The variable $x$ is known in math programming jargon as

*semicontinuous*. For whatever reason, the term is not well publicized; I taught a graduate course on integer programming for years without coming across it. Some solvers can model these variables directly. For instance, the C++ and Java APIs for CPLEX contain a class named IloSemiContVar. I'm not sure which other solvers directly support semicontinuous variables. For solvers that do not recognize semicontinuous variables, there is a generic formulation. You introduce a binary variable $y$ that will be 1 if $x$ is nonzero and 0 if $x$ is zero, and you add the contraints $x\ge ay$ and $x\le by$.

AMPL allows you to define a semicontinuous variable without explicitly adding constraints. Our example could be written in AMPL as -->

param a > 0; param b > a; var x in {0} union interval[a, b];Currently, AMPL converts that into the pair of constraints mentioned above, even if the chosen solver is CPLEX. Whether that will change in the future I do not know.

One potential virtue of directly modelling semicontinuous variables is that it may lead to a more parsimonious formulation. If the solver understands semicontinuous variables, it can modify the branching logic to separate nodes based on $x=0$ versus $x\ge a$ without having to add the binary variable $y$ and the extra constraints.

My 2 cents to add from past experience with the semi-cont approach in CPLEX on some practical examples.

ReplyDelete1. It is a cool and useful feature to have, but should be used with caution due to the 'big M' like structure hidden inside the resultant MIP.

2. In many practical instances, the naive LP relaxation is weak.

3. So, if possible, better to branch directly on the semi-cont. conditions (Nemhauser and Farias) rather than on the auxiliary binary variables x.

sorry, pt3 should read "... rather than on the auxiliary binary variables y".

ReplyDelete@Shiva: Is it your impression that CPLEX inserts the auxiliary binary variable (and constraints) when it sees a semicontinuous variable? I thought it was branching on the condition (your third point). If so, that raises the question of how the sc variable's domain factors into LP relaxations prior to the branch (is it treated as having domain [0, b] until then, which would be my guess).

ReplyDeleteYeah, that's what i thought. But i must confess that i used this feature practically 2-3 yrs ago, and it must have been least 2 versions prior to 12.x (we didn't have access to latest at that time). One would expect that by now, they would have implemented the best proven approach that can be practically integrated into an already complex code.

ReplyDeletePerhaps printing out the CPLEX-constructed .lp relaxation for a small example may give us a clue.

The LP relaxation apparently just treats the lower bound as zero. At least, if I set up a model with a semicontinuous variable and then add a conversion object to convert it to an ordinary float variable, the upper bound is preserved and the lower bound is set to zero.

ReplyDeletePaul,

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@bharath: Thanks for the kind words. As it turns out, I'm among the last people on the planet to hear about OR job openings -- we don't offer any OR degrees here (we did 20+ years ago), so we don't have any recruiting for OR positions here. INFORMS has a "jobs bank" (http://www.informs.org/Build-Your-Career/OR-MS-Job-Bank), and there are occasional postings on LinkedIn either tagged OR or posted in the INFORMS group there. There was some discussion of job listings on OR-Exchange a while back, too.

ReplyDelete@bharat I understand your request for OR job listing, sadly there are few (or none) really good listings. The information is squattered in various places and hard to find since the traditional key words is not really useful. Often companies look for other skills combined with OR.

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ReplyDeleteHas anybody found out for sure how cplex treats semicontinuous variables? Is it using auxillary binary variables or not?

ReplyDeletePer an inside source, if CPLEX deems the bound on a semicontinuous variable to be reasonable, it linearizes using a binary variable (effectively creating a big-M constraint). If the bound is deemed too large, it uses an indicator constraint. How the indicator constraint is then handled can be seen in the quote from Ed Klotz embedded in an answer by Mark L. Stone to a question about indicators in CPLEX: https://or.stackexchange.com/questions/231/when-to-use-indicator-constraints-versus-big-m-approaches-in-solving-mixed-int/348#348.

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