## 5.5. alldifferent

 DESCRIPTION LINKS GRAPH AUTOMATON
Origin
Constraint

$\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}\left(\mathrm{𝚅𝙰𝚁𝙸𝙰𝙱𝙻𝙴𝚂}\right)$

Synonyms

$\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏}$, $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚜𝚝𝚒𝚗𝚌𝚝}$, $\mathrm{𝚍𝚒𝚜𝚝𝚒𝚗𝚌𝚝}$, $\mathrm{𝚋𝚘𝚞𝚗𝚍}_\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$, $\mathrm{𝚋𝚘𝚞𝚗𝚍}_\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏}$, $\mathrm{𝚋𝚘𝚞𝚗𝚍}_\mathrm{𝚍𝚒𝚜𝚝𝚒𝚗𝚌𝚝}$, $\mathrm{𝚛𝚎𝚕}$.

Argument
 $\mathrm{𝚅𝙰𝚁𝙸𝙰𝙱𝙻𝙴𝚂}$ $\mathrm{𝚌𝚘𝚕𝚕𝚎𝚌𝚝𝚒𝚘𝚗}\left(\mathrm{𝚟𝚊𝚛}-\mathrm{𝚍𝚟𝚊𝚛}\right)$
Restriction
$\mathrm{𝚛𝚎𝚚𝚞𝚒𝚛𝚎𝚍}$$\left(\mathrm{𝚅𝙰𝚁𝙸𝙰𝙱𝙻𝙴𝚂},\mathrm{𝚟𝚊𝚛}\right)$
Purpose

Enforce all variables of the collection $\mathrm{𝚅𝙰𝚁𝙸𝙰𝙱𝙻𝙴𝚂}$ to take distinct values.

Example
$\left(〈5,1,9,3〉\right)$

The $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ constraint holds since all the values 5, 1, 9 and 3 are distinct.

Typical
$|\mathrm{𝚅𝙰𝚁𝙸𝙰𝙱𝙻𝙴𝚂}|>2$
Symmetries
• Items of $\mathrm{𝚅𝙰𝚁𝙸𝙰𝙱𝙻𝙴𝚂}$ are permutable.

• Two distinct values of $\mathrm{𝚅𝙰𝚁𝙸𝙰𝙱𝙻𝙴𝚂}.\mathrm{𝚟𝚊𝚛}$ can be swapped; a value of $\mathrm{𝚅𝙰𝚁𝙸𝙰𝙱𝙻𝙴𝚂}.\mathrm{𝚟𝚊𝚛}$ can be renamed to any unused value.

Usage

The $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ constraint occurs in most practical problems directly or indirectly. A classical example is the n-queen chess puzzle problem: Place $n$ queens on a $n$ by $n$ chessboard in such a way that no queen attacks another. Two queens attack each other if they are located on the same column, on the same row or on the same diagonal. This can be modelled as the conjunction of three $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ constraints. We associate to the ${i}^{th}$ column of the chessboard a domain variable ${X}_{i}$ that gives the row number where the corresponding queen is located. The three $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ constraints are:

• $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}\left({X}_{1},{X}_{2}+1,...,{X}_{n}+n-1\right)$ for the upper-left to lower-right diagonals,

• $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}\left({X}_{1},{X}_{2},...,{X}_{n}\right)$ for the rows,

• $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}\left({X}_{1}+n-1,{X}_{2}+n-2,...,{X}_{n}\right)$ for the lower right to upper-left diagonals.

They are respectively depicted by parts (A), (C) and (D) of Figure 5.5.1.

##### Figure 5.5.1. Upper-left to lower-right diagonals (A-B), rows (C) and lower-right to upper-left diagonals (D-E) A second example taken from [AsratianDenleyHaggkvist98] when the bipartite graph associated with the $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ constraint is convex is a ski assignment problem: “a set of skiers have each specified the smallest and largest skis they will accept from a given set of skis”. The task is to find a ski for each skier.

Examples such as Costas arrays or Golomb rulers involve one or several $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ constraints on differences of variables.

Quite often, the $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ constraint is also used in conjunction with several $\mathrm{𝚎𝚕𝚎𝚖𝚎𝚗𝚝}$ constraints, specially in the context of assignment problems [pages 372–374][Hooker07book].

