Find values of x that minimize
f(x) = (x1 – 0.2)2 + (x2– 0.2)2 + (x3– 0.2)2,
where
for all values of w1 and w2 over the ranges
1 ≤ w1 ≤
100,
1 ≤ w2 ≤
100,
starting at the point x = [0.25,0.25,0.25].
Note that the semi-infinite constraint is two-dimensional, that is, a matrix.
First, write a file that computes the objective function.
function f = myfun(x,s) % Objective function f = sum((x-0.2).^2);
Second, write a file for the constraints, called mycon.m.
Include code to draw the surface plot of the semi-infinite constraint
each time mycon is called. This enables you to
see how the constraint changes as X is being minimized.
function [c,ceq,K1,s] = mycon(X,s)
% Initial sampling interval
if isnan(s(1,1)),
s = [2 2];
end
% Sampling set
w1x = 1:s(1,1):100;
w1y = 1:s(1,2):100;
[wx,wy] = meshgrid(w1x,w1y);
% Semi-infinite constraint
K1 = sin(wx*X(1)).*cos(wx*X(2))-1/1000*(wx-50).^2 -...
sin(wx*X(3))-X(3)+sin(wy*X(2)).*cos(wx*X(1))-...
1/1000*(wy-50).^2-sin(wy*X(3))-X(3)-1.5;
% No finite nonlinear constraints
c = []; ceq=[];
% Mesh plot
m = surf(wx,wy,K1,'edgecolor','none','facecolor','interp');
camlight headlight
title('Semi-infinite constraint')
drawnowNext, invoke an optimization routine.
x0 = [0.25, 0.25, 0.25]; % Starting guess [x,fval] = fseminf(@myfun,x0,1,@mycon)
After nine iterations, the solution is
x
x =
0.2522 0.1714 0.1936and the function value at the solution is
fval
fval =
0.0036The goal was to minimize the objective f(x)
such that the semi-infinite constraint satisfied K1(x,w) ≤ 1.5. Evaluating mycon at
the solution x and looking at the maximum element
of the matrix K1 shows the constraint is easily
satisfied.
[c,ceq,K1] = mycon(x,[0.5,0.5]); % Sampling interval 0.5 max(max(K1)) ans = -0.0332
This call to mycon produces the following
surf plot, which shows the semi-infinite constraint at x.
