CLP(FD): Prolog Integer Arithmetic

It is rather as if the professional community had been suddenly transported to another planet where familiar objects are seen in a different light and are joined by unfamiliar ones as well. (Thomas S. Kuhn, The Structure of Scientific Revolutions)


To fully appreciate declarative integer arithmetic in Prolog, let us first consider arithmetic over natural numbers as a simpler special case.

The natural numbers are defined by the Peano axioms. In particular, 0 is a natural number, and for every natural number n, its successor S(n) is also a natural number. Thus, we could use the following Prolog program to define the set of natural numbers:
natnum(s(N)) :-
In this representation, the natural number 0 is represented by the Prolog integer 0, and the successor of any natural number N is represented by the compound term s(N). This representation is variously called successor arithmetic, successor notation and also Peano arithmetic.

With this representation, we could use the following Prolog program to define addition, which is a relation between two natural numbers and their sum:
nat_nat_sum(0, M, M).
nat_nat_sum(s(N), M, s(Sum)) :-
        nat_nat_sum(M, N, Sum).
This is a pure predicate that terminates if any of the arguments is instantiated. Other elementary relations can be defined analogously. However, such a representation suffers from several significant disadvantages: Therefore, Peano arithmetic—albeit useful to illustrate different ways in which we could represent our data—is not how we typically reason about numbers in Prolog.

Instead, we use built-in predicates to reason about numbers in Prolog. In the case of integers, these predicates are known as CLP(FD) constraints. CLP(FD) stands for Constraint Logic Programming over Finite Domains and reminds us of the fact that in reality, we can only represent a finite subset of integers on actual machines.

All widely used Prolog implementations provide CLP(FD) constraints. However, the exact details differ slightly between various systems. For example, in GNU Prolog, B-Prolog and other systems, CLP(FD) constraints are conveniently available right from the start. In contrast, you need to load a library to use them in SICStus Prolog and other systems.

If your Prolog systems provides CLP(FD) constraints as a library, adjust your initialization file so that this library is automatically available in all your programs. For example, in SICStus Prolog, you can put the following directive in your initialization file:
:- use_module(library(clpfd)).
For SICStus Prolog, also consider using CLP(Z), which is a generalization of CLP(FD).

It is highly advisable to make CLP(FD) or CLP(Z) constraints automatically available in all your programs, since almost all Prolog programs also reason about integers.


Generally, an n-ary constraint is a relation between n variables. For example, (=)/2 and dif/2 fit this definition: These predicates are also constraints! In fact, every predicate you impose can be regarded as a constraint on the set of solutions.

In the following, we are considering CLP(FD) constraints. These are built-in predicates that enable reasoning over integers in a pure and declarative way.

The most important CLP(FD) constraints are the arithmetic constraints (#=)/2, (#>)/2, (#<)/2 and (#\=)/2. There are also several other categories of constraints, such as combinatorial constraints. See your Prolog system's manual for more information.

Evaluating integer expressions

The most basic use of CLP(FD) constraints is evaluation of arithmetic expressions.

To evaluate integer expressions, we use the predicate (#=)/2 to denote equality of arithmetic expressions over integers.

Here are a few examples:
?- X #= 5 + 3.
X = 8.

?- 2 #= X + 9.
X = -7.

?- 1 #= 1 + Y.
Y = 0.
Equality is one of the most important predicates when reasoning about integers. As these examples illustrate, (#=)/2 is a pure relation that can be used in all directions, also of components of its arguments are still variables.

Example: Length of a list

For example, we can readily relate a list to its length as follows:
list_length([], 0).
list_length([_|Ls], Length) :-
        Length #= Length0 + 1,
        list_length(Ls, Length0).
A declarative reading of this predicate makes clear what this relation means: Example query:
?- list_length([a,b,c,d], Length).
Length = 4.
Importantly, this also works in more general cases. For example:
?- list_length(Ls, Length).
Ls = [],
Length = 0 ;
Ls = [_1716],
Length = 1 ;
Ls = [_42, _150],
Length = 2 .
It can also be used in a different direction. For example:
?- list_length(Ls, 3).
Ls = [_898, _1064, _1230] .
Note though that this does not terminate:
?- list_length(Ls, 3), false.
It takes only a single additional goal to make this query terminate. This is left as an exercise.

To avoid the accumulation of constraints, we can introduce an explicit accumulator. By this, we mean a term that represents intermediate states that arise. For example, here is an alternative solution for relating a list to its length:
list_length(Ls, L) :-
        list_length_(Ls, 0, L).

list_length_([], L, L).
list_length_([_|Ls], L0, L) :-
        L1 #= L0 + 1,
        list_length_(Ls, L1, L).
Again, making the goal list_length(Ls, 3) terminate is left as an exercise.


