Submitted:
31 July 2024
Posted:
01 August 2024
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Abstract
The primary objective of this paper is to establish connections between two well-known but previously independently developed theories: the theory of violator spaces and the theory of greedoids. Violator spaces were introduced by Matoušek et al. in 2008 as a generalization of linear programming problems. Greedoids were introduced by Korte and Lovász in 1981 in an effort to characterize combinatorial structures where greedy algorithms yield optimal solutions. In this work, we explore the relationships between violator spaces and greedoids, demonstrating that greedoids can be defined using a variant of a violator operator. These interrelations provide a new characterization of antimatroids.
Keywords:
violator space
; linear programming
; closure operator
; greedoid
; antimatroid
1. Preliminaries
The primary objective of the paper is to establish connections between two combinatorial concepts: violator spaces and greedoids. Violator spaces emerged as a generalization of Linear Programming (LP) problems. LP-type problems were introduced and studied by Matoušek, Sharir, and Welzl [11,13] as a combinatorial framework encompassing linear programming and other computational geometric problems. Further, Matoušek et al. [1] introduced violator spaces as a simpler framework, which generalizes LP-type problems. Every LP-type problem can naturally be converted into a violator space, but the reverse is not necessarily true. Greedoids were introduced as an attempt to characterize combinatorial structures where the greedy algorithm yields optimal solutions. This work explores the relationships between violator spaces and greedoids, demonstrating that greedoids can be defined using a variant of a violator operator. The interrelations between violator spaces and greedoids provide a new characterization of antimatroids.
2. Violator Spaces
Definition 1.
[1] An abstract LP-type problem is a quadruple , where E is a finite set, W is a set linearly ordered by ≤, and w is a mapping such that for all subsets the following properties are satisfied:
V1: (monotonicity);
V2: for all and with and , we have (locality).
Definition 2.
[13] Consider an abstract LP-type problem . We say that is a basis if for all proper subsets we have . For , a basis of X is an inclusion-minimal subset B of X with .
Solving an abstract LP-type problem means identifying a basis of E.
The set of all constraints violating , denoted as , is defined as:
This set includes all elements a in the ground set E that are not in X and, when added to X, cause the objective function w to increase.
One can see that the condition generally does not suffice for . For example [1], if for all , then any X of the same size have the same , however, , and so no distinct sets X share the value of . Thus, we can see that w by itself does not capture the combinatorial structure of the problem. To describe the "structure" of an LP-type problem Matoušek et al. [1] introduced the concept of a violator space.
Definition 3.
[1] A violator space is a pair , where E is a finite set and ν is a violator operator, i.e., a mapping such that for all subsets the following properties are satisfied:
V11: (consistency);
V22: and (locality).
Let be a violator space. Let . So, if is defined by (1) then
Theorem 1.
[3] Let E be a finite set and two mappings such that for all . Then is a violator space if and only if φ satisfies
C1: (extensivity);
C2: (self-convexity).
Thus, we have two equivalent approaches to define violator spaces:
- the classic one - , where the ground set E is equipped with the mapping satisfying consistency and locality;
- the dual one - , where the ground set E is equipped with the mapping satisfying extensivity and self-convexity.
In what follows, a pair , where E is a finite set and is a mapping satisfied extensivity and self-convexity, will be considered a violator space as well.
Since a violator operator is extensive it may be interpreted as a some type of a "closure" operator. The concept of closure appears in many disciplines, including topology, algebra, logic, geometry, convexity analysis, graph theory etc. It is also known by other names, such as span, hull, and envelope. For instance, the convex hull operator on the Euclidean space is a classic example of a closure operator.
Definition 4.
Let E be a finite set. is a closure operator on E if for all subsets the following properties are satisfied:
CL1: (extensivity);
CL2: (isotonicity);
CL3: (idempotence).
It was proved that every closure operator is a violator operator, since isotonicity and idempotence imply self-convexity, and extensivity with self-convexity imply idempotence [3]. However, not each violator operator is a closure operator, since it does not have to satisfy isotonicity.
Let us consider some additional properties of violator operators.
Lemma 1.
Let be a violator space. Then for all .
Proof.
"If": Let . Since extensivity implies , we conclude with .
"Only if": If , then . Hence, by self-convexity, . □
Proposition 1.
Let E be a finite set. For each extensive operator self-convexity is equivalent to the following property:
C22: for all .
Proof.
1. C2 ⇒C22: From Lemma 1. Hence, .
2. C22 ⇒C2: (from extensivity). Then C22 implies . □
Since the second property addresses all sets that lie between two given sets, we refer to this property as convexity, in accordance with Monjardet [8].
2.1. Uniquely generated violator spaces
Let be an arbitrary space with the operator . is a generator of if . A basis of X is a inclusion-minimal set (not necessarily included in X) with . A space is uniquely generated if every set has a unique basis.
Proposition 2.
[3] A violator space is uniquely generated if and only if for every
It is known that a closure operator is uniquely generated if and only if it satisfies the anti-exchange property [7,10,12]:
We extended this characterization to violator spaces in the following.
Theorem 2.
[3] Let be a violator space. Then is uniquely generated if and only if the operator φ satisfies the anti-exchange property.
An element x of a subset is an extreme point of X if . The set of extreme points of X is denoted by .
Proposition 3.
[3] For violator spaces: if and only if .
Theorem 3.
[3] Let be a violator space. Then is uniquely generated if and only if for every set , .
Corollary 1.
[3] Let be a uniquely generated violator space. Then for every the set is the unique basis of X.
3. Greedoids
Let we have a greedoid , i.e.,
(i);
(ii) such that (augmentation property).
