I'm afraid so, yes. Two main points:
1. The function to minimize has to be linear one (hence the name linear program) and has to be passed as a numerical vector. Meaning: You need to reformulate your problem as fit(q) = f'*q, and then pass f to intlinprog.
2. The correct order of arguments is
X = intlinprog(f,intcon,A,b,Aeq,beq,LB,UB)
If your problem does not have certain parts, e.g. the equality constraints, then you need to pass empty matrices [] instead. You can't just leave them away.
I put together a little dummy example to show you how this works:
function x_sol = my_int_minimizer()
% simple example:
% min( -x1 -x2 + x3)
% s.t.
% (1.) x1 + x2 + x3 <= 5.5
% (2.) x2 <= 2
% (3.) x3 >= 1
% (4.) x1 integer
% vector of unknowns: x, dimension 3x1
% function to minimize: J = f'*x
f = [ -1 -1 1 ]';
% integer constraint (4.)
intcon = 1;
% inequality constraint A*x <= b (1.)
Aineq = [ 1 1 1 ];
bineq = 5.5;
% equality constraints (none)
Aeq = [];
beq = [];
% lower / upper bounds
lb = [ -inf -inf 1 ]'; % (3.)
ub = [ inf 2 inf ]'; % (2.)
% solve problem
x_sol = intlinprog(f,intcon,Aineq,bineq,Aeq,beq,lb,ub);
% output solution
disp(x_sol)
end
Cheers
