Characterizing the integer points in 2-decomposable polyhedra by closedness under operations
Abstract
Characterizing the solution sets in a problem by closedness under operations is recognized as one of the key aspects of algorithm development, especially in constraint satisfaction. An example from the Boolean satisfiability problem is that the solution set of a Horn conjunctive normal form (CNF) is closed under the minimum operation, and this property implies that minimizing a nonnegative linear function over a Horn CNF can be done in polynomial time. In this paper, we focus on the set of integer points (vectors) in a polyhedron, and study the relation between these sets and closedness under operations from the viewpoint of 2-decomposability. By adding further conditions to the 2-decomposable polyhedra, we show that important classes of sets of integer vectors in polyhedra are characterized by 2-decomposability and closedness under certain operations, and in some classes, by closedness under operations alone. The most prominent result we show is that the set of integer vectors in a unit-two-variable-per-inequality polyhedron can be characterized by closedness under the median and directed discrete midpoint operations, each of these operations was independently considered in constraint satisfaction and discrete convex analysis.
1 Introduction
In this paper, we study the set of integer points (vectors) in a polyhedron, where a polyhedron is defined as a set of real vectors satisfying a system of linear inequalities. Polyhedra have been intensively studied in mathematical optimization and computer science, where they arise as constraints in (integer) linear programming problems [18, 20]. Since integer feasibility for a given polyhedron, i.e., determining whether there exists an integer vector in the polyhedron, is NP-hard, a special attention has been paid to polyhedra described by a system of linear inequalities of special form. A typical example is polyhedra described by a difference constraint (DC) system (which we call DC polyhedra), where each inequality is of the form or for some integer . Integer feasibility for DC polyhedra is known to be solvable in polynomial time by, e.g., the Bellman-Ford algorithm for the shortest path problem [1, 5]. Also, the set of integer vectors in a DC polyhedron coincides with a so called L♮-convex set in discrete convex analysis and can be characterized by closedness under certain operations (see, e.g., [14] and Theorem 4.1 in Section 4). For more examples, see Related Work below. In this paper, we push the investigation of such a characterization further.
We focus on the set of integer vectors in a polyhedron, and study the relation between these sets and closedness under operations from the viewpoint of 2-decomposability. 2-decomposability is a concept proposed for the constraint satisfaction problem (CSP) studied in artificial intelligence and computer science, which is the computational problem of determining whether it is possible to assign values to variables while satisfying all given constraints. Intuitively, we say that the set of integer vectors is 2-decomposable if, whenever each two-dimensional projection of a vector is in the corresponding projection of , it is in . By definition, a 2-decomposable set of integer vectors can be written by constraints on two variables (although the converse does not hold in general). Therefore, problems with 2-decomposability have attracted attention as an easy-to-handle class in the CSP.
2-decomposability has attracted attention for other reasons as well. In particular, it has become clear that 2-decomposability is associated with closedness under operations, an important tool in analyzing the computational complexity of the CSP. Intuitively, a set is closed under an operation when any vector obtained by applying the operation to multiple vectors in the set is also in the set. Jeavons, Cohen, and Cooper showed that it is equivalent for all sets generated by applying certain relational operations from set to be 2-decomposable and for to be closed under a majority operation [8] (see also Theorem 2.8 in Section 2). Here, a majority operation is a ternary operation that, whenever two arguments have the same value, then that value must be returned by the operation; in all other cases any value may be returned.
While the set of integer vectors in a polyhedron with 2-decomposability does not correspond exactly to the set of integer vectors closed under a majority operation, we show that by adding further conditions important classes of sets of integer vectors in polyhedra are characterized by 2-decomposability and closedness under certain operations, and in some classes, by closedness under certain operations alone. The most prominent result we show is that the set of integer vectors in a unit-two-variable-per-inequality (UTVPI) polyhedron can be characterized by closedness under the median and directed discrete midpoint operations, each of these operations was independently considered in the CSP and discrete convex analysis. Here, a polyhedron is called UTVPI if each inequality is of the form or for some integer . We have also clarified the relationship with the classes treated in discrete convex analysis such as integrally convex sets.
We further characterize 2-decomposability itself. As noted above, in the known result in order to capture 2-decomposability by closedness under operations, it is necessary to assume 2-decomposability with respect to the infinite set generated by . We observe that, in fact, closedness under operations cannot directly characterize 2-decomposability. We then show that 2-decomposability can be characterized by introducing partial operations and suitable closedness under such operations.
Related work
In the CSP, the relationship between representation of the solution set and its closedness under operations is well studied, especially when the domain is finite. In the finite domain Boolean case, the problem is a generalization of Boolean satisfiability problem (SAT), and the following results are known. Let be a positive integer.
-
•
A subset of is the solution set of a CNF where each clause has at least one negated (resp., unnegated) variable if and only if it is closed under the zero (resp., one) operation.
- •
-
•
A subset of is the solution set of a 2-CNF if and only if it is closed under the majority operation [17].
-
•
A subset of is the solution set of a system of linear equations over GF(2) if and only if it is closed under the affine operation [17].
Here, a conjunctive normal form (CNF) is conjunction of one or more clauses, a clause is a disjunction of literals, and a literal is an unnegated or negated variable. A solution of a CNF is a truth assignment to the variables such that every clause has at least one true literal. As is often done in the area of SAT, we identify true value with one and false value with zero. The zero (resp., one) operation is a unary operation that always returns zero (resp., one). A CNF is called Horn (resp., dual-Horn) if each clause of it has at most one unnegated (resp., negated) variable. A CNF is called a 2-CNF if each clause of it has at most two variables. The affine operation is define as .
