Function optimization meeting conditions
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How can i optimize the I function, i want to find the values of h(j) that minimize I, meetentig the conditions h(j+1)>h(j), h(end)<120 and h(j+1)-h(j)<1.25 ?
ht is a array beiing its size ht(lt,lc) or the same ht(i,j) and it is calculated in another function. The formula of Ins is Ins=ht(i,j)-h(j).
Thanks for the help
function [h] = hp(ht, Lc, Lt)
lt = 0:0.5:Lt;
lc = 0:0.5:Lc;
Ins = cell(length(lt), length(lc));
h= cell(length(Lc));
for i = 1:length(lt)
for j = 1:length(lc)
Ins{i,j} = @(h) (ht(i,j) - h);
end
end
h0 = zeros(size(lc));
A = [];
b = [];
Aeq = [];
beq = [];
lb = [];
ub = [];
nonlcon = @constraints;
h = fmincon(@(h) Ins, h0, A, b, Aeq, beq, lb, ub, nonlcon);
end
function [c] = constraints(h)
c>0;
c = h(2:end) - h(1:end-1);
end
10 Commenti
Torsten
il 8 Giu 2023
Modificato: Torsten
il 8 Giu 2023
If ht is nxm, the linear constraints can be defined by A and b as in the code below.
A and b are then used in the call to the optimizer, e.g.
Now it's your turn to define the objective function and the call to "fmincon" (or some similar optimizer).
(And incidentally the .^2 appears for the summands in the objective :-) )
m = 4;
v1 = ones(m,1);
w1 = -ones(m-1,1);
A1 = diag(v1) + diag(w1,1)
b1 = [zeros(m-1,1);120];
v2 = -ones(m,1);
w2 = ones(m-1,1);
A2 = diag(v2) + diag(w2,1);
A2(end,:) = []
b2 = 1.25*ones(m-1,1);
A = [A1;A2]
b = [b1;b2]
Risposta accettata
Torsten
il 8 Giu 2023
Spostato: Torsten
il 8 Giu 2023
ht = rand(401,51);
[n,m]=size(ht);
I=@(h) sum(sum((ht-h).^2));
h0 = zeros(1,m);
v1 = ones(m,1);
w1 = -ones(m-1,1);
A1 = diag(v1) + diag(w1,1);
b1 = [zeros(m-1,1);120];
v2 = -ones(m,1);
w2 = ones(m-1,1);
A2 = diag(v2) + diag(w2,1);
A2(end,:) = [];
b2 = 1.25*ones(m-1,1);
A = [A1;A2];
b = [b1;b2];
[h,fval,exitflag] = fmincon(I,h0,A,b)
Più risposte (1)
rakshit gupta
il 7 Giu 2023
You can consider following changes to the code to optimize the function while meeting the condition h(j+1)>h(j).
- Modify the Ins cell array to a function handle that takes in the h array.
Ins = @(h) ht - h;
2. Modify the h array to a vector instead of a cell array.
h = zeros(size(lc));
3. Add the upper bound constraint to ensure h(j+1) > h(j).
ub = inf(size(h));
ub(end) = h(end);
4. Modify the constraints function to return the inequality constraint.
function [c, ceq] = constraints(h)
c = h(2:end) - h(1:end-1);
ceq = [];
end
5. Call the fmincon function with the changes made above.
h = fmincon(Ins, h, A, b, Aeq, beq, lb, ub, @constraints);
These changes could help in optimizing the function.
6 Commenti
rakshit gupta
il 8 Giu 2023
Yes, you can try modifying 'h' vector by changing the creation of the 'h' vector to use the same size and data type as 'ht',
h = zeros(size(ht), 'like', ht);
This may help in making Ins scalar.
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