I know that similar questions have been answered several times, and based on the answers, I attempted a solution to my problem. But I simply do not get the right results. The problem is as follows. I wish to solve the following optimization:
$max(ax_1)$ subject to $x_1 = |x_2-x_3|$
where $a$ can be both positive and negative.
I tried to set up the a "Big-M" method based Binary LP (The bounds are well known and reasonably small.) by using the following constraints:
where p is binary.
The bounds are $0 \leq x_1 \leq U$, where $U << M$; $-\infty \leq x_2 \leq \infty $; $-\infty \leq x_3 \leq \infty$
But this did not work. Is my above approach correct? Am I doing something wrong because of non-convexity and because of lack of closed-form solutions (as explained in Linear programming with absolute values)?
If yes, are there any alternatives? I am stuck!
This is the theoretical part.
In practice, I am having an issue with the following problem. $max(x_1(1)+x_1(2)+x_1(3))$
$x_1(1) = |x_2(2)-x_2(1))|$
$x_1(2) = |x_2(3)-x_2(2))|$
$x_1(3) = |x_2(4)-x_2(3))|$
the bounds are $0\leq x_1(1) \leq 4$, $0 \leq x_1(2) \leq 12$, $0\leq x_1(3) \leq 11$. for $x_2(1,...,4)$, the bounds are [-inf inf].
I expect the fval to be 27 so that I get x_1(1,...,3) = 4, 12, 11. but I get fval to be 4 and x_1(1,...,3) = 4, 0, 0.
I suppose I am making a big blooper somewhere, but I cant figure out where...!