This is (hopefully) an easy answer but I haven't dealt with this before.

I have a MILP which includes an unbounded, continuous decision variable. However, I generally don't want this decision variable to take negative values, so I want to include a penalty in the objective function where there is zero penalty incurred for a positive value, but for negative values, the penalty is proportional to the absolute value. Basically, I want a value of -100 to be penalized twice as much as -50, and 10x as much as -10.

Right now I have formulated an indicator variable that is 1 if the decision variable is negative, which incurs a penalty; but this penalty is the same for any negative value.

Any suggestions on how to implement this?


2 Answers 2


If $x$ denotes your free variable, you can penalize the term $f(x)=\max\{-x,0\}$ in your objective function, which you can linearize by replacing it with a variable $y$, and constraints $y\ge -x, y\ge 0$.

  • $\begingroup$ Another good approach. +1 $\endgroup$
    – RobPratt
    Mar 7, 2022 at 20:46
  • 1
    $\begingroup$ I like this a lot. I'll then just put -y in the objective function (since it's a maximization). Thanks $\endgroup$ Mar 8, 2022 at 2:21

You do not need to introduce an indicator variable. Suppose $x$ is your free variable. Introduce nonnegative variables $x^+$ and $x^-$, replace $x$ with $x^+-x^-$ throughout, and penalize $x^-$ linearly in the objective.


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