How to use lsqnonlin command for solving a cost function minimization problem which consists of optimization variable?
4 Ansichten (letzte 30 Tage)
Ältere Kommentare anzeigen
Hi,
I'm trying to solve an optimization problem by using 'lsqnonlin' comman in matlab. My objective function consists of optimization variables created by 'optimvar' command. I came across with this error,
"Error using lsqfcnchk (line 80)
If FUN is a MATLAB object, it must have an feval method."
when i tried to use that objective function with lsqnonlin. I read that lsqnonlin only accepts function handles or anonymous functions, not symbolic objects. In this case how can i use my objective function with lsqnonlin? I left a piece of code below to show my problem clearly. By the way, I'm new at optimization toolbox. So I'm open for suggestions to use proper methods.
C = [0 0 0 1
0 1 0 0];
D = [0;0];
x = [zeros(4,1)];
u = optimvar('U',2);
f=0;
% for loop here to create an objective function
for i=1:2
y_ara = C*x + D*u(i);
error1 = 1-y_ara(1,1);
error2 = 1-y_ara(2,1);
f = f + error1^2 + error2^2; % Objective function including optimvar U(1) and U(2)
end
prob = optimproblem;
prob.Objective = f;
% initilization and constraits
x0 = [zeros(1,10)];
lb = [-10*ones(1,2)];
ub = [10*ones(1,2)];
options = optimoptions("lsqnonlin",'Algorithm','levenberg-marquardt');
[sol, fval, exitflag, output] = lsqnonlin(f, x0, lb, ub, options);
2 Kommentare
Matt J
am 9 Aug. 2022
Your ub and lb bounds do not make sense. You have 10 bounds, but only 2 unknowns u(i).
Akzeptierte Antwort
Matt J
am 9 Aug. 2022
Bearbeitet: Matt J
am 9 Aug. 2022
If you have an objective created with optimvar variables, you would solve the problem by using the solve command,
C = [0 0 0 1
0 1 0 0];
D = [0;0];
...
prob = optimproblem;
prob.Objective = f;
sol=solve(prob);
In the most basic use case, you would not call lsqnonlin or any other solver directly. The solve() command will choose the appropriate solver for you.
6 Kommentare
Matt J
am 9 Aug. 2022
Modeling with a 10th order polynomial sounds like a dangerous thing to do. At the very least, you will need a good initial guess, since polynomials tend to have lots of local minima. Thus, you might need to use.
Weitere Antworten (1)
Walter Roberson
am 9 Aug. 2022
C = [0 0 0 1
0 1 0 0];
D = [0;0];
x = [zeros(4,1)];
u = optimvar('U',2);
f=0;
% for loop here to create an objective function
for i=1:2
y_ara = C*x + D*u(i);
error1 = 1-y_ara(1,1);
error2 = 1-y_ara(2,1);
f = f + error1^2 + error2^2; % Objective function including optimvar U(1) and U(2)
end
prob = optimproblem;
prob.Objective = f;
% initilization and constraits
x0 = [zeros(1,10)];
lb = [-10*ones(1,2)];
ub = [10*ones(1,2)];
%options = optimoptions("lsqnonlin",'Algorithm','levenberg-marquardt');
[sol, fval, exitflag, output] = solve(prob)
prob
f
You have C*x but your x is all 0. You have D*u but your D is all 0. So your optimization expression becomes a constant.
Siehe auch
Kategorien
Mehr zu Systems of Nonlinear Equations finden Sie in Help Center und File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!