Other examples involving several $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ constraints sharing some variables can be found in the Usage slot of the $𝚔_\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ constraint.

Remark

Even if the $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ constraint had not this form, it was specified in ALICE [Lauriere76], [Lauriere78] by asking for an injective correspondence between variables and values: $x\ne y⇒f\left(x\right)\ne f\left(y\right)$. From an algorithmic point of view, the algorithm for computing the cardinality of the maximum matching of a bipartite graph was not used for checking the feasibility of the $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ constraint, even if the algorithm was already known in 1976. This stands from the fact that the goal of ALICE was to show that a general system could be as efficient as dedicated algorithms. For this reason the concluding part of [Lauriere76] explicitly mentions the fact that specialized algorithms should be discarded. On the one hand, many people, specially from the OR community, have complained about such radical statement [Roy06]. On the other hand, the motivation of such statement stands from the fact that a truly intelligent system should not rely on black box algorithms, but should rather be able to reconstruct them from some kind of first principle. How to achieve this is still an open question.

Some solvers use in a pre -processing phase before stating all constraints, an algorithm for automatically extracting large cliques [BronKerbosch73] from a set of binary disequalities in order to replace them by $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ constraints.

W.-J. van Hoeve provides a survey about the $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ constraint in [Hoeve01].

For possible relaxation of the $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ constraints see the $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}_\mathrm{𝚎𝚡𝚌𝚎𝚙𝚝}_\mathtt{0}$, the $𝚔_\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ (i.e., $\mathrm{𝚜𝚘𝚖𝚎}_\mathrm{𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$

Within the context of linear programming, relaxations of the $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ constraint are described in [WilliamsYan01] and in [Hooker07book].

Within the context of constraint-centered search heuristics, G. Pesant and A. Zanarini [ZanariniPesant07b] have proposed several estimators for evaluating the number of solutions of an $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ constraint (since counting the total number of maximum matchings of the corresponding variable -value graph is #P -complete [Valiant79]). Faster, but less accurate estimators, based on upper bounds of the number of solutions were proposed three years later by the same authors [ZanariniPesant10].

Given $n$ variables taking their values within the interval $\left[1,n\right]$, the total number of solutions of the corresponding $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ constraint corresponds to the sequence A000142 of the On -Line Encyclopedia of Integer Sequences [Sloane10].

Algorithm

The first complete filtering algorithm was independently found by M.-C. Costa [Costa94] and J.-C. Régin [Regin94]. This algorithm is based on a corollary of C. Berge that characterises the edges of a graph that belong to a maximum matching but not to all [Berge70].A similar result is in fact given in [Petersen1891]. A short time after, assuming that all variables have no holes in their domain, M. Leconte came up with a filtering algorithm [Leconte96] based on edge finding. A first bound-consistency algorithm was proposed by Bleuzen-Guernalec et al. [GuernalecColmerauer97]. Later on, two different approaches were used to design bound-consistency algorithms. Both approaches model the constraint as a bipartite graph. The first identifies Hall intervals in this graph [Puget98], [LopezOrtizQuimperTrompBeek03] and the second applies the same algorithm that is used to compute arc-consistency, but achieves a speedup by exploiting the simpler structure [Glover67] of the graph [MehlhornThiel00]. Ian P. Gent et al. discuss in [GentMiguelNightingale08] implementations issues behind the complete filtering algorithm and in particular the computation of the strongly connected components of the residual graph (i.e., a graph constructed from a maximum variable -value matching and from the possible values of the variables of the $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ constraint), which appears to be the main bottleneck in practice.

From a worst case complexity point of view, assuming that $n$ is the number of variables and $m$ the sum of the domains sizes, we have the following complexity results:

• Complete filtering is achieved in $O\left(m\sqrt{n}\right)$ by Régin's algorithm [Regin94].

• Range consistency is done in $O\left({n}^{2}\right)$ by Leconte's algorithm [Leconte96].

