How to obtained matris vector solution using fminunc function?

Question

Alexi 2023-3-25

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1935024-how-to-obtained-matris-vector-solution-using-fminunc-function

编辑： Torsten 2023-3-25

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 

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Answer 1

Torsten 2023-3-25

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1935024-how-to-obtained-matris-vector-solution-using-fminunc-function#answer_1200764

编辑：Torsten 2023-3-25

在 MATLAB Online 中打开

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 ?

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Alexi 2023-3-25

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

Torsten 2023-3-25

编辑：Torsten 2023-3-25

在 MATLAB Online 中打开

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