Elements of are called feasible sets.
The rank function of a greedoid is defined as follows:
Theorem 4.
[7] A function is the rank function of a greedoid if and only if for all and all the following conditions hold:
(1.1)
(1.2) (subcardinality)
(1.3) implies (monotonicity)
(1.4) implies (local submodularity).
Moreover, the greedoid is uniquely determined by its rank function:
So, the pair may be considered as a greedoid with defined by (6).
3.1. LP-tYpe Problems and Greedoids
The rank function r of a greedoid satisfies the all properties of the mapping w of LP-type problem. It is monotone (Theorem 4) and satisfies the locality.
Proposition 4.
Let be a greedoid with rank function r. Then and implies for all and .
Proof.
If and there is a maximal feasible set , with . In addition, there is a feasible set with . Since A is a maximal feasible set in Y, , and . Then (from the augmentation property) there exists for which . Since A is a maximal feasible set in Y, , and so , i.e., is a feasible set, and then . □
Thus a greedoid may be considered as an abstract LP-type problem .
Note, that the family is the family of all bases of the LP-type problem, since for each proper subset A of a feasible set
However, if the bases of a LP-type problem form a greedoid, a function w does not have to be a greedoid’s rank function. For example, every monotone function w with different values on each element of turns all subsets of E to be bases. While the bases constitute a greedoid, a function w does not have to satisfied subcardinality.
Let us build a specific LP-type problem where E is a finite set, v is a mapping such that for all subsets the following properties are satisfied:
(1);
(2) (subcardinality);
(3) implies (monotonicity);
(4) For all and with and ,we have (locality).
The property (4) may be reformulated as follows:
(5) For all and with and , we have .
Proposition 5.
Let v be a monotone mapping on . Then locality (5) is equivalent to local submodularity (1.4).
Proof.
1. Let v satisfy locality and . Denote . Then , and , then (from (5)) we have .
2. Let v satisfy local submodularity, , , and . By repeatedly applying local submodularity we prove that . It is easy to see that the proposition is correct for . Let . From monotonicity it follows that . Hence, . Local submodularity implies . Denote .
Prove by induction on n that for each . From monotonicity it follows that for each . Then implies . Thus . □
In fact, the property of local submodularity may be extended as follows.
Corollary 2.
If such that , then .
Theorem 4 implies that a LP-type problem with mapping and determines a greedoid by .
3.2. Violator and Rank Closure Operators on Greedoids
Define (rank) closure operator of greedoids ([7]): . The operator is extensive () and idempotent (), but it does not have to be an isotone operator. So is not always a closure operator. At the same time, the definition of coincides with the definition of a violator operator (2). The following theorem supports the interpretation of greedoids as a subclass of violator spaces since the properties (i) and (ii) define a violator operator.
Theorem 5.
[2] A mapping is the closure operator of some greedoid if and only if
(i)
(ii)
(iii) if for all , and then .
Moreover, if σ satisfies (i), (ii), and (iii), then
.
Based on the definition of extreme points we have
Then the following condition for all is equivalent to , and so the property (iii) may be rewritten as follows:
Define - the family of bases w.r.t. operator .
Proposition 6.
for each violator operator σ.
Proof.
If , i.e., for all , then for all holds , and so ( from Proposition 3) . Then . If then there exists such that . Hence for each self-convexity implies , i.e., . This concludes the proof, with . □
It is worth mentioning that Property (iii) is not necessary for to be a greedoid.
Example 1.
Let . Define for each except . It is easy to check that the space is a uniquely generated violator space (satisfies both extensivity and convexity), where the family of bases forms a greedoid. At the same time, operator φ does not satisfies (7) which is equivalent to (iii). Indeed, if , then , , but .
If we consider the rank function of the greedoid , we can see that , and so . Then for this function σ the property (iii) holds and we have a not uniquely generated violator space.
Thus the same family of bases may be obtained by different mappings.
3.3. Antimatroids - Uniquely Generated Greedoids
Definition 5.
An antimatroid is a greedoid closed under union.
Definition 6.
An accessible set system is a set system in which every nonempty feasible set contains an element x such that is feasible.
By definition, the family of feasible sets of a greedoid is an accessible set system.
Lemma 3.
[7] For an accessible set system the following statements are equivalent:
(A1) is an antimatroid
(A2) is closed under union
(A3) implies .
Proposition 7.
An antimatroid is a uniquely generated greedoid.
Proof.
Let be an antimatroid. Since each antimatroid is a greedoid, it remains to prove that the greedoid is uniquely generated. Suppose there are two bases and such that . Since is a family of bases, then . Hence , because is an antimatroid. But (see Lemma 2). Contradiction. □
Since for each greedoid is a violator operator, Theorem 2 implies the following.
Corollary 3.
The operator σ of each antimatroid satisfies the anti-exchange property.
Theorem 6.
The family is an antimatroid if and only if is a uniquely generated greedoid.
Proof.
It remains to prove that each uniquely generated greedoid is an antimatroid. Suppose, , but . Then , and . Contradiction to anti-exchange property. □
4. Conclusion
We have demonstrated that greedoids can be defined using a variant of the violator operator. The relationships between violator spaces and greedoids enabled us to provide a new characterization of antimatroids.
To gain further structural insights into antimatroids, one can explore cospanning relations. Given an operator , two sets X and Y are cospanning if . The equivalence relation known as cospanning has been studied for greedoids [7], and it has been shown that these relations uniquely determine greedoids. Similar equivalence relations have also been explored for violator spaces [3,4,9]. Investigating these structures could lead to new characterizations of antimatroids and other combinatorial structures.
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