In the finite domain non-Boolean case, the following results are known. Let be a finite set.
-
•
A subset of is 0/1/all if and only if it is closed under the dual discriminator operation [7].
- •
-
•
When is a totally ordered set, a subset of is the solution set of a conjunctions of the form if and only if it is closed under the minimum operation [9].
-
•
When for some prime , a subset of is the solution set of a system of linear equations over GF() if and only if it is closed under the affine operation [7].
Here, a subset of is called 0/1/all if, for each , equals either , or , and symmetrically, for each , equals either , or , where is a projection on the th coordinate. The dual discriminator operation is a ternary operation such that if and otherwise. The median operation will be defined in Section 2. The affine operation is define as .
In the infinite domain case, not much research has been done. It is worth noting that in the infinite case, no discussion about representation is possible without specifying how the constraints are given. The following results are known.
-
•
A semilinear set of is the solution set of semilinear Horn formula if and only if it is closed under the maximum operation [3].
-
•
A semilinear set of is the solution set of semilinear tropically convex formula if and only if it is closed under the maximum operation and operation for all [3].
-
•
A semilinear set of is the solution set of a conjunction of bends if and only if it is closed under the median operation [2].
-
•
A subset of is the solution set of a DC system if and only if it is closed under and [14].
-
•
A subset of is the solution set of a system of linear inequalities of the form if and only if it is closed under the maximum, minimum, +1, and -1 operations [14].
Here, a subset of is semilinear if it is a finite union of finite intersections of (open or closed) linear half spaces. A semilinear Horn formula is a finite conjunction of semilinear Horn clauses, this is, finite disjunctions of the form where and there exists a such that for all and , , and . A semilinear tropically convex formula is a finite conjunction of basic semilinear tropically convex formula, this is, finite disjunctions of the form where and there exists a such that for all and , , , and for all . For each , the operation takes as input and outputs . A bend is a formula of the form where , , , and are such that if and only if for .
In summary, previous research has mainly been concerned with the representation of sets closed under a majority, the minimum (maximum), and the affine operations.
Outline
The rest of the paper is organized as follows. Section 2 introduces the various definitions used in the paper. In Section 3, we analyze the relationship between 2-decomposability and closure properties. In Section 4, we investigate the relationship between the sets of integer vectors in 2-decomposable polyhedra and closedness under operations. In Section 5, we characterize 2-decomposability by a certain closedness under partial operations. Finally, we conclude the paper in Section 6.
2 Preliminaries
Let and denote the sets of integers and reals, respectively. We assume that is an integer greater than one throughout the paper. We also assume that is a positive integer, unless otherwise specified, throughout the paper.
We will analyze the relationship between polyhedral representations of a set of integer vectors and closedness under operations in terms of 2-decomposability.
First, we define 2-decomposability of a set . For and a vector , the projection is defined to be the -dimensional vector . Moreover, for a set , let . For (), the join is defined as .
Definition 2.1 (2-decomposability [8]).
A set is said to be -decomposable if it contains all vectors such that for all , or equivalently, .
For any (-ary) operation and any , where , define to be . Namely, is obtained by a componentwise application of to . We will characterize a subset in terms of closedness under operations.
Definition 2.2 (Closedness under operation).
Let be a subset of and be an operation. We say is closed under , or -closed for short, if for all ,
We particularly focus on the following operations. The following one is introduced in [19].
Definition 2.3.
The directed discrete midpoint operation is defined as follows. For each ,
Namely, if the average of and is an integer, then outputs the integer, and otherwise, it outputs one of the two integers obtained by rounding that is “close” to .
Definition 2.4.
A ternary operation is called a majority operation, if for all ,
Definition 2.5.
The median operation is defined as follows. Let . Choose such that and . Then is defined by
Namely, outputs the median value of its three arguments.
By definition, the median operation is a majority operation.
Definition 2.6.
For a set and an operation , we define the -closure of as an intersection of all sets that include and are -closed.
We remark that itself includes and is closed under . Hence, is the (inclusion-wise) smallest set which includes and is closed under . Moreover, is indeed a closure operator, namely, it is extensive (i.e., ), monotone (i.e., implies ), and idempotent (i.e., ).
Definition 2.7.
For a set , we define a convex closure of as an intersection of all convex sets that include . For a set , we define a closed convex closure of as an intersection of all (topologically) closed convex sets that include .
It is well known that and are closure operators.
The following theorem shown in [8] connects 2-decomposability with closedness under operations111Theorem 2.8 is originally shown for a set for a finite set in [8]. However, in Subsection 4.4 in the same paper, it is mentioned that the result also holds for an infinite set .. Several operations over the subsets of in the theorem are not defined here, since we only use the direction that (1) implies (2) in this paper.
Theorem 2.8.
For a set , the following conditions are equivalent:
-
(1)
is closed under a majority operation.
-
(2)
Every in is 2-decomposable. In particular, is 2-decomposable.
Here, is the family of all subsets that can be obtained from using some sequence of (i) Cartesian product, (ii) equality selection, and (iii) projection operation.
Now, we provide some definitions for polyhedra.
Definition 2.9 (Polyhedron).
A set is called a polyhedron if there exist and such that holds.
It is known that any polyhedron is a closed convex set.
Definition 2.10.
Let be a matrix.
-
•
is said to be quadratic if each row of A contains at most two nonzero elements.
-
•
is said to be linear if each row of A contains at most one nonzero element.
-
•
is said to be unit if each element of is either , or .
-
•
is said to be difference if each row of A contains at most one and at most one .
Definition 2.11.
Let be a subset of .
