If I have a set of linear constraints in which each constraint has at most (say) 4 variables (all nonnegative and with {0,1} coefficients except for one variable that can have a -1 coefficient), what is known about the solution space? I am less concerned with an efficient solution (although please indicate if one is known) than with knowing how small the minimum of the objective function can be, as a function of the number of variables and number of constraints, and the number of variables per constraint.
More concretely, the program is something like
minimize t
subject to
for all i, x_i is a positive integer
x1 + x2 + x3 - t < 0
x1 + x4 + x5 - t < 0
...
x3 + x6 - t ≥ 0
x1 + x2 + x7 - t ≥ 0
...
If a concrete question is needed, then is it the case that the minimum solution obeys t <= O(max{# of variables, # of constraints}), with the constant in the O() depending on the sparseness? But even if the answer is no, I am more interested in knowing what kind of textbook or paper to study for a discussion of such issues, and if there is an area of study devoted to this kind of thing but I just don't know the terms to search for. Thank you.
Update: With further reflection (and thinking through the rather simple reduction of 3SAT to ILP, which uses constraints with three variables), I realize that the issue of coefficients is critical (if there is going to be an efficient algorithm). More precisely, all x_i variables have 0 or 1 coefficients (with at most three 1 coefficients in any one constraint), and all t variables have -1 coefficients, and all comparisons have variables on the left and 0 on the right. I updated the above example to clarify.