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On the system of two all_different predicates

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Abstract

The polytope defined by the convex hull of integer vectors satisfying the system of the two all_different predicates was examined. The dimension of this polytope was established and subsequently two classes of facet-defining inequalities were also exhibited. A separation algorithm of low complexity, which provided only the facet-defining inequalities violated by a given vector was introduced. The obtained results could be directly applied to the optimization problem involving the maximization of a linear function over the system of two all_different predicates.

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... As remarked in section 2, checking feasibility in such a setting is trivial. In [1], a somewhat different aspect of this setting is considered. Assume that the set D is labelled by the set of integers 0, 1, . . . ...
... Now what is the structure of the polytope defined by the convex hull of integer vectors corresponding to feasible solutions? The authors of [1] establish the dimension of this polytope, and also obtain classes of facet-defining inequalities. We consider the variant where dimensions / variables are associated with each edge of the graph, rather than each vertex in X. Viewed as a purely graphtheoretic decision/optimisation problem, this makes eminent sense as it directly generalises the well-studied matching polytope (see for instance [11]): we wish to assign 0,1 values to each edge variable (a value of 1 for an edge corresponds to putting this edge into the solution M , 0 corresponds to omitting this edge) such that all vertices of X (or as many as possible) have an incident edge in M , and M is a feasible solution. ...
... The corresponding linear program replaces the last condition above by ∀e : x e ∈ [0, 1]. Let P I denote the convex hull of integer solutions to the integer program, and let P L denote the convex hull of feasible solutions to the linear program. ...
... We let P X be the convex hull of feasible solutions in the finite-domain model (1) and P Y be the convex hull of feasible solutions in the 0-1 model (2). The finite-domain variables x i are readily expressed in terms of the 0-1 variables y ij : ...
... , n}. The facial structure of a system of two all-different constraints is studied in [1,2]. Facets for general all-different systems are derived for combs in [9,10,12] and for odd holes and webs in [11]. ...
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We explore the idea of obtaining valid inequalities for a 0–1 model from a finite-domain constraint programming formulation of the problem. In particular, we formulate a graph coloring problem as a system of all-different constraints. By analyzing the polyhedral structure of all-different systems, we obtain facet-defining inequalities that can be mapped to valid cuts in the classical 0–1 model of the problem. We focus on cuts corresponding to cycles and webs and show that they are stronger than known cuts for these structures. We also identify path cuts and show they do not strengthen the bound. Computational experiments for a set of benchmark instances reveal that finite-domain cycle cuts often deliver tighter bounds, in less time, than classical 0–1 cuts.
... Related research on binary constraint satisfaction problems appears in [2,21]. Furthermore, the linear representation of various systems of alldifferent constraints has been studied [1,3,15,16]. ...
... Rather, they address a wide spectrum of models composed of structures having the submodular/supermodular representation property. Examples of such models include the intersection of two alldifferent constraints [1], two polymatroids associated with matroids on distinct ground sets, etc. ...
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Submodularity defines a general framework rich of important theoretical properties while accommodating numerous applications. Although the notion has been present in the literature of Combinatorial Optimization for several decades, it has been overlooked in the analysis of global constraints. The current work illustrates the potential of submodularity as a powerful tool for such an analysis. In particular, we show that the cumulative constraint, when all tasks are identical, has the submodular/supermodular representation property, i.e., it can be represented by a submodular/supermodular system of linear inequalities. Motivated by that representation, we show that the system of any two (global) constraints not necessarily of the same type, each bearing the above-mentioned property, has an integral relaxation given by the conjunction of the linear inequalities representing each individual constraint. This result is obtained through the use of the celebrated polymatroid intersection theorem.
... As remarked in Section 2, checking feasibility in such a setting is trivial. In [1], a somewhat different aspect of this setting is considered. Assume that the set D is labelled by the set of integers 0, 1,. ...
... Now what is the structure of the polytope defined by the convex hull of integer vectors corresponding to feasible solutions? The authors of [1] establish the dimension of this polytope and also obtain classes of facetdefining inequalities. ...
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... The special case in which G is a clique is the familiar AllDifferent constraint. Another special case that has received some attention is the case of two AllDifferent constraints sharing some of their variables [1]. ...
... If a valid coloring exists, we say the graph is colorable. 1 Valid colorings correspond to assignments respecting the SomeDifferent constraint. We view a coloring of the graph as a collection of individual point colorings, where by point coloring we mean a coloring of a single vertex by one of the colors. ...