• Bound-consistency is performed in $O\left(nlogn\right)$ in [Puget98], [MehlhornThiel00], [LopezOrtizQuimperTrompBeek03]. If sort can be achieved in linear time, typically when the $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ constraint encodes a permutation,In this context the total number of values that can be assigned to the variables of the $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ constraint is equal to the number of variables. Under this assumption sorting the variables on their minimum or maximum values can be achieved in linear time. the worst case complexity of the algorithms described in [MehlhornThiel00], [LopezOrtizQuimperTrompBeek03] goes down to $O\left(n\right)$.

Within the context of explanations [JussienBarichard00], the explanation of the filtering algorithm that achieves arc-consistency for the $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ constraint is described in [Rochart05]. Given the residual graph (i.e., a graph constructed from a maximum variable -value matching and from the possible values of the variables of the $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ constraint), the removal of an arc starting from a vertex belonging to a strongly connected component ${𝒞}_{1}$ to a distinct strongly connected component ${𝒞}_{2}$ is explained by all missing arcs starting from a descendant component of ${𝒞}_{2}$ and ending in an ancestor component of ${𝒞}_{1}$ (i.e., since the addition of any of these missing arcs would merge the strongly connected components ${𝒞}_{1}$ and ${𝒞}_{2}$). Let us illustrate this on a concrete example. For this purpose assume we have the following variables and the values that can potentially be assigned to each of them, $A\in \left\{1,2\right\}$, $B\in \left\{1,2\right\}$, $C\in \left\{2,3,4,6\right\}$, $D\in \left\{3,4\right\}$, $E\in \left\{5,6\right\}$, $F\in \left\{5,6\right\}$, $G\in \left\{6,7,8\right\}$, $H\in \left\{6,7,8\right\}$. Figure 5.5.2 represents the residual graph associated with the maximum matching corresponding to the assignment $A=1$, $B=2$, $C=3$, $D=4$, $E=5$, $F=6$, $G=7$, $H=8$. It has four strongly connected components containing respectively vertices $\left\{A,B,1,2\right\}$, $\left\{C,D,3,4\right\}$, $\left\{E,F,5,6\right\}$ and $\left\{G,H,7,8\right\}$. Arcs that are between strongly connected components correspond to values that can be removed:

• The removal of value 2 from variable $C$ is explained by the absence of the arcs corresponding to the assignments $A=3$, $A=4$, $B=3$ and $B=4$ (since adding any of these missing arcs would merge the blue and the pink strongly connected components containing the vertices corresponding to value 2 and variable $C$).

• The removal of value 6 from variable $C$ is explained by the absence of the arcs corresponding to the assignments $E=3$, $E=4$, $F=3$ and $F=4$. Again adding the corresponding arcs would merge the two strongly connected components containing the vertices corresponding to value 6 and variable $C$.

• The removal of value 6 from variable $G$ is explained by the absence of the arcs corresponding to the assignments $E=7$, $E=8$, $F=7$ and $F=8$.

• The removal of value 6 from variable $H$ is explained by the absence of the arcs corresponding to the assignments $E=7$, $E=8$, $F=7$ and $F=8$.

##### Figure 5.5.2. Strongly connected components of the residual graph illustrating the explanation of the removal of a value for the constraint $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$$\left(〈A,B,C,D,E,F,G,H〉\right)$, $A\in \left\{1,2\right\}$, $B\in \left\{1,2\right\}$, $C\in \left\{2,3,4,6\right\}$, $D\in \left\{3,4\right\}$, $E\in \left\{5,6\right\}$, $F\in \left\{5,6\right\}$, $G\in \left\{6,7,8\right\}$, $H\in \left\{6,7,8\right\}$: the explanation why value 2 is removed from variable $C$ corresponds to all missing arcs whose addition would merge the blue and the pink strongly connected components (i.e., the missing arcs corresponding to the assignments $A=3$, $A=4$, $B=3$ and $B=4$ that are depicted by thick pink lines) After applying bound-consistency the following property holds for all variables of an $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ constraint. Given a Hall interval $\left[l,u\right]$, any variable $V$ whose range $\left[\underline{V},\overline{V}\right]$ intersects $\left[l,u\right]$ without being included in $\left[l,u\right]$ has its minimum value $\underline{V}$ (respectively maximum value $\overline{V}$) that is located before (respectively after) the Hall interval (i.e., $\underline{V}).

