Try our new documentation site (beta).

SOS Constraints

A Special-Ordered Set, or SOS constraint, is a highly specialized constraint that places restrictions on the values that variables in a given list can take. There are two types of SOS constraints. In an SOS constraint of type 1 (an SOS1 constraint), at most one variable in the specified list is allowed to take a non-zero value. In an SOS constraint of type 2 (an SOS2 constraint), at most two variables in the specified, ordered list are allowed to take a non-zero value, and those non-zero variables must be contiguous in the list. The variables in an SOS constraint can be continuous, integer, or binary.

Again, tolerances play an important role in SOS constraints. Specifically, variables that take values less than IntFeasTol (in absolute value) are considered to be zero for the purposes of determining whether an SOS constraint is satisfied.

An SOS constraint is described using a list of variables and a list of corresponding weights. While the weights have historically had intuitive meanings associated with them, we simply use them to order the list of variables. The weights should be unique. This is especially important for an SOS2 constraint, which relies on the notion of contiguous variables. Since the variables in the SOS are ordered by weight, contiguity becomes ambiguous when multiple variables have the same weight.

It is often more efficient to capture SOS structure using linear constraints rather than SOS constraints. The optimizer will often perform this reformulation automatically. This is controlled with four parameters: PreSOS1BigM, PreSOS1Encoding, PreSOS2BigM and PreSOS2Encoding.

The reformulation adds constraints of the form <span>$</span>x \leq M b<span>$</span>, where <span>$</span>x<span>$</span> is the variable that participates in the SOS constraint, <span>$</span>b<span>$</span> is a binary variable, and <span>$</span>M<span>$</span> is an upper bound on the value of variable <span>$</span>x<span>$</span>. Large values of <span>$</span>M<span>$</span> can lead to numerical issues. The two parameters PreSOS1BigM and PreSOS2BigM control the maximum value of <span>$</span>M<span>$</span> that can be introduced by this reformulation. SOS constraints that would require a larger value aren't converted. Setting one of these parameters to 0 disables the corresponding reformulation.

Additionally, there are several known integer formulations for SOS1 and SOS2 constraints. These reformulations differ in the number of variables that they introduce to the problem, in the complexity of the resulting LP relaxations, and in their properties in terms of branching and cutting planes. The two parameters PreSOS1Encoding and PreSOS2Encoding control the choice of the reformulation performed.

Try Gurobi for Free

Choose the evaluation license that fits you best, and start working with our Expert Team for technical guidance and support.

Evaluation License
Get a free, full-featured license of the Gurobi Optimizer to experience the performance, support, benchmarking and tuning services we provide as part of our product offering.
Academic License
Gurobi supports the teaching and use of optimization within academic institutions. We offer free, full-featured copies of Gurobi for use in class, and for research.
Cloud Trial

Request free trial hours, so you can see how quickly and easily a model can be solved on the cloud.