-
•
is called a TVPI (two-variable-per-inequality) polyhedron if there exist quadratic matrix and vector such that holds.
-
•
is called a UTVPI (unit-two-variable-per-inequality) polyhedron if there exist unit quadratic matrix and vector such that holds.
-
•
is called a DC polyhedron if there exist difference matrix and vector such that holds.
-
•
is called a SVPI (single-variable-per-inequality) polyhedron if there exist linear matrix and vector such that holds.
Note that a SVPI polyhedron is a DC polyhedron, a DC polyhedron is a UTVPI polyhedron, and a UTVPI polyhedron is a TVPI polyhedron.
Definition 2.12 (Representability).
A set is said to be representable by a TVPI (resp, UTVPI, DC, SVPI) system if for some TVPI (resp, UTVPI, DC, SVPI) polyhedron. In this case, we say is a TVPI (resp, UTVPI, DC, SVPI) representation of .
Definition 2.13.
For a set , we define a UTVPI closure as an intersection of all subsets of that include and are representable by UTVPI systems.
We remark that itself includes and is representable by a UTVPI system. Hence, is the (inclusion-wise) smallest set which includes and is representable by a UTVPI system. Indeed, is a closure operator.
Proposition 2.14 ([2, Theorem 3.2]).
Let be a subset of . If is representable by a TVPI system, then is closed under the median operation.
Remark 2.15.
Proposition 2.14 can be easily generalized to the case where is representable by an infinite set of two-variable inequalities, i.e., for an infinite set , where for each .
We now introduce notions from discrete convex analysis.
Definition 2.16 (Integer neighborhood).
For vector , its integer neighborhood is defined as .
Definition 2.17 (Midpoint-neighbor-closedness).
A set is said to be midpoint-neighbor-closed if for each , the integer neighborhood of is contained in , i.e. .
Definition 2.18 (Integral convexity).
A set is said to be integrally convex if the convex closure of coincides with the union of the convex closures of over , i.e., if, for any , implies .
Theorem 2.19 (Theorem 2.1 in [15]).
A set is integrally convex if and only if
(1) |
for every with .
Proposition 2.20 (Proposition 2.1 in [13]).
A set is an integrally convex set if and only if is representable by a UTVPI system.
3 2-decomposability and closure properties
In this section, we investigate the relationships between 2-decomposability and closure properties, which will be used in Section 4.
Lemma 3.1.
Let be a subset of and be an operation.
-
(i)
If is -closed, then for each , is -closed.
-
(ii)
If for each , is -closed, then is -closed.
It follows that, if is -closed, then is also -closed.
Proof.
-
For any , there exists such that for . Since is -closed, . Thus, .
-
Take any . Then, for any and , . Since each is -closed, . Since for each and , we have .
∎
Lemma 3.2.
Let be an operation. Then the operator that takes as input and outputs is a closure operator.
Proof.
To show this, we check the following three conditions.
-
Firstly, we show . For any and for any , it is clear that . Thus, .
-
Secondly, we show that if then . Since and are monotonically increasing in terms of set inclusion, the composition is also monotonically increasing.
-
Finally, we show . By (ii), . Thus, we show the inverse inclusion, that is
It suffices to show that for each
By minimality of , it suffices to show that
Now, .
∎
Theorem 3.3 (2-decomposability and general closure property).
Let be a subset of . Let be a property for subsets of which has closure operator (for each ). Consider the following properties.
-
(i)
is closed under some majority operation and closed in terms of , i.e., .
-
(ii)
is closed under some majority operation and for each , is closed in terms of , i.e., .
-
(iii)
is 2-decomposable, i.e., , and for each , is closed in terms of , i.e., .
-
(iv)
.
Then we have a chain of implications: (ii) (iii) (iv). Moreover, (i) is incomparable to any other properties in general.
Proof.
(ii) (iii) follows by Theorem 2.8. For (iii) (iv), since for each , we have . ∎
Theorem 3.4 (2-decomposability and closure property induced by operations).
Let be a subset of and be an operation. Consider the following properties.
-
(i)
is closed under some majority operation and .
-
(ii)
is closed under some majority operation, and for each , is -closed.
-
(iii)
, and for each , is -closed.
-
(iv)
.
Then we have a chain of implications: (i) (ii) (iii) (iv).
Proof.
(i) (ii) directly follows by Lemma 3.1 (i). For (ii) (i), since is closed under some majority operation, it is 2-decomposable, i.e., . Then is -closed from Lemma 3.1 (ii). (ii) (iii) and (iii) (iv) follow from Theorem 3.3.
Theorem 3.5 (2-decomposability and closure property induced by special operations).
Let be a subset of and be an operation. Assume that there exists a majority operation such that for any if is -closed then it is -closed. Then the following are equivalent.
-
(i)
is closed under some majority operation and .
-
(ii)
is closed under some majority operation, and for each , is -closed.
-
(iii)
, and for each , is -closed.
-
(iv)
.
Proof.
By Theorem 3.4, it suffices to show that (iv) (i). Assume . For each , is closed under majority operation by assumption. Hence, is -closed and -closed by Lemma 3.1, implying that (i) holds. ∎
Remark 3.6.
Contrast to Theorem 3.5, in Theorem 3.4 (iii) does not imply (ii) for general operation . For example, when is a projection, (iii) means is 2-decomposable and (ii) means is closed under some majority operation, which are not equivalent (see Section 5).
Theorem 3.7 (2-decomposability and closure property induced by ).
Let be a subset of . Consider the following properties.
-
(i)
is closed under some majority operation and closed in terms of , i.e., .
