How do I plot the value at each iteration?

My code below is to compute the minimum value and vector using the conjugate gradient method. How do i plot the value at each iteration? Thanks.

%this program computes the optimal solution of a nonlinear programming
%using the conjugate-gradient method
%xstar - the global optimal solution
%value - optimal solution
function[xstar, minv] = conj_grad(Q,x,q,c,n,tol)
tic;
g0 = (0.5*Q'*x) + (0.5*Q*x) + q; %g is the gradient
d = -g0;
b = (-g0) + Q*x;
g_old = g0;

for i = 1:n
    alpha = -(g_old'*d)/(d'*Q*d)
    x = x + (alpha*d)
    g_new = (Q*x) - b
    beta = (g_new'*Q*d)/(d'*Q*d)
    res = norm(g_new) %res = residual which is the min distance
    value = (0.5*x'*Q*x) +(q'*x) + c
    if res < tol
         xstar = x
         minv = value
         fprintf("The minimum value is %d and it converged after %d iterations \n", value, i)
         break
    end
    d = (-g_new) + (beta*d)
    g_old = g_new
end
toc 
end

Answer 1

[Cris LaPierre]

I shared a simple example in a similar post recently. That might help.

[SA]

Hi Cris,

I went through the code but I don't need a need figure for each itereation. I just want one figure that has each itereation and its corresponding value

[Cris LaPierre]

There were two options shown in that post. The first one does what you want. The main difference here is that example used a while loop. You can replace it with a for loop instead.

[SA]

Hi Cris,

I don't really understand the code so I am unable to implement it in mine

[Cris LaPierre]

Fair enough. It's unclear to me which value(s) you want plotted. If you can write the plot command you want, I can help you insert it into your code.

[SA]

At each itreation (i) a res(residue) and value is obtained. I want to plot each i against its corresponding value and each i against its corresponding res, that is

plot(i,value)

plot(i,res).

thanks

[Cris LaPierre]

This assumes res and value can be plotted on the same axes.

%this program computes the optimal solution of a nonlinear programming
%using the conjugate-gradient method
%xstar - the global optimal solution
%value - optimal solution
function[xstar, minv] = conj_grad(Q,x,q,c,n,tol)
tic;
g0 = (0.5*Q'*x) + (0.5*Q*x) + q; %g is the gradient
d = -g0;
b = (-g0) + Q*x;
g_old = g0;

for i = 1:n
    alpha = -(g_old'*d)/(d'*Q*d)
    x = x + (alpha*d)
    g_new = (Q*x) - b
    beta = (g_new'*Q*d)/(d'*Q*d)
    res = norm(g_new) %res = residual which is the min distance
    value = (0.5*x'*Q*x) +(q'*x) + c
    
    % Plot res, value
    plot(i,res,'ro',i,value,'gs')
    hold on
    
    if res < tol
         xstar = x
         minv = value
         fprintf("The minimum value is %d and it converged after %d iterations \n", value, i)
         break
    end
    d = (-g_new) + (beta*d)
    g_old = g_new
end
hold off
toc 
end

[SA]

It worked Cris. Thanks so much for the help.