# Part 6: AllDifferent Constraint¶

We ask you not to publish your solutions on a public repository. The instructors interested to get the source code of our solutions can contact us.

## Forward-Checking Filtering¶

Implement a dedicated propagator AllDifferentFWC.java for the global AllDifferent constraint. Unlike AllDifferentBinary.java, it must not decompose the AllDifferent constraint by posting binary disequality constraints but instead do the following: when a variable becomes fixed, its value is removed from the domains of all the other variables. This achieves the same filtering as AllDifferentBinary.java, namely what is called forward-checking consistency. Avoid iteration over already fixed variables when removing a value: implement the sparse-set technique, as in Sum.java. Test your implementation in AllDifferentFWCTest.java. Modify NQueens.java by using AllDifferentFWC.java and experiment with the 15-queens instance: how much speed-up do you observe for finding all the solutions?

## Domain-Consistent Filtering¶

The objective is to implement the filtering algorithm described in [Regin94] to remove every impossible value for the AllDifferent constraint (this is called generalized arc consistency and is also known as domain consistency). More precisely, you must:

Régin’s algorithm is a four-step procedure that can be described with the following figure: The four steps are:

1. Computing an initial maximum matching in the variable-value graph for the consistency test (matched edges and value nodes are colored blue in the figure).
2. Building a directed graph: each matched edge becomes a (directed) arc from the variable node to the value node, and each unmatched edge becomes a (directed) arc from the value node to the variable node. Additionally, a dummy node is added that has an incoming arc from each unmatched value node, and an outgoing arc to each matched value node.
3. Computing the strongly connected components (SCCs). Note that for this step, the number of each node in the figure corresponds to their SCC rather than their index or value for variable and value nodes respectively.
4. Any arc that was not a matched edge and that connects two nodes from different components is removed. Note that for this step the number of each node in the figure once again corresponds to their index or value for variable and value nodes respectively.

The two main algorithmic building blocks are provided:

• MaximumMatching.java is a class that computes a maximum matching given an array of variables. Instantiate this class once in the constructor of AllDifferentDC and then call compute in the propagate method.
• GraphUtil.java contains a static method with signature public static int[] stronglyConnectedComponents(Graph graph) to compute the strongly connected components. The returned array gives for each node its connected component id.

One of the main difficulties of this exercise is to implement the Graph interface to represent the residual graph of the maximum matching:

```public static interface Graph {
/* the number of nodes in this graph */
int n();

/* incoming nodes ids incident to node idx */
Iterable<Integer> in(int idx);

/* outgoing nodes ids incident to node idx */
Iterable<Integer> out(int idx);
}
```

It uses an adjacency list that is updated in the method updateGraph(). We advise you to use a dense representation with node ids as illustrated on the black nodes of the example (step2: directed graph).

Once your code passes the tests, you can experiment your new constraint on all the models you have seen so far in order to measure the pruning gain on the number of nodes (n-Queens, TSP, QAP, etc).

 [Regin94] Régin, J.-C. (1994). A filtering algorithm for constraints of difference in CSPs. 12th National Conference on Artificial Intelligence (AAAI-94). (PDF)