-
(ii)
is closed under some majority operation, and for each , is closed in terms of , i.e., .
-
(iii)
, and for each , is closed in terms of , i.e., .
-
(iv)
.
Then we have a chain of implications: (ii) (iii) (iv) (i).
Proof.
By Theorem 3.3, it suffices to show that (iii) (ii) and (iv) (i).
For (iii) (ii), assume that , and for each , . Then, each is represented as a TVPI system with possibly infinitely many inequalities, and thus it is closed under the median operation (see Remark 2.15). It follows from Lemma 3.1 (ii) that is closed the median operation. Hence (ii) holds.
We then show that (iv) (i). (iv) means that is represented as a TVPI system with possibly infinitely many inequalities. Hence, is closed under the median operation (see Remark 2.15). Moreover, since is the set of integer vectors in an intersection of closed convex sets, we have . ∎
Proposition 3.8 (Hierarchy of closedness in 2-dimension).
For a subset , consider the following properties.
-
(i)
is midpoint-neighbor-closed.
-
(ii)
is closed under and
-
(iii)
is -closed.
-
(iii)’
is integrally convex.
-
(iv)
.
-
(v)
is closed under the median operation.
-
(vi)
is closed under some majority operation.
Then we have a chain of implications: (i) (ii) (iii) (iii)’ (iv) (v) (vi).
Proof.
(i) (ii) is clear. (ii) (iii) is shown in shown in [19, Corollary 2]. (iii) (iii)’ is also shown in [19] (see also Lemma 4.7).
For (iii)’ (iii), from Proposition 2.20, if is integrally convex, then it can be represented by a UTVPI system. Then, is -closed from Proposition 4.10 in Section 4.
For (iii) (iv), since is -closed, it can be represented by a UTVPI system by Lemma 4.13. Since a polyhedron is convex and topologically closed, (iv) holds. (iv) (v) follows from Remark 2.15. (v) (vi) is clear since the median operation is a majority operation. ∎
4 Polyhedral representation and closedness under operations
We here analyze the relationship between the set of integer vectors in 2-decomposable polyhedra and closedness under operations using the results in Section 3.
4.1 Main results
Using Theorems 3.5, 3.7 and 3.8, we obtain the following. The proof of (iii) is based on the results in Subsection 4.2, which constitutes the most technical part in this paper.
Theorem 4.1.
Let be a subset of .
-
(i)
is representable by a SVPI system if and only if is midpoint-neighbor-closed.
-
(ii)
is representable by a DC system if and only if is closed under and , and from Theorem 3.5, these are equivalent to, say, .
-
(iii)
is representable by a UTVPI system if and only if is closed under some majority operation and , and from Theorem 3.5, these are equivalent to, say, .
-
(iv)
is representable by a TVPI system with possibly infinitely many inequalities if and only if .
-
(v)
is closed under the median operation if and only if .
-
(vi)
is closed under some majority operation if and only if for some majority operation .
Proof.
(i): We first show the only-if part. Assume that is representable by a SVPI system. Take any . Since is representable by a SVPI system, interval is contained in , i.e., . Since for each , holds, is midpoint-neighbor-closed.
We then show the if part. Assume that is midpoint-neighbor-closed. Then (and each ) is also closed under and . Thus, by (ii) shown below, (and each ) is representable by a DC system. It follows that is 2-decomposable, i.e., , by Propositions 2.14 and 2.8. Then it suffices to show that each is representable by a SVPI system. Fix a DC representation of and denote it by . Let be a difference inequality in . We show that intersects at less than two points, which implies that removing from and appropriately adding and to it result in a representation of with less difference inequalities than . Assume otherwise that intersects at two (distinct) points, say, and . We can assume that . Since is midpoint-neighbor-closed, each is in . Then . Thus violates inequality . However, is in , a contradiction. Similarly, we can remove from . Hence, is representable by a SVPI system.
(ii) is a well-known result in discrete convex analysis; see, e.g., Equation (5.20) in [14].
(iii): We first show the only-if part. If is representable by a UTVPI system, by Theorem 4.9 (or Proposition 4.10), is closed under and . Moreover, the operation is a majority operation.
We then show the if part. Assume that is closed under some majority operation and . Then is 2-decomposable, i.e., by Theorem 2.8. Since each is closed under by Lemma 3.1, each can be represented by a UTVPI system from Lemma 4.13. Hence, is representable by a UTVPI system.
(iv): We first show the only-if part. Assume that is representable by a TVPI system with possibly infinitely many inequalities. This implies that for some and a (possibly infinite) set of indices. For each , let . Then, for , if and only if for . This implies that . Now, . Hence, holds.
We then show the if part. Note that is equivalent to the following condition:
-
•
for , if and only if for .
Each can be represented as for some and a (possibly infinite) set of indices, since is the intersection of all the closed half-spaces containing (see [16, Corollary 11.5.1]). Hence, for , if and only if . This implies that . Hence, is representable by a TVPI system with possibly infinitely many inequalities.
(v) follows from Theorems 3.5 and 3.8.
(vi) follows from Theorem 3.5. ∎
Combining Theorem 4.1 and the following proposition, we can characterize some closure operators.
Proposition 4.2.
Let be a property for a subset of which has closure operator , and be a property for a subset of which has closure operator . Then the following are equivalent.
-
(i)
For any ,
-
(ii)
For any , .
Proof.
We first show (i) (ii). By (a generalization of) Lemma 3.2, and are both closure operators. Then, (i) means that the families of the closed set defined by these closure operators are the same. Thus, these closure operators are the same and (ii) holds. We then show (ii) (i). Assume that (ii) is true. Then, . ∎
From Theorems 4.1 and 4.2, we see, for example, that is the same as (see also Theorem 4.9 below).
