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Optimization with a linear objective function, subject to linear equality and linear inequality constraints.
2
votes
Need help optimizing the loading of passengers on small airplanes
This is an optimization problem.
The first step is to work out more precisely what your objective function is. In other words, given a candidate assignment of pilots, you need a well-specified way t …
0
votes
Writing a linear program to model balanced bin packing
Define a variable $b$ that represents the imbalance between the lengths of the parking lanes. For instance, if $b=5$, that means that the lengths of every pair of parking lanes differs by at most 5. …
1
vote
Accepted
Suppose we have two variables $x,y \in [1,n]$. How can we write $x \neq y$ in integer-linear...
With linear programming: you can't.
With integer linear programming, here is one solution: introduce zero-or-one variable $t$, and the inequalities
$$x \le y - 1 + (1-t)n, \qquad x \ge y+1 - tn.$$
Int …
0
votes
Linear programming vs integer linear programming
Yuval Filmus suggests a counterexample: $x_1 + 2x_2 = 2$, $0 \le x_1 \le 1$, $0 \le x_2 \le 1$.
Here there is a feasible solution to the LP instance with $x_1=1$, namely, $x_1=1$, $x_2=1/2$.
However t …
1
vote
Accepted
Infeasible linear programming reduce errors to find solution
One approach: replace each inequality $a_1 x_1 + \dots + a_n x_n \le b$ by $a_1 x_1 + \dots + a_n x_n \le b + t$, replace each inequality $a_1 x_1 + \dots + a_n x_n \ge b$ by $a_1 x_1 + \dots + a_n x_ …
2
votes
Finding a set of maximally different solutions using linear programming or other optimizatio...
A heuristic, using linear programming
One approach might be to pick a random objective function, and maximize it. Then repeat, with a different set of objective functions each time.
In other words, …
1
vote
Accepted
Help wrapping my head around a combinatorial optimization problem
I recommend you use an integer linear programming (ILP) solver to approach this. It will be relatively easy to code this up, and the resulting solution will probably out-perform any other simple sche …
1
vote
Cast to boolean, for integer linear programming
Here's a solution that uses two temporary variables. Let $t,u$ be integer zero-or-one variables, with the intended meaning that $t=1$ if $x \ge 0$, $u=1$ if $x \le 0$, and $y=\neg(t \land u)$. These …
3
votes
Accepted
Use max operation in a constraint in Linear Programming
Introduce variables $m_1,m_2,m_3$ to represent the three maxes.
Add the linear inequality $m_1 + m_2 + m_3 \ge q$.
Then, add the following extra inequalities for $m_1$:
$0 \le m_1 \le 1$
$m_1 \le …
2
votes
Accepted
Does there always exist equivalent (M)(I)LPs with and without objective functions?
Yes (assuming you truly did mean "if" rather than "if and only if").
Add any objective function you want (e.g., 1, or the sum of the variables, or anything), then feed it to a MILP solver and see whe …
5
votes
Accepted
Converting If-else condition to Linear Programming
This can be expressed with just the equation $X=Y$. Since $X,Y$ are zero-or-one variables, the only possible assignments that are consistent with your condition are $X=Y=0$ and $X=Y=1$.
See Express …
2
votes
Accepted
Linear programming, Checking a constraint based on condition
If $X$ and $Y$ are zero-or-one (binary) integer variables, then this is encoded as
$$X \ge Y.$$
Why does this work? If $Y=1$, then this enforces the constraint $X \ge Y$, as you wanted. If $Y=0$, …
2
votes
Accepted
Express a "complex" IF-Statement to Linear Programming
You can solve this by introducing some temporary variables ($t,u,v$) and applying the big-M method.
For each expression L <= b <= U, we'll introduce 0-or-1 variables $t,u,v$, with the intent that $u= …
1
vote
Better way to formulate these constraints?
If your measure of "better" is number of variables or number of constraints, I don't believe it is possible to do better. You obviously can't use fewer variables as all of them are already defined; yo …
2
votes
Accepted
How to efficiently specify a MILP constraint with nested AND and ORs
It can be done with two auxiliary variables, y1,y2:
x1 >= y1
x1 >= y2
x1 <= y1 + y2
x2 + x3 + x4 >= 3*(y2 - y1)
x2 + x3 + x4 <= 2 + y1 + y2
x5 + x6 >= 2*(y1 - y2)
x5 + x6 <= 1 + y1 + y2
x7 >= y1 + …