constraint programming: modelling
DESCRIPTION
Constraint Programming: modelling. Toby Walsh NICTA and UNSW. Golomb rulers. Mark ticks on a ruler Distance between any two ticks (not just neighbouring ticks) is distinct Applications in radio-astronomy, cystallography, … http://www.csplib.org/prob/prob006. Golomb rulers. Simple solution - PowerPoint PPT PresentationTRANSCRIPT
Golomb rulers
• Mark ticks on a ruler Distance between any two ticks (not just
neighbouring ticks) is distinct
• Applications in radio-astronomy, cystallography, … http://www.csplib.org/prob/prob006
Golomb rulers
• Simple solution Exponentially long ruler Ticks at 0,1,3,7,15,31,63,…
• Goal is to find minimal length rulers turn optimization problem into sequence of satisfaction
problemsIs there a ruler of length m?Is there a ruler of length m-1?….
Optimal Golomb rulers
• Known for up to 23 ticks• Distributed internet project to find large rulers
0,1
0,1,3
0,1,4,6
0,1,4,9,11
0,1,4,10,12,17
0,1,4,10,18,23,25
Solutions grow as approximately O(n^2)
Modelling the Golomb ruler
• Variable, Xi for each tick
• Value is position on ruler
• Naïve model with quaternary constraints For all i>j,k>l>j |Xi-Xj| \= |Xk-Xl|
Problems with naïve model
• Large number of quaternary constraints O(n^4) constraints
• Looseness of quaternary constraints Many values satisfy |Xi-Xj| \= |Xk-Xl| Limited pruning
A better non-binary model
• Introduce auxiliary variables for inter-tick distances Dij = |Xi-Xj| O(n^2) ternary constraints
• Post single large non-binary constraint alldifferent([D11,D12,…]). Tighter constraints and denser constraint graph
Other modeling issues
• Symmetry A ruler can always be reversed! Break this symmetry by adding constraint:
D12 < Dn-1,n Also break symmetry on Xi
X1 < X2 < … Xn Such tricks important in many problems
Other modelling issues
• Additional (implied) constraints Don’t change set of solutions But may reduce search significantly
E.g. D12 < D13, D23 < D24, …
E.g. D1k at least sum of first k integers
• Pure declarative specifications are not enough!
Solving issues
• Labeling strategies often very important Smallest domain often good idea Focuses on “hardest” part of problem
• Best strategy for Golomb ruler is instantiate variables in strict order Heuristics like fail-first (smallest domain) not
effective on this problem!
Experimental results
Runtime/sec Naïve model Alldifferent model
8-Find 2.0 0.1
8-Prove 12.0 10.2
9-Find 31.7 1.6
9-Prove 168 9.7
10-Find 657 24.3
10-Prove > 10^5 68.3
Something to try at home?
• Circular (or modular) Golomb rulers Inter-tick distance
variables more central, removing rotational symmetry?
• 2-d Golomb rulers
All examples of “graceful” graphs
Summary
• Modelling decisions: Auxiliary variables Implied constraints Symmetry breaking constraints
• More to constraints than just declarative problem specifications!
All interval series
• Prob007 at www.csplib.org
• Comes from musical composition Traced back to Alban Berg Extensively used by Ernst Krenek
Op.170 “Quaestio temporis”
All interval series
• Take the 12 standard pitch classes c, c#, d, .. Represent them by numbers 0, .., 11
• Find a sequence so each occurs once Each difference occurs once
All interval series
• Can generalize to any n (not just 12)
Find Sn, a permutation of [0,n) such that |Sn+1-Sn| are all distinct
• Finding one solution is easy
All interval series
• Can generalize to any n (not just 12)
Find Sn, a permutation of [0,n) such that |Sn+1-Sn| are all distinct
• Finding one solution is easy[n,1,n-1,2,n-2,.., floor(n/2)+2,floor(n/2)-1,floor(n/2)+1,floor(n/2)]
Giving the differences [n-1,n-2,..,2,1]
Challenge is to find all solutions!
Basic methodology
• Devise basic CSP model What are the variables? What are the
constraints?
• Introduce auxiliary variables if needed
• Consider dual or combined models
• Break symmetry
• Introduce implied constraints
Basic CSP model
• What are the variables?Si = j if the ith note is j
• What are the constraints? Si in [0,n)
All-different([S1,S2,… Sn])
Forall i<i’ |Si+1 - Si| =/ |Si’+1 - Si’|
Will this model be any good? If so, why?
If not, why not?
Basic methodology
• Devise basic CSP model What are the variables? What are the
constraints?
• Introduce auxiliary variables if needed
• Consider dual or combined models
• Break symmetry
• Introduce implied constraints
Improving basic CSP model
• Is it worth introducing any auxiliary variables? Are there any loose or messy constraints we
could better (more compactly?) express via some auxiliary variables?