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We introduce the SomeDifferent constraint as a generalization of AllDifferent. SomeDifferent requires that values assigned to some pairs of variables will be different. It has many practical applications. For example, in workforce management, it may enforce the requirement that the same worker is not assigned to two jobs which are overlapping in time. Propagation of the constraint for hyper-arc consistency is NP hard. We present a propagation algorithm with worst case time complexity O(n 3β n ) where n is the number of variables and β≈3.5 (ignoring a trivial dependence on the representation of the domains). We also elaborate on several heuristics which greatly reduce the algorithm’s running time in practice. We provide experimental results, obtained on a real-world workforce management problem and on synthetic data, which demonstrate the feasibility of our approach.
... Based on the approach presented in [5], the objective of this paper is to give a complete characterization of the polytope of two at-least predicates interacting. Similar work has been done in [10] for all-different predicate where a complete characterization is first given and then this has seen much progress in [1] where the polytope of two all-different predicates considered simultaneously is demonstrated. Although, in [8] are given some facets of multiple all-different predicates we are still missing a complete characterization. ...
... at-least m {x 1 , x 2 , . . . , x n } = k (1) means that at least m of the n variables, with integral domain [0, l], will take on value k. ...
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Constraint programming is a powerful tool for modeling various problems in operations research. Its strength lies in the use of predicates, or global high-level constraints, on a few variables to efficiently model complex and varied problem structures. In this paper, we consider the interactions of two instance of the predicate at-least. Each predicate ensures that a subset of a set of integer variables takes on a specified value. The convex hull of a single such predicate is known but the hull of intersecting predicates is still an open question. We have completely determined the convex hull representation of two intersecting predicates and provide a polynomial separation algorithm for inclusion in branch-and-bound integer programming software. We also characterize a number of facets of the intersection of two at-least predicates.
... In this paper we are interested in extending the seminal work of [15] where the complete characterization of the all-different predicate was first demonstrated. This was then followed by [1] where two all-different predicates were considered and the polytope was characterized. Also followed by [2], [3] where a graph coloring model was used to find valid inequalities. ...
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Constraint programming is a powerful tool for modeling various problems in operations research. Its strength lies in the use of predicates, or global high-level constraints, on a few variables to efficiently model complex and varied problem structures. In this paper, we consider the predicate at-least. It bounds the number of variables in a set that may receive a specific value. This is a generalization of the standard logic condition expressed when the sum of binary variables is expressing a lower bound on the cardinality of a set. We have completely determined the convex hull representation of this predicate and provide a polynomial separation algorithm for inclusion in branch-and-bound integer programming software.
... The last example presented describes such a model for the all different predicate [4]. Further examples are the symmetric all different predicate [5] and the system of two all different predicates [6]. Thus, the definition of a maximum cardinality matching constraint provides a framework encompassing other predicates. ...
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... The case of a single all different constraint is easy to solve, as it represents bipartite matching. The case of two simultaneously applied all different constraints was studied by Appa, Magos, and Mourtos (2005). The general case of multiple simultaneously applied all different constraints is, in some sense, equivalent to graph colouring. ...
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... In many cases, it is practical to reformulate a problem with 0-1 variables for purposes of relaxation, but ideally one would generate cuts in the original finitedomain variables. Finite-domain cuts have been derived for a few constraints, such as alldiff systems (graph coloring) [2,3,10,11,37,38], the circuit constraint (which can be used to formulate the traveling salesman problem) [19], and disjunctive scheduling [29]. Yet much remains to be done. ...
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... All facets for a single all-different constraint are given in [5, 14] . The facial structure of a system of two all-different constraints is studied in [1, 2]. Facets for general all-different systems are derived for comb structures in [7, 8, 10] and for odd holes and webs in [9]. ...
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... Besides encoding more complex global cardinality constraints, conjunctions of among constraints, (CAC), appear in many problems, such as Sudoku or latin squares. In consequence, CACs have been previously studied [10,32,38], specially the particular case of conjunctions of AllDiff constraints [2,3,9,13,22,23,25]. Although deciding the satisfiability of an arbitrary conjunction of among constraints is NP-complete [32] this body of work shows that sometimes there are benefits in reasoning about the interaction between the among constraints. ...
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