We mention the following.
Proposition 4.3.
Let be a subset of . is representable by a UTVPI system if and only if it is integrally convex and 2-decomposable.
Proof.
We first show the only-if part. From Theorem 4.1, is closed under some majority operation and . Hence, is 2-decomposable and integrally convex by Theorems 2.8 and 4.7, respectively.
We then show the if part. Since is 2-decomposable, . Moreover, it is known that the projection of an integrally convex set is integrally convex [12, Theorem 3.1]. Hence, each is integrally convex. Then each can be represented by a UTVPI system (see Proposition 2.20). Hence, is representable by a UTVPI system. ∎
Remark 4.4.
When is a subset of , the following are equivalent: (i) is representable by a UTVPI system, (ii) is -closed, and (iii) is integrally convex. The equivalence of (i) and (iii) follows from Proposition 2.20, and that of (ii) and (iii) follows from Proposition 3.8.
When is a subset of , the following are equivalent: (i) is representable by a UTVPI system, (ii) is representable by a TVPI system, (iii) is -closed, and (iv) is closed under some majority operation. To see this, it suffices to show that (iv) implies (i). Assume that is closed under some majority operation. Notice that there exists the unique majority operation over , and is representable by a 2-CNF from [17]. Since 2-CNF can be formulated as a UTVPI system, (i) holds.
We also remark that any subset of is -closed and integrally convex. To see this, it suffices to show that any subset of is -closed. Take any . One can verify that for any , . Therefore, is -closed.
Remark 4.5.
We remark that property is not preserved by projection. Namely, an analog of Lemma 3.1 does not hold. Consider the following example.
Let be a set defined as
Then, in the figure below, is the black points and is the black line segment.
Thus, we have . However, the projection does not satisfy this condition. In fact, since the projection , the convex closure . Thus, . Therefore, we have .
Remark 4.6.
We also remark that, if a set is representable by a TVPI system, then it is -closed by Proposition 2.14. However, even if is -closed and representable by a linear-inequality system (i.e., is the integer vectors in a polyhedron), it is not representable by a TVPI system in general as Example 4.15 in Subsection 4.3 shows.
4.2 Remaining proofs
The following lemma is shown in [19] for a more general function variant, but a proof is given here for readability.
Lemma 4.7.
Let be a subset of . If is -closed, then it is integrally convex.
Proof.
From Theorem 2.19, it suffices to show that for any . Since is closed under , we have . It is clear that . Now, since , we have . Hence, is integrally convex. ∎
We have proved that any closed under is integrally convex. The converse, however, does not hold in general as the following example shows.
Example 4.8.
Let . The points in lies on the plane in three-dimension. One can verify that
and is integrally convex. On the other hand, as , S is not closed under . Hence, integral convexity does not imply -closedness in general.
We now show the key result to show Theorem 4.1 (iii).
Theorem 4.9.
A set is representable by a UTVPI system if and only if it is closed under and .
Theorem 4.9 is divided into Propositions 4.10 and 4.14.
Proposition 4.10.
Let be a subset of . If is representable by a UTVPI system, then it is closed under and .
Proof.
Since a UTVPI polyhedron is a TVPI polyhedron, it follows that is closed under by Proposition 2.14. Thus we show that is closed under in what follows.
Assume that is represented by a UTVPI system for some matrix and vector , i.e., . Without loss of generality, we assume that is an integer vector. Let . We show that by showing that holds. For each , let be the th row of . We have to show that for each . Since is a UTVPI system, each is one of the forms of , , , , , or for some with . We use the fact that and (since and are in ) in what follows.
We only show the cases where and . The other cases can be shown similarly and thus are omitted.
Case 1: .
We show that . Note that from and we have , implying that .
Case 1.1: and are even.
In this case, we have and . Thus, we have
Case 1.2: is even and is odd.
In this case, implies that since the left-hand side is odd. Thus, we have . Since and , we have
Case 1.3: is odd and is even.
We can show in a similar way as in Case 1.2.
Case 1.4: and are odd.
We further divide into the cases of , , or ( and ).
If , then since is odd. Moreover, . Hence, we have
If , then since is odd. Moreover, . Hence, we have
Finally, consider the case of and . We have , and thus . Since is an integer we have . Similarly, we have Therefore, we have
Case 2: .
We show that . Note that from and we have , implying that . Then the proof goes similarly to that of Case 1.
Now, we have shown that satisfies each inequality (). Hence, holds. Therefore, is closed under . This completes the proof. ∎
As a corollary of the Proposition 4.10, we have the following result.
Corollary 4.11.
For a set , the directed discrete midpoint closure of is included in the UTVPI closure of .
Proof.
Since is representable by a UTVPI system, by Proposition 4.10, is closed under . Now, is the (inclusion-wise) smallest set that is closed under and includes . Therefore, . ∎
We note that there exists a TVPI polyhedron where its integer vectors are not -closed.
Example 4.12.
Consider the following TVPI system.
(4) |
The set of integer vectors satisfying the system is
Since , is not -closed.
We now show the opposite direction of Proposition 4.10. The following is a key lemma to show our result.
Lemma 4.13.
Let be a subset of . If is -closed, then is representable by a UTVPI system.
Proof.
See Remark 4.4. ∎
Proposition 4.14.
If a set is closed under and , then it is representable by a UTVPI system.
Proof.
First, since is -closed, it is 2-decomposable, i.e., . Since each is two-dimensional and -closed by Lemma 3.1, it is representable by a UTVPI system by Lemma 4.13. Then, clearly, ). Hence, is representable by a UTVPI system. ∎
Proof of Theorem 4.9.
