I dislike big M’s and I cannot lie

Big M’s in integer programming formulations have been a topic of discussion on Twitter and the blogosphere in the past week. For example, see Marc-Andre Carle‘s posts (primal and dual) and followup tweets and also some older posts from Paul Rubin and Bob Fourer.

My initial reaction was don’t let the software handle it. If possible, try to reformulate the problem.

In this post, I explain myself and give supporting computational experiments.

Example: The uncapacitated facility location problem

Consider the uncapacitated facility location (UFL) problem. The task is to open a subset of n available facilities to service a set of m customers. The goal is to minimize the sum of facility opening costs and customer-delivery costs.

The parameters for this problem include the cost f_i to open facility i and the cost c_{ij} associated with fulfilling customer j‘s demand via facility i. We allow for a facility to fulfill a fractional amount of a customer’s demand, in which case the demand-fulfillment costs are proportional.

1. The disaggregated formulation

One natural formulation is as follows, where x_{ij} is the proportion of customer j‘s demand that is fulfilled by facility i, and y_i is a binary variable representing the decision to open facility i.

The objective is to minimize the total cost:

\min \sum_{i=1}^n \sum_{j=1}^m c_{ij} x_{ij} + \sum_{i=1}^n f_i y_i.

Each customer’s demand should be fulfilled:

\sum_{i=1}^n x_{ij}=1,~~j =1,\dots, m.

If a facility is closed, then it can fulfill none of a customer’s demand:

x_{ij} \le y_i,~~i=1,\dots, n,~j=1,\dots, m.

Finally, the x variables should be nonnegative, and the y variables should be binary.

2. The big-M formulation

In the big-M formulation, everything is the same, except that the x_{ij} \le y_i constraints are replaced by the constraints:

\sum_{j=1}^m x_{ij} \le M y_i.

To keep the formulation as strong as possible, the value used for M should be as small as possible. In this case, we should set M=m, which is also what you get when you aggregate the x_{ij} \le y_i constraints. (Carle’s post from above explores what happens when you use unnecessarily large M’s in situations like this.)

Which formulation is “better”?

Depending on who you ask, either of these formulations could be better. Many textbooks on integer programming prefer the first, since its LP relaxation is much stronger, meaning that the number of branch-and-bound nodes that will be explored is fewer. Results for 5 different benchmark instances with m=n=100 show that these bounds can indeed be much stronger.

LP 1 objective LP 2 objective
123unifS                 67,441                 12,271
623unifS                 70,006                 12,674
1823unifS                 69,649                 12,066
2623unifS                 68,776                 11,958
3023unifS                 71,639                 12,170

On the other hand, the big-M formulation has only O(n+m) constraints (if we exclude nonnegativity bounds), whereas the first formulation has \Omega(nm) constraints. So, the time to solve the LP relaxation for the big-M formulation is probably less. Indeed, the recent textbook by Conforti, Cornuéjols, and Zambelli states:

When the aggregated formulation is given to state-of-the-art solvers, they are able to detect and generate disaggregated constraints on the fly, whenever these constraints are violated by the current feasible solution. So, in fact, it is preferable to use the aggregated formulation because the size of the linear relaxation is much smaller and faster to solve.

However, MIP solvers rely on sparse matrix representation, meaning that the number of nonzeros in the formulation is more indicative of its size than the quantity obtained by multiplying the number of variables by the number of constraints. Indeed, the number of nonzeros to impose the x_{ij} \le y_i constraints is only about twice the number of nonzeros to impose the big-M constraints.

So, I was not exactly convinced that the time to solve the LP relaxations would be much different and decided to run some experiments. Here are the times (in seconds) to solve the LP relaxations for the same 5 benchmark instances in Gurobi 7.0.2.

Solve time for LP 1 Solve time for LP 2
123unifS                   0.115                   0.022
623unifS                   0.125                   0.023
1823unifS                   0.107                   0.022
2623unifS                   0.128                   0.022
3023unifS                   0.150                   0.024

While the LP times are indeed longer for the disaggregated formulation, it is only by a factor of 5 or 6, even though the number of constraints has increased by a factor of 50!

However, what we are actually interested in is the time to solve IP. By this measure, the disaggregated formulation wins though both times are reasonable for these sized instances. Experiments for larger instances are in the appendix.

Solve time for IP 1 Solve time for IP 2
123unifS                   3.496                   8.151
623unifS                   1.607                   7.176
1823unifS                   1.528                   4.468
2623unifS                   1.263                   3.415
3023unifS                   4.932                 14.153


I have only provided test results for 5 UFL instances, so I cannot make any general conclusions about the (dis)advantages of big M’s from this post. However, I can say that one should at least look for alternative formulations that do not rely on big M’s and then empirically compare them.

I leave with a rhetorical question. Where would the field of integer programming be if everyone concluded that the Miller-Tucker-Zemlin formulation for TSP was good enough (even though it has big M’s)? What if they said “just let the solver take care of it”?


I constructed larger instances following the same procedures that were used when making the m=n=100 instances. Namely, each facility has an opening cost of 3000, and the assignment costs are uniformly distributed integers between 0 and 1000. (Actually, I used rand() % 1001 in C++, which is slightly biased to smaller numbers.)

The LP times are much like before.

Solve time for LP 1 Solve time for LP 2
n=100,m=150 0.437 0.057
n=100,m=200 0.506 0.076
n=150,m=100 0.422 0.070
n=200,m=100 0.641 0.083

So are the IP times.

Solve time for IP 1 Solve time for IP 2
n=100,m=150 178.049 225.599
n=100,m=200 420.491 501.478
n=150,m=100 51.808 153.065
n=200,m=100 110.433 143.192

Somewhat surprisingly, the IP time for formulation 2 decreased when increasing the number of facilities from 150 to 200. Probably the solver got lucky.

I have designated the x variables as continuous in the formulation, but there will always be an optimal solution in which they are binary. Given that there are more presolve procedures that apply to binary variables than continuous variables, it might help to declare them as binary. I tested the idea on a randomly constructed instance with m=n=100, but the continuous setting was better.

binary x continuous x
Solve time for IP 1 25.577 7.405
Solve time for IP 2 46.358 12.897

2 thoughts on “I dislike big M’s and I cannot lie

  1. Pierre Bonami

    I agree with your conclusion that one should always look for alternative formulations (and actually always in integer programming) but it also always depends on the underlying algorithm used.

    It would be interesting to know what the comparison would look like if you use a Benders algorithm (like the one in the latest CPLEX). I don’t know the answer, but it might be different.

  2. Paul A. Rubin

    At a conference workshop shortly after the introduction of indicator constraints in CPLEX, an IBM presenter (responding to a question, I think) said that if you knew a tight values for M, you would probably be better off using it than using indicators (and essentially relying on CPLEX to derive a value for M). I don’t know if that advice still holds, but I’m inclined to think so.


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