The $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ constraint is entailed if and only if there is no value $v$ that can be assigned two distinct variables of the $\mathrm{𝚅𝙰𝚁𝙸𝙰𝙱𝙻𝙴𝚂}$ collection (i.e., the intersection of the two sets of potential values of any pair of variables is empty).

Reformulation

The $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ constraint can be reformulated into a set of disequalities constraints. This model neither preserves bound-consistency nor arc-consistency:

• On the one hand a model, involving linear constraints, preserving bound-consistency was introduced in [BessiereKatsirelosNarodytskaQuimperWalsh09IJCAI]. For each potential interval $\left[l,u\right]$ of consecutive values this model uses $|\mathrm{𝚅𝙰𝚁𝙸𝙰𝙱𝙻𝙴𝚂}|$ 0-1 variables ${B}_{1,l,u},{B}_{2,l,u},...,{B}_{|\mathrm{𝚅𝙰𝚁𝙸𝙰𝙱𝙻𝙴𝚂}|,l,u}$ for modelling the fact that each variable of the collection $\mathrm{𝚅𝙰𝚁𝙸𝙰𝙱𝙻𝙴𝚂}$ is assigned a value within interval $\left[l,u\right]$ (i.e., $\forall i\in \left[1,|\mathrm{𝚅𝙰𝚁𝙸𝙰𝙱𝙻𝙴𝚂}|\right]:{B}_{i,l,u}⇔\mathrm{𝚅𝙰𝚁𝙸𝙰𝙱𝙻𝙴𝚂}\left[i\right].\mathrm{𝚟𝚊𝚛}\in \left[l,u\right]$),How to encode the reified constraint ${B}_{i,l,u}⇔\mathrm{𝚅𝙰𝚁𝙸𝙰𝙱𝙻𝙴𝚂}\left[i\right].\mathrm{𝚟𝚊𝚛}\in \left[l,u\right]$ with linear constraints is described in the Reformulation slot of the $\mathrm{𝚒𝚗}_\mathrm{𝚒𝚗𝚝𝚎𝚛𝚟𝚊𝚕}_\mathrm{𝚛𝚎𝚒𝚏𝚒𝚎𝚍}$ constraint. and an inequality constraint for enforcing the condition that the sum of the corresponding 0-1 variables is less than or equal to the size $u-l+1$ of the corresponding interval (i.e. ${B}_{1,l,u}+{B}_{2,l,u}+...+{B}_{|\mathrm{𝚅𝙰𝚁𝙸𝙰𝙱𝙻𝙴𝚂}|,l,u}\le u-l+1$).

• On the other hand, it was shown in [BessiereKatsirelosNarodytskaWalsh09] that there is no polynomial sized decomposition that preserves arc-consistency.