It follows from Propositions 4.10 and 4.14. ∎
4.3 TVPI representability cannot be characterized by median-closedness
One may expect that if the set of integer vectors in a polyhedron is -closed, then it is representable by a TVPI system (i.e., the opposite of Proposition 2.14). However, this is not the case, as the following example shows.
Example 4.15.
Let
Consider a set defined as
Then is the black points in the figure below.
We now show that is -closed, whereas it is not representable by a TVPI system.
First we see that is -closed. Indeed, it is trivial when at least two of three arguments of are the same. Otherwise, we have
Thus, is -closed.
We next show that is not representable by a TVPI system. Assume otherwise that we have a TVPI representation . Then the following equality holds.
(5) |
Indeed, since , the left-hand side of is included in the right-hand side. Conversely, letting be a vector in the right-hand side of , we show that . It suffices to show that any two-variable inequality of is satisfied by , i.e., . Since, , we have . Moreover, since , we have . Hence, we have .
Now, , , and are the following grayed out subsets where the black circles are the projection of the points in , and the squares are integer points in the grayed out subset that are not in the projection of the points in .
We see that is included in the right-hand side of . But which means is not representable by a TVPI system.
5 Characterizing 2-decomposability
We now turn our attention to characterizing 2-decomposability.
Definition 5.1 (Partial operation).
An operation , where , is called a (-ary) partial operation (over ). is called the domain of and denoted by . If , then we say is defined, and otherwise (i.e., if ) undefined.
Since a partial operation is not defined for some inputs, we can define closedness under such operation in several ways.
Definition 5.2.
Let be a subset of . Let , where , be a partial operation.
-
(i)
We say is strongly closed under (or, strongly -closed) if for any , there exists that satisfies for each such that is defined.
-
(ii)
We say is weakly closed under (or, weakly -closed) if for any such that is defined for all , we have .
Definition 5.3.
Let be a subset of . Let be an operation associated with the th coordinate for each . We say is closed under (or, -closed) if for any
Definition 5.4.
The partial majority operation is defined as follows. Firstly, its domain . Then, for all ,
We first summarize the relationships between 2-decomposability and closedness under (partial) operations.
Proposition 5.5.
For a subset , consider the following properties.
-
(i)
is closed under some majority operation.
-
(ii)
We can associate a majority operation for each coordinate of and is -closed.
-
(iii)
is strongly closed under the partial majority operation.
-
(iv)
(i.e., is 2-decomposable).
-
(v)
is weakly closed under the partial majority operation.
For each property, we also consider the hereditary version of it, denoted by , which requires that every projection of satisfies the property. Then we have a chain of implications: (i) (ii) (iii) (iv) (v). We also have the following equivalences: (i) , (ii) , and (iii) .
Proof.
For (i) (ii), if is closed under majority operation , we can take for all . For (ii) (iii), consider arbitrary . We know that . Moreover, for each such that we have since is a majority operation. Hence, is strongly closed under .
For (iii) (iv), it is clear that . Thus, we show the inverse inclusion. This is trivial when . Assume that in the following. Take . Now, for , we show for any subset of cardinality there exists such that for all . We show this by induction on . First, consider the case of . Without of loss generality, we consider . Since , there exist such that , , and . Therefore, for each , is defined and equal to . Since is strongly closed under , there exists such that for all . This shows the case of . For , Without of loss generality, we consider . By induction hypothesis for , and , we can take such that for all where . It follows that for all we have . Since is strongly closed under , there exists such that for all . This shows the case of general . Therefore, .
We then show (iv) (v). We assume . For any such that for all , let . By the definition of , for each , is equal to at least one of and . Thus, . Since , we have . Hence, is weakly closed under .
For the relationships with the hereditary versions, it is clear that implies . Hence, it suffices to show that (i) (i, (ii) (ii, (iii) (iii, and (v (iii.
For (i) (i, we can show that closedness under operations is preserved by projection in a similar way as Lemma 3.1.
For (ii) (ii, let and take . Then for each there exists such that . By assumption, . Therefore, .
For (iii) (iii, let and take . Then for each there exists such that . By assumption, there exists that satisfies for all such that is defined. Then satisfies that for all such that is defined.
Finally, for (v (iii, it suffices to show that (v (iii) (since we have shown (iii) (iii). Take . Let . By assumption, is weakly -closed. Therefore, there exists such that for all . Then there exists such that for all . This satisfies that for all such that is defined. ∎
We note that none of the properties in Proposition 5.5 other than (iv) can characterize (iv), i.e., 2-decomposability. We observe in the following example that it is indeed not possible to characterize 2-decomposability by closedness under operations by showing that 2-decomposability is not preserved by projection. Note that closedness under operations is preserved by projection, which can be shown in a similar way as Lemma 3.1 (i).
Example 5.6.
Let . Then is 2-decomposable. To see this, take . First, assume that . As and , we must have . Hence, we have . We can similarly show in the cases of and . Hence, is 2-decomposable. On the other hand, , which equals , is not 2-decomposable, since . Hence, 2-decomposability is not preserved by projection in general.
While 2-decomposability cannot be characterized by closedness under operations, we show that it can be indeed characterized by weak closedness under infinitely many partial operations. Intuitively, we prepare all the partial operations that correspond to the condition of 2-decomposability. Concretely, assume that is 2-decomposable. Then contains all vectors such that for all . The latter condition can be rewritten as , , , , , , where can be any (distinct) integer. Then the condition is guaranteed by weak closedness under the partial operation such that , , , and for all , where can be any (distinct) integer, and is undefined for the other inputs. In this way, we prepare partial operations that correspond to each dimension to characterize 2-decomposability by weak closedness under operations.
