How to plot an optimization in logarithmic scale?
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The following code is expected to plot the output of Rosenbrock's function against number of iterations ( for the sake of the problem, don't be concerned with the quality of the source code).
function value = banana(x0)
a = x0(1);
b = x0(2);
x = x0(3);
y = x0(4);
value = (1 - x + a)^2 + 100 * (y - b * (x - a)^2)^2;
end
a = int8(4 * rand()) / 2;
b = int8(4 * rand()) / 2;
[x1, y1] = random(a, b);
[x2, y2] = random(a, b);
[x3, y3] = random(a, b);
[x4, y4] = random(a, b);
x = [x1; x2; x3; x4];
y = [y1; y2; y3; y4];
save values.mat;
for i = 1
x0 = [a, b, x(i), y(i)];
options = optimset('PlotFcns', { @optimplotfval });
[solution_point, fval,exitflag,output] = fminsearch(@banana,double(x0),options);
old_x=[x(i),y(i)];
new_x_1 = linspace(-2,2,51);
new_x_2 = linspace(-2,2,51);
for j=1:51
if(new_x_1(j)>= old_x(1))
new_x_1(j)=old_x(1);
break;
end
end
for j=1:51
if(new_x_2(j)>= old_x(2))
new_x_2(j)=old_x(2);
break;
end
end
end

My questions are,
(1) Is this plot really showing the output of Rosenbrock's function against number of iterations?
(2) The plot shows a sudden slump in the output of the function. I need to show this plot in logarithmic form, so that a smooth curve is plotted. How can I do that?
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