parse error at x0

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Janidu
Janidu 2023-1-4
评论： Voss 2023-1-4
Hello I'm new to matlab coding and here Im getting this parse error and I cannot figure it out. From this code I need to find the minimum of the function using steepest descent method. And I would like to know this code correct or not please.
this is my func : F (x,y) = 10x2−4xy+7y2−455x−y−16
and the starting point is : [ 0,10 ]
thanks for help!
%steepest descent method
function f = target_function(x, y)
f = 10 * x.^2 - 4 * x .* y + 7 * y.^2 - 20 * sqrt(5) * x + 4 * sqrt(5) * y - 16;
end
%parameters
x0 = [0 10];
eps = 0.01;
%initialization
point_min = x0';
target_func_min = target_function(point_min(1), point_min(2));
w = antigradient(point_min);
nw = norm(w);
tau = (1 + sqrt(5)) / 2;
%statistics
points(:, 1) = point_min;
target_func_values(1) = target_func_min;
iter_count = 0;
target_func_calls_count = 0;
%One-Dimensional Minimization Using the Golden Section Method
while nw > eps
iter_count = iter_count + 1;
kp = 0;
step = 0;
while abs(kp - step) < eps
a = step;
b = a + 2;
step = step + 2;
kp1 = ((b * (tau - 1) + a) / tau);
kp2 = ((a * (tau - 1) + b) / tau);
X = point_min + kp1 * w;
f_X1 = target_function(X(1), X(2));
X = point_min + kp2 * w;
f_X2 = target_function(X(1), X(2));
target_func_calls_count = target_func_calls_count + 2;
while norm(b - a) > eps
if (f_X1 > f_X2)
kp = kp2;
a = kp1;
kp1 = kp2;
kp2 = ((a * (tau - 1) + b) / tau);
f_X1 = f_X2;
X = point_min + kp2 * w;
f_X2 = target_function(X(1), X(2));
else
kp = kp1;
b = kp2;
kp2 = kp1;
kp1 = ((b * (tau - 1) + a) / tau);
f_X2 = f_X1;
X = point_min + kp1 * w;
f_X1 = target_function(X(1), X(2));
end
target_func_calls_count = target_func_calls_count + 1;
end
end
point_min = point_min + kp * w;
w = antigradient(point_min);
target_func_min = target_function(point_min(1), point_min(2));
nw = norm(w);
points(:, iter_count + 1) = point_min;
target_func_values(iter_count + 1) = target_func_min;
end
Min = min(min(points(1, :)), min(points(2, :)));
Max = max(max(points(1, :)), max(points(2, :)));
space = linspace(Min - 5, Max + 5, 500);
[gridX, gridY] = meshgrid(space, space);
gridJ = target_function(gridX, gridY);
figure;
contour(gridX, gridY, gridJ, target_func_values, 'black', 'ShowText', 'on');
hold on;
plot(points(1, :), points(2, :), 'r.-');
fprintf(func count: %.0f\n',target_func_calls_count);
fprintf(iter: %.0f\n\n', iter_count);
fprintf(min point: (%2.2f; %2.2f)\n', point_min);
fprintf(min value: %2.2f\', target_func_min);

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采纳的回答

Voss
Voss 2023-1-4
Looks like you are missing a single-quote at the beginning of each of your fprintf calls (and an "n" is missing in the last one):
fprintf(func count: %.0f\n',target_func_calls_count);
fprintf(iter: %.0f\n\n', iter_count);
fprintf(min point: (%2.2f; %2.2f)\n', point_min);
fprintf(min value: %2.2f\', target_func_min);
Should be:
fprintf('func count: %.0f\n',target_func_calls_count);
fprintf('iter: %.0f\n\n', iter_count);
fprintf('min point: (%2.2f; %2.2f)\n', point_min);
fprintf('min value: %2.2f\n', target_func_min);
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Janidu
Janidu 2023-1-4
Ran in:
I did that. But there are still errors. (
%steepest descent method
%parameters
x0 = [0 10];
eps = 0.01;
%initialization
point_min = x0';
target_func_min = target_function(point_min(1), point_min(2));
w = antigradient(point_min);
Unrecognized function or variable 'antigradient'.
nw = norm(w);
tau = (1 + sqrt(5)) / 2;
%statistics
points(:, 1) = point_min;
target_func_values(1) = target_func_min;
iter_count = 0;
target_func_calls_count = 0;
%One-Dimensional Minimization Using the Golden Section Method
while nw > eps
iter_count = iter_count + 1;
kp = 0;
step = 0;
while abs(kp - step) < eps
a = step;
b = a + 2;
step = step + 2;
kp1 = ((b * (tau - 1) + a) / tau);
kp2 = ((a * (tau - 1) + b) / tau);
X = point_min + kp1 * w;
f_X1 = target_function(X(1), X(2));
X = point_min + kp2 * w;
f_X2 = target_function(X(1), X(2));
target_func_calls_count = target_func_calls_count + 2;
while norm(b - a) > eps
if (f_X1 > f_X2)
kp = kp2;
a = kp1;
kp1 = kp2;
kp2 = ((a * (tau - 1) + b) / tau);
f_X1 = f_X2;
X = point_min + kp2 * w;
f_X2 = target_function(X(1), X(2));
else
kp = kp1;
b = kp2;
kp2 = kp1;
kp1 = ((b * (tau - 1) + a) / tau);
f_X2 = f_X1;
X = point_min + kp1 * w;
f_X1 = target_function(X(1), X(2));
end
target_func_calls_count = target_func_calls_count + 1;
end
end
point_min = point_min + kp * w;
w = antigradient(point_min);
target_func_min = target_function(point_min(1), point_min(2));
nw = norm(w);
points(:, iter_count + 1) = point_min;
target_func_values(iter_count + 1) = target_func_min;
end
Min = min(min(points(1, :)), min(points(2, :)));
Max = max(max(points(1, :)), max(points(2, :)));
space = linspace(Min - 5, Max + 5, 500);
[gridX, gridY] = meshgrid(space, space);
gridJ = target_function(gridX, gridY);
figure;
contour(gridX, gridY, gridJ, target_func_values, 'black', 'ShowText', 'on');
hold on;
plot(points(1, :), points(2, :), 'r.-');
fprintf('func count: %.0f\n',target_func_calls_count);
fprintf('iter: %.0f\n\n', iter_count);
fprintf('min point: (%2.2f; %2.2f)\n', point_min);
fprintf('min value: %2.2f\', target_func_min);
function f = target_function(x, y)
f = 10 * x.^2 - 4 * x .* y + 7 * y.^2 - 20 * sqrt(5) * x + 4 * sqrt(5) * y - 16;
end
Voss
Voss 2023-1-4
I also got the error about "antigradient" being unrecognized. What is it?

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