sets: ten/1..10/:y; four/1..4/; score(ten,four):a,x; endsets [obj]max=@sum(score(i,j):x(i,j)*a(i,j)); @for(score(i,j):x(i,j)>=y(i)); @for(four(j):@sum(ten(i):x(i,j))<=6); @sum(ten(i):y(i))=4; @for(ten(i):(@sum(four(j):x(i,j)))*(1-y(i))<=3); @for(ten:@bin(y)); @for(score:@bin(x)); M=@sum(score(i,j):x(i,j)*a(i,j)); data: a=9.5 10 9.8 9.9 9.8 9.4 10 9.6 10 9.5 9.5 10 9.5 9.9 9.7 10 9.5 9.7 9.3 9.9 9.9 9.9 9.1 9.5 10 10 9.3 9.8 10 10 9.9 9.8 9.5 9.8 10 9.9 9.7 9.5 9.6 9.8; enddata end model: sets: ten/1..10/:y; four/1..4/; score(ten,four):a,x,D; endsets [obj]min=(236.2-@sum(score(i,j):x(i,j)*a(i,j)))/@sum(score(i,j):x(i,j)*D(i,j))^0.5; @for(score(i,j):x(i,j)>=y(i)); @sum(ten(i):y(i))=4; @for(four(j):@sum(ten(i):x(i,j))<=6); @for(ten(i):@sum(four(j):(1-y(i))*x(i,j))<=3); @for(ten:@bin(y)); @for(score:@bin(x)); M=(236.2-@sum(score(i,j):x(i,j)*a(i,j)))/@sum(score(i,j):x(i,j)*D(i,j))^0.5; D1=(@sum(score:x*D))^0.5; data: a= 9.2500 9.0000 9.5000 9.1000 9.6000 9.0000 9.0000 9.3000 9.0000 9.1000 9.2500 9.8000 9.1000 9.1000 9.5000 9.0000 9.2500 9.4000 8.9000 9.7000 9.7000 9.1000 8.9000 9.2500 9.8000 9.0000 8.9000 9.2000 9.0000 9.8000 9.1000 9.3000 9.2500 9.2000 9.0000 9.7000 9.4000 9.1000 9.2000 9.5000; D= 0.1420 0.1440 0.0380 0.0880 0.0180 0.0800 0.1440 0.0380 0.1440 0.1280 0.1425 0.0180 0.1280 0.0880 0.0380 0.1440 0.1425 0.0380 0.0720 0.0180 0.0180 0.0880 0.0320 0.1425 0.0180 0.1440 0.0720 0.1520 0.1440 0.0840 0.0880 0.1580 0.1425 0.1520 0.1440 0.0320 0.0380 0.1280 0.1280 0.0380; enddata end帮我改成Matlab代码

以下是将该模型转化为MATLAB代码的示例：

% Define Sets
ten = 1:10;
four = 1:4;

% Define Parameters
a = [9.5 10 9.8 9.9 9.8 9.4 10 9.6 10 9.5 9.5 10 9.5 9.9 9.7 10 9.5 9.7 9.3 9.9 9.9 9.9 9.1 9.5 10 10 9.3 9.8 10 10 9.9 9.8 9.5 9.8 10 9.9 9.7 9.5 9.6 9.8];
D = [0.1420 0.1440 0.0380 0.0880 0.0180 0.0800 0.1440 0.0380 0.1440 0.1280 0.1425 0.0180 0.1280 0.0880 0.0380 0.1440 0.1425 0.0380 0.0720 0.0180 0.0180 0.0880 0.0320 0.1425 0.0180 0.1440 0.0720 0.1520 0.1440 0.0840 0.0880 0.1580 0.1425 0.1520 0.1440 0.0320 0.0380 0.1280 0.1280 0.0380];

% Define Variables
y = optimvar('y', ten, 'Type', 'integer', 'LowerBound', 0, 'UpperBound', 1);
x = optimvar('x', ten, four, 'Type', 'integer', 'LowerBound', 0, 'UpperBound', 1);

% Define Objective Function
obj = sum(sum(x.*a));

% Define Constraints
constr = [
    sum(y) == 4;
    sum(x, 1) <= 6;
    sum(x, 2) <= 3*(1-y);
    sum(x.*repmat(y', 1, 4), 1) == 4;
    x >= y;
];

% Define Optimization Problem
prob = optimproblem('Objective', obj, 'Constraints', constr);

% Define Solver Options
options = optimoptions('intlinprog', 'Display', 'off');

% Solve the Problem
[sol, fval, exitflag, output] = solve(prob, 'options', options);

% Display Results
disp("Optimal Solution:");
disp(sol.x);
disp("Optimal Objective Value:");
disp(fval);

注意：由于该模型中存在整数变量，因此需要使用整数线性规划求解器。在MATLAB中，可以使用intlinprog函数来求解整数线性规划问题。在本示例中，我们将Solver Options设置为intlinprog的Display选项为“off”，以避免在求解过程中输出大量信息。