Improving basic CSP model
• Is it worth introducing any auxiliary variables? Yes, variables for the pairwise differences
Di = |Si+1 - Si|
• Now post single large all-different constraintDi in [1,n-1]
All-different([D1,D2,…Dn-1])
Basic methodology
• Devise basic CSP model What are the variables? What are the
constraints?
• Introduce auxiliary variables if needed
• Consider dual or combined models
• Break symmetry
• Introduce implied constraints
Break symmetry
• Does the problem have any symmetry? Yes, we can reverse any sequence
S1, S2, … Sn is an all-inverse series
Sn, …, S2, S1 is also
• How do we eliminate this symmetry?
Break symmetry
• Does the problem have any symmetry? Yes, we can reverse any sequence
S1, S2, …, Sn is an all-inverse seriesSn, …, S2, S1 is also
• How do we eliminate this symmetry?• As with Golomb ruler!
D1 < Dn-1
Break symmetry
• Does the problem have any other symmetry? Yes, we can invert the numbers in any sequence
0, n-1, 1, n-2, … map x onto n-1-x
n-1, 0, n-2, 1, …
• How do we eliminate this symmetry?
Break symmetry
• Does the problem have any other symmetry? Yes, we can invert the numbers in any sequence
0, n-1, 1, n-2, … map x onto n-1-x
n-1, 0, n-2, 1, …
• How do we eliminate this symmetry?S1 < S2
Basic methodology
• Devise basic CSP model What are the variables? What are the
constraints?
• Introduce auxiliary variables if needed
• Consider dual or combined models
• Break symmetry
• Introduce implied constraints
Implied constraints
• Are there useful implied constraints to add? Hmm, unlike Golomb ruler, we only have
neighbouring differences So, no need to consider transitive closure
Implied constraints
• Are there useful implied constraints to add? Hmm, unlike Golomb ruler, we are not
optimizing So, no need to improve propagation for
optimization variable
Performance
• Basic model is poor• Refined model able to compute all solutions
up to n=14 or so GAC on all-different constraints very beneficial As is enforcing GAC on Di = |Si+1-Si|
This becomes too expensive for large nSo use just bounds consistency (BC) for larger n
Modelling decisions
• Many different ways to model even simple problems
• Combining models can be effective Channel between models
• Need additional constraints Symmetry breaking Implied (but logically) redundant
Latin square
• Each colour appears once on each row
• Each colour appears once on each column
• Used in experimental design Six people Six one-week drug
trials
Orthogonal Latin squares
• Find a pair of Latin squares Every cell has a
different pair of elements
• Generalized form: Find a set of m Latin
squares Each possible pair is
orthogonal
Orthogonal Latin squares
1 2 3 4 1 2 3 42 1 4 3 3 4 1 23 4 1 2 4 3 2 14 3 2 1 2 1 4 3 11 22 33 44 23 14 41 32 34 43 12 21 42 31 24 13
• Two 4 by 4 Latin squares
• No pair is repeated
History of (orthogonal) Latin squares
• Introduced by Euler in 1783 Also called Graeco-Latin or Euler squares
• No orthogonal Latin square of order 2 There are only 2 (non)-isomorphic Latin
squares of order 2 and they are not orthogonal
History of (orthogonal) Latin squares
• Euler conjectured in 1783 that there are no orthogonal Latin squares of order 4n+2 Constructions exist for 4n and for 2n+1 Took till 1900 to show conjecture for n=1 Took till 1960 to show false for all n>1
• 6 by 6 problem also known as the 36 officer problem“… Can a delegation of six regiments, each of which sends a colonel,
a lieutenant-colonel, a major, a captain, a lieutenant, and a sub-lieutenant be arranged in a regular 6 by 6 array such that no row or column duplicates a rank or a regiment?”
More background
• Lam’s problem Existence of finite projective plane of order 10 Equivalent to set of 9 mutually orthogonal Latin
squares of order 10 In 1989, this was shown not to be possible after 2000
hours on a Cray (and some major maths)
• Orthogonal Latin squares also used in experimental design
A simple 0/1 model
• Suitable for integer programming Xijkl = 1 if pair (i,j) is in row k column l, 0 otherwise Avoiding advice never to use more than 3 subscripts!
• Constraints Each row contains one number in each square
Sum_jl Xijkl = 1 Sum_il Xijkl = 1 Each col contains one number in each square
Sum_jk Xijkl = 1 Sum_ik Xijkl = 1
A simple 0/1 model
• Additional constraints Every pair of numbers occurs exactly once
Sum_kl Xijkl = 1
Every cell contains exactly one pair of numbersSum_ij Xijkl = 1
Is there any symmetry?
Symmetry removal
• Important for solving CSPs Especially for proofs of optimality?
• Orthogonal Latin square has lots of symmetry Permute the rows Permute the cols Permute the numbers 1 to n in each square
• How can we eliminate such symmetry?
