beq
so any vectorized method for building the A, Aeq, b, beq, matrices would be appropriate. Similarly, nonlinear constraints in Matlab are expressed using user-defined constraint functions of the following form
function [cineq,ceq]=nonlcon(x)
....
end
where cineq and ceq are vectors of inequality and equality constraint violations, respectively. When the number of variables or constraints is large, you would use vectorized Matlab commands to generate cineq and ceq from x.
  5 Comments
Matt J
Matt J on 21 Nov 2019
Edited: Matt J on 21 Nov 2019
When I run the code
Code you haven't shown, so it's anybody's guess what's wrong with it....
However, my guess would be that your call to fmincon looks like this
fmincon(_____,nonlcon)
instead of this
fmincon(_____,@nonlcon)

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