The number of rows in A must be the same as the number of elements of b error

[x, fval, exitflag] = maximize_profit

Error using linprog
The number of rows in A must be the same as the number of elements of b.

Error in solution>maximize_profit (line 25)
[x, fval, exitflag] = linprog(c, A, b, Aeq, beq, lb, ub);

function [x, fval, exitflag] = maximize_profit()
% Define the initial guess for decision variables as a vector
x0 = zeros(40, 1);
% Define the coefficients of the objective function as a double vector
c = [-6, 3, 17, 10, 63, 34, 15, 22, -2, 15, ...
    -11, -7, -16, 9, 49, 16, 4, 10, -8, 8, ...
    7, 3, 16, 13, 60, 25, 12, 19, 4, 13, ...
    -4, 0, -3, -48, -15, -7, -17, -9, -3];
% Define the constraint matrix A
A = [eye(10), zeros(10, 30);       % X1a to X1j constraints
    zeros(10, 10), eye(10), zeros(10, 20);  % X2a to X2j constraints
    zeros(10, 20), eye(10), zeros(10, 10);  % X3a to X3j constraints
    zeros(10, 30), eye(10)];       % X4a to X4j constraints
% Define the right-hand side of inequality constraints (b)
b = [30; 40; 50; 60];
% Define the linear equality constraints matrix Aeq
Aeq = [];
beq = [];
% Define the lower and upper bounds for the decision variables
lb = zeros(40, 1);
ub = [];
% Solve the optimization problem
[x, fval, exitflag] = linprog(c, A, b, Aeq, beq, lb, ub);
end

[Dyuman Joshi]

Your definition of the optimization problem is not consistent -

c is 1x39, A is 40x40, b is 4x1.

It seems c is missing an element and b is supposed to be 40x1.

If I were to guess, b might be -

b = [30*ones(10,1); 40*ones(10,1); 50*ones(10,1); 60*ones(10,1)];

However, only you know what you are attempting to solve. So, make the appropriate changes and your code should be working fine.

[Kanav]

This is the actual word problem I am trying to code. Maybe this will help you understand why and where I am facing the error?

Answer 1

[Torsten]

% Define the coefficients of the objective function as a double vector
c = -[-6, 3, 17, 10, 63, 34, 15, 22, -2, 15, ...
      -11, -7, -16, 9, 49, 16, 4, 10, -8, 8, ...
      7, 3, 16, 13, 60, 25, 12, 19, 4, 13, ...
      -1, 0, 13, 3, 48, 15, 7, 17, 9 3];
% Define the constraint matrix A
A = [eye(10),eye(10),eye(10),eye(10)];
b = [30;30;20;20;10;20;20;10;30;10];
% Define matrix Aeq
Aeq = [ones(1,10),zeros(1,10),zeros(1,10),zeros(1,10);
       zeros(1,10),ones(1,10),zeros(1,10),zeros(1,10);
       zeros(1,10),zeros(1,10),ones(1,10),zeros(1,10);
       zeros(1,10),zeros(1,10),zeros(1,10),ones(1,10)];
beq = [30;40;50;60];
% Define the lower and upper bounds for the decision variables
lb = zeros(40, 1);
ub = Inf*ones(40,1);
% Solve the optimization problem
[x, fval, exitflag] = linprog(c, A, b, Aeq, beq, lb, ub);

Optimal solution found.

x(1:10).'

ans = 1×10

0 0 0 0 0 20 10 0 0 0

x(11:20).'

ans = 1×10

0 0 0 20 0 0 10 0 0 10

x(21:30).'

ans = 1×10

30 10 0 0 10 0 0 0 0 0

x(31:40).'

ans = 1×10

0 0 20 0 0 0 0 10 30 0