Minimize a function using gradient descent
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How can we minimise the following function using gradient descent (using a for loop for iterations and a surface plot to display a graph that shows the minimisation)
% initial values: x = y = 2
z = 2*(x^2) + 3*(y^2);
采纳的回答
X = -2:0.1:2;
Y = -2:0.1:2;
[X,Y] = meshgrid(X,Y);
Z = 2*X.^2+3*Y.^2;
surf(X,Y,Z)
hold on
x(1) = 2; % initial value of x
y(1) = 2; % initial value of y
z(1) = 2.*x(1).^2 + 3.*y(1).^2;
stepsize = 0.1;
for i = 1:30
zx = 4*x(i);
zy = 6*y(i);
x(i+1) = x(i) - stepsize*zx; %gradient descent
y(i+1) = y(i) - stepsize*zy;
z(i+1) = 2.*x(i+1).^2 + 3.*y(i+1).^2
end
plot3(x,y,z,'Markersize',10,'Color','red')
hold off
更多回答（1 个）
Let us visualize and formulate the minimization problem first. So you want to start descending from the point 
, circled in the image. The contour plot can give you an estimation where you are heading to from the starting point.
, circled in the image. The contour plot can give you an estimation where you are heading to from the starting point.f = @(x,y) 2*(x.^2) + 3*(y.^2);
[x,y] = meshgrid(-2.5:0.25:2.5, -2.5:0.25:2.5);
z = f(x, y);
[fx, fy] = gradient(z, 0.25);
cs = contour(x, y, z);
axis square
clabel(cs);
hold on
plot(2, 2, 'ro', 'linewidth', 1.5)
quiver(x, y, -fx, -fy);
hold off
xlabel('x')
ylabel('y')
We try to first obtain the solution with the fminsearch() function. Then, we can write the gradient descent algorithm to compare with the result.
fun = @(x) 2*(x(1).^2) + 3*(x(2).^2);
[x, fval] = fminsearch(fun, [2, 2])
x =
1.0e-04 *
0.0707 -0.3490
fval =
3.7533e-09
Surface plot with the mesh() function:
[x, y] = meshgrid(-3:0.375:3);
z = 2*(x.^2) + 3*(y.^2);
[u, v] = gradient(z, 0.375);
w = 1;
magnitude = sqrt(u.*u + v.*v + w.*w);
u = u./magnitude;
v = v./magnitude;
w = w./magnitude;
mesh(x, y, z)
axis square
xlabel('x');
ylabel('y');
zlabel('z');
hold on
quiver3(x, y, z, -0.75*u, -0.75*v, w, 0)
hold off
1 个评论
Tatiana Danilova
2022-7-7
Minimize a cost function using gradient descent
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