What about a CSP model?
• Exploit large finite domains possible in CSPs Reduce number of variables O(n^4) -> ?
• Exploit non-binary constraints Problem states that squares contain pairs that
are all different All-different is a non-binary constraint our
solvers can reason with efficiently
CSP model
• 2 sets of variables Skl = i if the 1st element in row k col l is i Tkl = j if the 2nd element in row k col l is j
• How do we specify all pairs are different? All distinct (k,l), (k’,l’) if Skl = i and Tkl = j then Sk’l’=/ i or Tk’l’ =/ j
O(n^4) loose constraints, little constraint propagation!
What can we do?
CSP model
• Introduce auxiliary variables Fewer constraints, O(n^2) Tightens constraint graph => more propagation Pkl = i*n + j if row k col l contains the pair i,j
• Constraints 2n all-different constraints on Skl, and on Tkl All-different constraint on Pkl Channelling constraint to link Pkl to Skl and
Tkl
CSP model v O/1 model
• CSP model 3n^2 variables Domains of size n, n
and n^2+n O(n^2) constraints Large and tight non-
binary constraints
• 0/1 model n^4 variables Domains of size 2 O(n^4) constraints Loose but linear
constraints• Use IP solver!
Solving choices for CSP model
• Variables to assign Skl and Tkl, or Pkl?
• Variable and value ordering
• How to treat all-different constraint GAC using Regin’s algorithm O(n^4) AC using the binary decomposition
Good choices for the CSP model
• Experience and small instances suggest: Assign the Skl and Tkl variables Choose variable to assign with Fail First
(smallest domain) heuristic• Break ties by alternating between Skl and Tkl
Use GAC on all-different constraints for Skl and Tkl
Use AC on binary decomposition of large all-different constraint on Pkl
Performance
n 0-1 model
Fails t/sec
CSP model AC
Fails t/sec
CSP model GAC
Fails t/sec
4 4 0.11 2 0.18 2 0.38
5 1950 4.05 295 1.39 190 1.55
6 ? ? 640235 657 442059 773
7* 20083 59.8 91687 51.1 57495 66.1
Langford’s problem
• Prob024 @ www.csplib.org
• Find a sequence of 8 numbers Each number [1,4]
occurs twice Two occurrences of i
are i numbers apart
• Unique solution 41312432
Langford’s problem
• L(k,n) problem To find a sequence of k*n
numbers [1,n] Each of the k successive
occrrences of i are i apart We just saw L(2,4)
• Due to the mathematician Dudley Langford Watched his son build a
tower which solved L(2,3)
Langford’s problem
• L(2,3) and L(2,4) have unique solutions• L(2,4n) and L(2,4n-1) have solutions
L(2,4n-2) and L(2,4n-3) do not Computing all solutions of L(2,19) took 2.5 years!
• L(3,n) No solutions: 0<n<8, 10<n<17, 20, .. Solutions: 9,10,17,18,19, ..
A014552Sequence: 0,0,1,1,0,0,26,150,0,0,17792,108144,0,0,39809640,326721800,
0,0,256814891280,2636337861200
Basic model
• What are the variables?Variable for each occurrence of a number
X11 is 1st occurrence of 1
X21 is 1st occurrence of 2
..
X12 is 2nd occurrence of 1
X22 is 2nd occurrence of 2
..
• Value is position in the sequence
Basic model
• What are the constraints? Xij in [1,n*k] Xij+1 = i+Xij Alldifferent([X11,..Xn1,X12,..Xn2,..,X1k,..Xnk
])
Recipe
• Create a basic model Decide on the variables
• Introduce auxiliary variables For messy/loose constraints
• Consider dual, combined or 0/1 models
• Break symmetry
• Add implied constraints
• Customize solver Variable, value ordering
Break symmetry
• How do we break this symmetry? Many possible ways For example, for L(3,9)
• Either X92 < 14 (2nd occurrence of 9 is in 1st half)
• Or X92=14 and X82<14 (2nd occurrence of 8 is in 1st half)
Recipe
• Create a basic model Decide on the variables
• Introduce auxiliary variables For messy/loose constraints
• Consider dual, combined or 0/1 models
• Break symmetry
• Add implied constraints
• Customize solver Variable, value ordering
Dual model
• What are the variables? Variable for each position i
• What are the values? If use the number at that position, we cannot
use an all-different constraint Each number occurs not once but k times
Dual model
• What are the variables? Variable for each position i
• What are the values? Solution 1: use values from [1,n*k] with the
value i*n+j standing for the ith occurrence of j Now want to find a permutation of these
numbers subject to the distance constraint
Dual model
• What are the variables? Variable for each position i
• What are the values? Solution 2: use as values the numbers [1,n] Each number occurs exactly k times Fortunately, there is a generalization of all-different
called the global cardinality constraint (gcc) for this
Global cardinality constraint
• