1. Global Constraints Toby Walsh National ICT Australia and University of New South Wales www.cse.unsw.edu.au/~tw

2. Course outline ● Introduction ● All Different ● Lex ordering ● Value precedence ● Complexity ● GAC-Schema ● Soft Global Constraints ● Global Grammar Constraints ● Roots Constraint ● Range Constraint ● Slide Constraint ● Global Constraints on Sets

3. ROOTS and RANGE ● Large catalog of global constraints – but very little taxonomy? – global grammar constraints specify a “family” of useful global constraints ● ROOTS and RANGE let us define many counting and occurrence constraints

4. Counting and Occurrence Constraints ● Many (resource bounded) optimization & decision problems contain: – occurrence constraints (on values that occur)

5. Counting and Occurrence Constraints ● Many (resource bounded) optimization & decision problems contain: – occurrence constraints (on values that occur) – E.g. the value “Thur 3-5pm” should occur once in the timetable of any PhD student following the course on global constraints

6. Counting and Occurrence Constraints ● Many (resource bounded) optimization & decision problems contain: – counting constraints (on number of vals or vars satisfying a condition)

7. Counting and Occurrence Constraints ● Many (resource bounded) optimization & decision problems contain: – counting constraints (on number of vals or vars satisfying a condition) – E.g. the value “night shift” can be used by at most 3 variables in any 7!

8. Using ROOTS and RANGE ● Simple, declarative language for specifying many counting & occurrence constraints ● two new primitives: ● ROOTS and RANGE global constraints ● Executable language ● propagate primitives ● need just to provide propagators within the constraint solver for ROOTS and RANGE

9. Specification language ● Based around properties of functions – consider sequence of vars X1,..,Xn each taking a value in a D – then one view of X1,..,Xn is a function: X : [1,n] -> D – For example X1=a, X2=b, X3=a …

10. Specification language ● Based around properties of functions – consider sequence of vars X1,..,Xn each taking a value in a D – then one view of X1,..,Xn is a function: X : [1,n] -> D – For example X1=a, X2=b, X3=a … X(1)=a, X(2)=b, X(3)=a …

11. Specification language ● Based around properties of functions – consider sequence of vars X1,..,Xn each taking a value in a D – then one view of X1,..,Xn is a function: X : [1,n] -> D – This view is useful for specifying global constraints – NV ALUES ([X1,..,Xn],N) is equivalent to |R ANGE (X)|=N

12. R ANGE constraint ● Restricts range of function to a given subset – R ANGE ([X1,..,Xn],S,T) iff X(S)=T [1,n] ST

13. R ANGE constraint ● Restricts range of function to a given subset – R ANGE ([X1,..,Xn],S,T) iff X(S)=T e.g. RANGE([1,3,1,2,5],{2,4,5},{2,3,5}) [1,n] ST

14. R ANGE constraint ● Restricts range of function to a given subset – R ANGE ([X1,..,Xn],S,T) iff X(S)=T – useful to describe values used (when values are resources) – Some examples ● RANGE([X1,..Xn],{1,..n},T) and |T|=N is NValues ● RANGE([X1,..Xn],{1,..n},T) and |T|=n is AllDifferent ● …

15. R ANGE constraint ● RANGE constraint involves both finite- domain variables (X1,..,Xn) and set variables(S,T) – to describe propagation, need to define new type of local consistency – hybrid consistency is a local consistency property for global constraints involving both finite domain and set variables

16. Hybrid Consistency ● Constraint is “hybrid consistent (HC) – for each a in dom(X), there is a support – each a in ub(S) appears in one support – each a in lb(S) appears in all supports ● Reduces to GAC on finite domain vars,and BC on set vars

17. Hybrid Consistency ● Constraint is “hybrid consistent (HC) – for each a in dom(X), there is a support – each a in ub(S) appears in one support – each a in lb(S) appears in all supports ● Consider RANGE([X1,..X4],S,T) where – X1,X2 in {1,2,5}, X3 in {3,4} and X4 in {1} – {4} subseteq S subseteq {1,2,3,4} – {} subseteq T subseteq {1,2}

18. R ANGE constraint ● Restricts range of function to a given subset – R ANGE ([X1,..,Xn],S,T) iff X(S)=T ● Polynomial to make HC – O( nd+nt^3/2 ) where d=max(dom(Xi)) and t=|lb(T)|

19. R OOTS constraint ● Restricts domain to those mapping to a given subset – R OOTS ([X1,..,Xn],S,T) iff S=X -1 (T) [1,n] ST

20. R OOTS constraint ● Restricts domain to those mapping to a given subset – R OOTS ([X1,..,Xn],S,T) iff S=X -1 (T) – E.g. ROOTS([3,1,2,3,0],{1,4,5},{0,3}) [1,n] ST

21. R OOTS constraint ● Restricts domain to those mapping to a given subset – R OOTS ([X1,..,Xn],S,T) iff S=X -1 (T) – E.g. RANGE([3,1,2,3,0],{1,5},{0,3}) [1,n] ST

22. R OOTS constraint ● Restricts domain to those mapping to a given subset – R OOTS ([X1,..,Xn],S,T) iff S=X -1 (T) ● Useful to describe variables using particular values ● An example – R OOTS ([X1,..,Xn],S,{d1,..,dm}) & |S|=N is AMONG constraint

23. R OOTS constraint ● Restricts domain to those mapping to a given subset – R OOTS ([X1,..,Xn],S,T) iff S=X -1 (T) – NP-hard to make HC – but O( nd ) when Xi or T are ground (all cases here!)

24. R OOTS constraint ● R OOTS ([X1,..,Xn],S,T) iff S=X -1 (T) – Reduction from 3SAT in N vars and M clauses ● n=N+M ● Xi in {i,-i} for 1<=i<=N – Xi = i iff xi is false

25. R OOTS constraint ● R OOTS ([X1,..,Xn],S,T) iff S=X -1 (T) – Reduction from 3SAT in N vars and M clauses ● n=N+M ● Xi in {i,-i} for 1<=i<=N ● XN+j in {i,-k,l} iff jth clause is (xi or -xk or xl)

26. R OOTS constraint ● R OOTS ([X1,..,Xn],S,T) iff S=X -1 (T) – Reduction from 3SAT in N vars and M clauses ● n=N+M ● Xi in {i,-i} for 1<=i<=N ● XN+j in {i,-k,l} iff jth clause is (xi or -xk or xl) ● {} subseteq T subsetqe Union_i {i, -i}

27. R OOTS constraint ● R OOTS ([X1,..,Xn],S,T) iff S=X -1 (T) – Reduction from 3SAT in N vars and M clauses ● n=N+M ● Xi in {i,-i} for 1<=i<=N ● XN+j in {i,-k,l} iff jth clause is (xi or -xk or xl) ● {} subseteq T subseteq Union_i {i, -i} ● S={N+1, … N+M}

28. Specification language ● Equalities and inequalities – X≤ N, X=N,... ● Set constraints – S subseteq T, |S|=N,... ● Two global constraints – R OOTS and R ANGE

29. Some examples ● A LL D IFFERENT ([X1,..,Xn]) iff R ANGE ([X1,..,Xn],{1,..,n},T) & |T|=n – unfortunately enforcing HC on decomposition does not make A LL D IFFERENT constraint GAC ● E.g. X1, X2 in {1,2}, X3 in {1,2,3,4} ● {1,2} subseteq T subseteq {1,2,3,4} – so sometimes worth developing specialized propagators

30. Some examples ● P ERMUTATION ([X1,..,Xn]) iff R ANGE ([X1,..,Xn],{1,..,n},{1,..,n}) – special case of A LL D IFFERENT – clearly enforcing HC on “decomposition” makes constraint GAC – so sometimes this specification language is a good way to implement specific global constraints

31. Some examples ● NV ALUES ([X1,..,Xn,N]) – |{Xi | 1≤i≤n }|=N – R ANGE ([X1,..,Xn],{1,..,n},T) & |T|=N – enforcing HC on decomposition does not enforce GAC on NV ALUES – but this is to be expected as it is NP-hard to do so! – one way (at least) to implement this global constraint

32. Some examples ● A MONG ([X1,..,Xn],[d1,..,dm],N) – |{i | Xi=dj }|=N – R OOTS ([X1,..,Xn],S,{d1,..,dm}) & |S|=N – enforcing HC on decomposition enforces GAC on A MONG – again example of where specification language is a good way to implement a specific global constraint

33. Some examples ● C OMMON (N,M,[X1,..,Xn],[Y1,..,Ym]) – |{i | ∃ j. Xi=Yj }|=N and |{j | ∃ i. Xi=Yj }|=M R ANGE ([Y1,..,Ym],{1,..m},T) & R OOTS ([X1,..,Xn],S,T) & |S|=N & R ANGE ([X1,..,Xn],{1,..n},V) & R OOTS ([Y1,..,Yn],U,V) & |U|=M

34. Some examples ● C OMMON (N,M,[X1,..,Xn],[Y1,..,Ym]) – |{i | ∃ j. Xi=Yj }|=N and |{j | ∃ i. Xi=Yj }|=M Enforcing HC on decomposition does not enforce GAC on C OMMON – but to be expected as NP-hard to do so!

35. Some examples ● LINKSET2BOOLEANS(S,[B1,..Bn]) holds iff Bi=j iff j in S ● ROOTS([B1,..Bn],S,{1}) ● Decomposition clearly does not hinder propagation ● ROOTS is polynomial to enforce HC

36. Some examples ● NOTALLEQUAL([X1,..Xn]) ● Nasty large disjunction ● RANGE([X1,..Xn],{1,..n},T) & |T|>1

37. Other examples G CC S YM A LL D IFFERENT E LEMENT D OMAIN C ONTIGUITY ●... Whilst we can express many such global constraints, not all give effective propagators (e.g. GCC).

38. Global grammar constraints ● Filtering algorithms from finite sized automata/membership of a regular language – How do they compare? ● Such approaches are complementary – these automata can specify C ONTIGUITY – but not P ERMUTATION

39. Conclusions ● Counting and Occurrence constraints can be specified using simple declarative language – needs two new global constraints: ROOTS and RANGE ● Efficient and effective means to implement a number of such constraints: – when propagating specification achieves GAC – or constraint is NP-hard to propagate – however, we may still need to design a specialized propagator (e.g. gcc)