Systems
Used in
See also

generalisation: $\mathrm{𝚊𝚕𝚕}_\mathrm{𝚖𝚒𝚗}_\mathrm{𝚍𝚒𝚜𝚝}$ ($\mathrm{𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎}$ replaced by $\mathrm{𝚕𝚒𝚗𝚎}\mathrm{𝚜𝚎𝚐𝚖𝚎𝚗𝚝}$, all of the same size), $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}_\mathrm{𝚋𝚎𝚝𝚠𝚎𝚎𝚗}_\mathrm{𝚜𝚎𝚝𝚜}$ ($\mathrm{𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎}$ replaced by $\mathrm{𝚜𝚎𝚝}\mathrm{𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎}$), $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}_\mathrm{𝚌𝚜𝚝}$ ($\mathrm{𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎}$ replaced by $\mathrm{𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎}+\mathrm{𝚌𝚘𝚗𝚜𝚝𝚊𝚗𝚝}$), $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}_\mathrm{𝚒𝚗𝚝𝚎𝚛𝚟𝚊𝚕}$ ($\mathrm{𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎}$ replaced by $\mathrm{𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎}/\mathrm{𝚌𝚘𝚗𝚜𝚝𝚊𝚗𝚝}$), $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}_\mathrm{𝚖𝚘𝚍𝚞𝚕𝚘}$ ($\mathrm{𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎}$ replaced by $\mathrm{𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎}\mathrm{mod}\mathrm{𝚌𝚘𝚗𝚜𝚝𝚊𝚗𝚝}$), $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}_\mathrm{𝚙𝚊𝚛𝚝𝚒𝚝𝚒𝚘𝚗}$ ($\mathrm{𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎}$ replaced by $\mathrm{𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎}\in \mathrm{𝚙𝚊𝚛𝚝𝚒𝚝𝚒𝚘𝚗}$), $\mathrm{𝚍𝚒𝚏𝚏𝚗}$ ($\mathrm{𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎}$ replaced by orthotope), $\mathrm{𝚍𝚒𝚜𝚓𝚞𝚗𝚌𝚝𝚒𝚟𝚎}$ ($\mathrm{𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎}$ replaced by $\mathrm{𝚝𝚊𝚜𝚔}$), $\mathrm{𝚐𝚕𝚘𝚋𝚊𝚕}_\mathrm{𝚌𝚊𝚛𝚍𝚒𝚗𝚊𝚕𝚒𝚝𝚢}$ (control the number of occurrence of each value with a counter variable), $\mathrm{𝚐𝚕𝚘𝚋𝚊𝚕}_\mathrm{𝚌𝚊𝚛𝚍𝚒𝚗𝚊𝚕𝚒𝚝𝚢}_\mathrm{𝚕𝚘𝚠}_\mathrm{𝚞𝚙}$ (control the number of occurrence of each value with an interval), $\mathrm{𝚕𝚎𝚡}_\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ ($\mathrm{𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎}$ replaced by $\mathrm{𝚟𝚎𝚌𝚝𝚘𝚛}$), $\mathrm{𝚗𝚟𝚊𝚕𝚞𝚎}$ (count number of distinct values).

Keywords
Arc input(s)

$\mathrm{𝚅𝙰𝚁𝙸𝙰𝙱𝙻𝙴𝚂}$

Arc generator
$\mathrm{𝐶𝐿𝐼𝑄𝑈𝐸}$$↦\mathrm{𝚌𝚘𝚕𝚕𝚎𝚌𝚝𝚒𝚘𝚗}\left(\mathrm{𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎𝚜}\mathtt{1},\mathrm{𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎𝚜}\mathtt{2}\right)$

Arc arity
Arc constraint(s)
$\mathrm{𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎𝚜}\mathtt{1}.\mathrm{𝚟𝚊𝚛}=\mathrm{𝚟𝚊𝚛𝚒𝚊𝚋𝚕𝚎𝚜}\mathtt{2}.\mathrm{𝚟𝚊𝚛}$
Graph property(ies)
$\mathrm{𝐌𝐀𝐗}_\mathrm{𝐍𝐒𝐂𝐂}$$\le 1$

Graph class
$\mathrm{𝙾𝙽𝙴}_\mathrm{𝚂𝚄𝙲𝙲}$

Graph model

We generate a clique with an equality constraint between each pair of vertices (including a vertex and itself) and state that the size of the largest strongly connected component should not exceed one.

Parts (A) and (B) of Figure 5.5.3 respectively show the initial and final graph associated with the Example slot. Since we use the $\mathrm{𝐌𝐀𝐗}_\mathrm{𝐍𝐒𝐂𝐂}$ graph property we show one of the largest strongly connected component of the final graph. The $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ holds since all the strongly connected components have at most one vertex: a value is used at most once.

##### Figure 5.5.3. Initial and final graph of the $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ constraint  (a) (b)
Automaton

Figure 5.5.4 depicts the automaton associated with the $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ constraint. To each item of the collection $\mathrm{𝚅𝙰𝚁𝙸𝙰𝙱𝙻𝙴𝚂}$ corresponds a signature variable ${𝚂}_{i}$ that is equal to 1. The automaton counts the number of occurrences of each value and finally imposes that each value is taken at most one time.

##### Figure 5.5.4. Automaton of the $\mathrm{𝚊𝚕𝚕𝚍𝚒𝚏𝚏𝚎𝚛𝚎𝚗𝚝}$ constraint 