We can reason even more generally over integers. Consider a logical variable V that we want to use as a placeholder for an integer between 0 and 2. We express this on the toplevel using the constraint (in)/2:
?- V in 0..2.
V in 0..2.
If we place additional goals on the toplevel, the system automatically takes into account the admissible set of integers we specified for V, which we call its associated domain. In particular, the system prevents us from unifying V with integers that are not elements of its domain:
?- V in 0..2, V #= 3.
In contrast, the following query succeeds:
?- V in 0..2, V #= 1.
V = 1.
Now two variables X and Y, both constrained to the interval 0..2:
?- X in 0..2, Y in 0..2.
X in 0..2,
Y in 0..2.
Or, equivalently:
?- [X,Y] ins 0..2.
X in 0..2,
Y in 0..2.


We can successively bind a variable to all integers of its associated domain via backtracking, using the enumeration predicate indomain/1:
?- V in 0..2, indomain(V).
V = 0 ;
V = 1 ;
V = 2.
Assigning concrete values to constrained variables is called labeling. The predicate label/1 lifts indomain/1 to lists of variables:
?- [X,Y] ins 0..1, label([X,Y]).
X = Y, Y = 0 ;
X = 0, Y = 1 ;
X = 1, Y = 0 ;
X = Y, Y = 1.
If label/1 were not available, you could define it like this:
label(Vs) :- maplist(indomain, Vs).
Labeling is a form of search that always terminates. This property is of extreme importance for termination analysis and allows us to cleanly separate the modeling part from the actual search.

In practice, the order in which variables are bound to concrete values of their domains matters. For this reason, the predicate labeling/2, which is a generalization of label/1, lets you specify different strategies when enumerating admissible values. A simple and often very effective heuristics is to always label that variable next whose domain contains the smallest number of elements. This strategy is called "first-fail", since these variables are often most likely to cause failure of the labeling process, and trying them early can prune huge portions of the search tree. It is available via labeling([ff], Vars).

For other problems, a static reordering of variables may suffice. With N-queens for example, it is worth trying to order the variables so that labeling starts from the board's center and gradually moves to the borders.

Constraint propagation

From what we have seen above, it follows that we can combine different constraints by stating them as a conjunction. For instance, we can state that Z is the sum of X and Y, both of which are integers between 0 and 2:
?- [X,Y] ins 0..2, Z #= X + Y.
X in 0..2,
Y in 0..2,
Z in 0..4.
Note that the domain of Z is deduced from the posted constraints without being stated explicitly. Narrowing domains based on posted constraints is called (constraint) propagation, and it is performed automatically by the constraint solver. If we bind Z to 0, the system deduces that both X and Y are also 0:
?- [X,Y] ins 0..2, Z #= X + Y, Z #= 0.
X = 0,
Y = 0,
Z = 0.
In this case, propagation yields ground instances for all variables. In other cases, constraint propagation detects unsatisfiability of a set of constraints without any labeling:
?- X in 0..1, X #> 2.
In yet other cases, domain boundaries are adjusted:
?- [X,Y] ins 0..2, Z #= X + Y, Z #= 1.
Z = 1,
X in 0..1,
Y in 0..1.
A set of constraints and variables with associated domains is called (globally) consistent if all variables can be simultaneously bound to (at least) one value of their respective domains such that all constraints are satisfied. In general, all variables must be labeled to find out whether a set of constraints is consistent. However, there exist weaker forms of consistency that are guaranteed without labeling.

For example, the all_different/1 constraint does not detect unsatisfiability of the following:
?- [X,Y,Z] ins 0..1, all_different([X,Y,Z]).
X in 0..1,
all_different([X, Y, Z]),
Y in 0..1,
Z in 0..1.
The all_distinct/1 constraint, in contrast, detects the inconsistency without labeling any variables:
?- [X,Y,Z] ins 0..1, all_distinct([X,Y,Z]).
To guarantee that stronger form of consistency, the all_distinct/1 constraint must do extra work for propagation. It therefore depends on the problem at hand whether it pays off to use it instead of all_different/1. If there are many solutions distributed throughout the whole search space, a naive search for solutions may easily find them, and additional pruning may only make this process slower. On the other hand, if solutions are relatively sparse, the additional pruning of all_distinct/1 can help to more effectively prune irrelevant parts of the search space. In practice, all_distinct/1 is typically fast enough and is preferable due to its much stronger propagation.

Legacy predicates

CLP(FD) constraints have been available only for a few decades. This means that they are still a comparatively recent development in Prolog systems. Most available textbooks have not yet had a chance to take such innovations into account. Many Prolog instructors have completed their formal education before constraints have become widely available. As a result, many Prolog programmers have never even heard of constraints.

Instead of arithmetic constraints like (#=)/2 and (#>)/2, you can also use lower-level arithmetic predicates like (is)/2 and (>)/2 to reason about integers. However, this comes with several hefty drawbacks. For example: For these reasons, I regard (is)/2, (>)/2 and other low-level predicates as legacy predicates. I expect them to gradually disappear from application code when the current generation of Prolog instructors is replaced, and more general techniques are taught instead.

Further reading

CLP(FD) constraints let you solve a large variety of combinatorial optimization tasks and puzzles.

Correctness Considerations in CLP(FD) Systems contains an introduction to CLP(FD) and many pointers to further literature.

Several CLP(FD) examples are available from:

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