Now, we formally state the above intuition. For each integer , we will define a partial operation over with . We first define . Let be the set of two-element subsets of , that is
Thus, we have . We identify with below. Then, we define a family as where each is defined as
By definition, we have , and for different , we have .
Then, for each and , we define as
Now, taking union of all of the above set for , we obtain , that is
Note that if , we have . Therefore, for , we define where is the unique element such that for some .
Then we define a set of partial operations as
Now we are ready to show our result that characterizes 2-decomposability.
Theorem 5.7.
Let be a subset of . is 2-decomposable if and only if is weakly -closed for each .
Proof.
For the if part, take any . It suffices to show that . For each , there exists such that . Now, applying to vectors , we obtain . Since is weakly -closed, we have . Therefore, is 2-decomposable.
We then show the only-if part. Assume that is 2-decomposable. We show that is weakly -closed for each . Fix and take any (multi-)set of elements of on which is defined. For , define as
Then we have for each . Moreover, by definition, we have . Hence, it suffices that . For each , arbitrarily take and such that . Then we have . On the other hand, when , we have and , implying that . Moreover, when , by taking arbitrary , we have and , implying that . Therefore, for each , and since is 2-decomposable, we have . This completes the proof. ∎
Remark 5.8.
Theorem 5.7 is true even if is replaced with any set, since we do not use the property of in the proof.
6 Conclusion and future work
We have analyzed the relationship between polyhedral representations of a set of integer vectors and closedness under operations in terms of 2-decomposability. We especially show that the set of integer vectors is representable by a UTVPI system if and only if it is closed under the median operation and the directed discrete midpoint operation. We also characterize 2-decomposability by weak closedness under partial operations.
Investigating other relationship between polyhedral representations and closedness under operations seems an interesting future direction. One candidate would be the relationship between Horn polyhedra (i.e., each inequality has at most one positive coefficient in the defining system) and the minimum operation. It is known that the set of integer vectors in a Horn polyhedron is closed under the minimum operation (see, e.g., [10]), but it is not known whether the converse is true.
Acknowledgments
The authors thank Akihisa Tamura for helpful comments and suggestions to improve presentation of the paper. The first author was partially supported by JST, ACT-X Grant Number JPMJAX200C, Japan, and JSPS KAKENHI Grant Number JP21K17700. The third author was supported by JST CREST Grant Number JPMJCR22M1. The fourth author was supported by WISE program (MEXT) at Kyushu University.
References
- [1] Richard Ernest Bellman. On a routing problem. Quarterly Applied Mathematics, 16:87–90, 1958.
- [2] Manuel Bodirsky and Marcello Mamino. A polynomial-time algorithm for median-closed semilinear constraints. arXiv: 1808.10068v2, 2018.
- [3] Manuel Bodirsky and Marcello Mamino. Tropically convex constraint satisfaction. Theory of Computing Systems, 62:481–509, 2018.
- [4] David Cohen, Peter Jeavons, Peter Jonsson, and Manolis Koubarakis. Building tractable disjunctive constraints. Journal of the ACM, 47:826–853, 2000.
- [5] Lester Randolph Ford. Network flow theory. Technical report, The Rand Corporation, Santa Monica, 1956.
- [6] Alfred Horn. On sentences which are true of direct unions of algebras. The Journal of Symbolic Logic, 16:14–21, 1951.
- [7] Peter Jeavons, David Cohen, and Marc Gyssens. A unifying framework for tractable constraints. In Proceedings of the First International Conference on Principles and Practice of Constraint Programming, pages 276–291.
- [8] Peter G. Jeavons, David A. Cohen, and Martin C. Cooper. Constraints, consistency and closure. Artificial Intelligence, 101:251–265, 1998.
- [9] Peter G. Jeavons and Martin C. Cooper. Tractable constraints on ordered domains. Artificial Intelligence, 79:327–339, 1995.
- [10] H. van Maaren and C. Dang. Simplicial pivoting algorithms for a tractable class of integer programs. Journal of Combinatorial Optimization, 6(2):133–142, 2002.
- [11] J. C. C. McKinsey. The journal of symbolic logic. Discrete Applied Mathematics, 8:61–76, 1943.
- [12] Satoko Moriguchi and Kazuo Murota. Projection and convolution operations for integrally convex functions. Discrete Applied Mathematics, 255:283–298, 2019.
- [13] Satoko Moriguchi, Kazuo Murota, Akihisa Tamura, and Fabio Tardella. Scaling, proximity, and optimization of integrally convex functions. Mathematical Programming, 175:119–154, 2019.
- [14] Kazuo Murota. Discrete Convex Analysis. Society for Industrial and Applied Mathematics, 2003.
- [15] Kazuo Murota and Akihisa Tamura. Recent progress on integrally convex functions. Japan Journal of Industrial and Applied Mathematics, 40:1445–1499, 2023.
- [16] R. Tyrrell Rockafellar. Convex Analysis. Princeton University Press, New Jersey, USA, 1970.
- [17] Thomas J. Schaefer. The complexity of satisfiability problems. In Proceedings of the 10th annual ACM Symposium on Theory of Computing, pages 216–226, 1978.
- [18] Alexander Schrijver. Theory of Linear and Integer Programming. John Wiley & Sons Ltd, Chichester, 1986.
- [19] Akihisa Tamura and Kazuya Tsurumi. Directed discrete midpoint convexity. Japan Journal of Industrial and Applied Mathematics, 38:1–37, 2021.
