Writing Constaints for simulannealbnd
1 visualizzazione (ultimi 30 giorni)
Mostra commenti meno recenti
Saifullah Khalid
il 22 Giu 2017
Commentato: Saifullah Khalid
il 23 Giu 2017
I am trying to optimize an objective using simulannealbnd function. I have constraints of upper and lower bounds on decision variable x. The constraints are: x > 0 and sum(x)< Maximum_Average_Price. I have coded as follow but the resultant sum(x) goes beyond Maximum_Average_Price. I would appreciate any help.
x0=[price(1:4), priceDash(5:28)];
lb = ones(1,28);
ub= min(sum(x0), price_avg_max);
0 Commenti
Risposta accettata
Alan Weiss
il 22 Giu 2017
The constraint you have is not a bound, it is a linear inequality constraint that cannot be represented by a bound.
I strongly suggest that you do not use simulannealbnd for this problem, and instead use fmincon if your objective function is smooth, and use patternsearch otherwise. If you are looking for a global solution, then start the solver from a variety of initial points
x0 = lb + rand(size(lb)).*(ub - lb);
By the way, you wrote that your lower bound is x >= 1, not x >= 0, since you said lb = ones and not zeros.
Alan Weiss
MATLAB mathematical toolbox documentation
4 Commenti
Alan Weiss
il 23 Giu 2017
No and no. A linear constraint is not the same as a bound constraint. Draw yourself a picture of x1 + x2 <= 1, and see if you can figure out how to get that picture as a bound constraint. The answer is no, you cannot.
You will have to do one of several things:
- Write your own simulated annealing solver that accepts linear constraints.
- Use a different solver.
- Find a different problem.
Sorry, that is the set of choices.
Alan Weiss
MATLAB mathematical toolbox documentation
Più risposte (0)
Vedere anche
Prodotti
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!