How to obtained matris vector solution using fminunc function?

c_1=[3;5;8];
c_2=[2;6;8];

fun = @(x) (x(1)-c_1).^2 +  (x(2)-c_2).^2;  % cost function 
x0 = [[0.1;0.1;0.1],[0.1;0.1;0.1]];         % İnitial Gues
[x,fval] = fminunc(fun,x0)                  % Results 

Answer 1

[Torsten]

You will have to use fminunc three times:

c_1=[3;5;8];
c_2=[2;6;8];
x1_sol = zeros(size(c_1));
x2_sol = zeros(size(c_1));
for i=1:numel(c_1)
  fun = @(x) (x(1)-c_1(i)).^2 +  (x(2)-c_2(i)).^2;  % cost function 
  x0 = [0.1,0.1];                                   % İnitial Guess
  [x,fval] = fminunc(fun,x0);                       % Results 
  x1_sol(i) = x(1);
  x2_sol(i) = x(2);
end

Local minimum found.

Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.


Local minimum found.

Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.


Local minimum found.

Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.

x1_sol

x1_sol = 3×1

3.0000 5.0000 8.0000

x2_sol

x2_sol = 3×1

2.0000 6.0000 8.0000

Or do you try to solve a different problem ?

[Alexi]

Thank you for your answer I think I can adapt this approach to my original problem.

[Torsten]

Maybe you mean

c_1=[3;5;8];
c_2=[2;6;8];
fun = @(x)sum((x(1)-c_1).^2 + (x(2)-c_2).^2);   % cost function
x0 = [0.1,0.1];                                 % İnitial Guess
[x,fval] = fminunc(fun,x0)                      % Results 

Local minimum found.

Optimization completed because the size of the gradient is less than
the value of the optimality tolerance.

x = 1×2

5.3333 5.3333

fval = 31.3333

?