- [20] Robert J. Vanderbei. Linear Programming: Foundations and Extensions. Springer, fifth edition, 2020.
Appendix
A More Direct Proof of Lemma 4.13 in Subsection 4.2
Our proof of Lemma 4.13 in Subsection 4.2 heavily relies on previous work, but we provide a more direct proof here for readability of the paper.
Lemma 4.13.
Let be a subset of . If is -closed, then is representable by a UTVPI system.
Basic observations
Lemma 6.1.
Let be a subset of . If is representable by a UTVPI system, then so are the following sets:
-
(i)
the reflection of over the -axis, i.e., ,
-
(ii)
the reflection of over the -axis, i.e., ,
-
(iii)
the reflection of over the line , i.e., ,
-
(iv)
the reflection of over the line , i.e., , and
-
(v)
the translation of by an integer vector , i.e., .
Proof.
Since is represented by a UTVPI system, there exist a unit quadratic matrix and a vector such that . We use the fact that for a surjection , if there exist a matrix and a vector such that if and only if for , then the image is equal to . In fact, for any there exists an element such that , and since if and only if , we have . Conversely, for any , since is surjective there exists an such that . Now, since if and only if , this is in which means . Using this fact, we show the lemma as follows.
-
(i)
Let be a unit quadratic matrix whose the first column is the same as that of and the second column is times that of . Then clearly if and only if , and thus, is a UTVPI representation of the reflection of over the -axis.
-
(ii)
Let be a unit quadratic matrix whose the first column is times that of and the second column is the same as that of . Then clearly if and only if , and thus, is a UTVPI representation of the reflection of over the -axis.
-
(iii)
Let be a unit quadratic matrix obtained by permuting the first and second column of . Then clearly if and only if , and thus, is a UTVPI representation of the reflection of over the line .
-
(iv)
Since the reflection of over the line can be obtained by a sequence of the reflection over the line , the reflection over the -axis, and the reflection over the -axis, the reflection of over the line is representable by a UTVPI system by (i)-(iii).
-
(v)
Since if and only if , is a UTVPI representation of the translation of by .
∎
Lemma 6.2.
Let be a subset of . If is -closed, then so are the following sets:
-
(i)
the reflection of over the -axis, i.e., ,
-
(ii)
the reflection of over the -axis, i.e., ,
-
(iii)
the reflection of over the line , i.e., ,
-
(iv)
the reflection of over the line , i.e., , and
-
(v)
the translation of by an integer vector , i.e., .
Proof.
We use the easy-to-prove fact that if a map satisfies for , then the image is also -closed. In fact, for all there exist such that . Since in -closed, , and thus, .
-
(i)
Since for , for . Thus, we have . Hence the reflection of over the -axis is also -closed.
-
(ii)
This can be similarly proven as (i).
-
(iii)
This is followed by .
-
(iv)
The reflection of over the is a composition of the reflection of over the -axis, the -axis, and the line . Thus, it follows from (i)-(iii).
-
(v)
It follows from .
∎
Another proof of Lemma 4.13
Given the basic observations in Basic observations, we are ready to prove Lemma 4.13 in a more self-contained manner.
Proof of Lemma 4.13.
Assume that is -closed. By Proposition 2.20, it suffices to show that is integrally convex. Let . We show in the following. By Carathéodory’s theorem there exist three elements such that .
Now we will show the -closure of is equal to the UTVPI hull of it. First we show a similar equality for two-point case, i.e., . By Corollary 4.11, is included in . If a line through is parallel to -axis, -axis, the line , or the line , it is clear that consists of all integer points between and on and is also the same set, and thus, . If not, from Lemma 6.1 and Lemma 6.2, by reflecting and translating and appropriately, it suffices to show the case of and where . In this case, consists integer points that satisfy
By , all points in also satisfy these inequalities.
First we will show . If we show some point is in where , we retake and by induction on , we have . And if we show there exists that satisfies , then, since , by induction on , we have . Thus, we will show that such is in . When are both even, and by repeating halving the point, we have a such that at least one of or is odd number. First we consider the case when is odd. Then and
which means we obtain desirable . Otherwise; is even and is odd, and . Here
and we again obtain desirable . Hence is in . Now, is also in , since it is obtained by translating by and reflect it over -axis and -axis. Thus, all vertices of is in . Now, since the boundary of is parallel to -axis, -axis, the line , or the line , all points on the boundaries are in . Finally, any inner point lies on the line segment that connects and which are on the boundaries, implying that is in . Therefore, we have .
Next, we show . By Corollary 4.11, , and by the same argument of the two points case, it is enough to show any vertex of is in . When coincides with one of , is trivially in . Thus, we assume equals none of . If necessary, by retaking the indexes of , is the intersection of boundary through and that through . Let be the angle between the two boundaries. Then, is one of or , but we show that must be . In fact, assume . By Lemma 6.1 and Lemma 6.2, we may assume , lies on the positive part of -axis, and lies on the line and is in the first quadrant. Then, satisfy , and thus, is not in , a contradiction. Moreover, let . Again we may assume (i) , and and lie on the positive parts of -axis and -axis, respectively or (ii) , lies on the line and is in the first quadrant, and lies on the line and is in the forth quadrant. In case (i) satisfy , in case (ii) satisfy , and thus, in both cases is not in , a contradiction. Hence . We assume , , and where are positive integers. Then by the case of two points, . Therefore, all vertices of are in . Therefore, we have .
Now . By and Proposition 2.20, is integrally convex and thus . Since is -closed, and thus . Therefore, is integrally convex. ∎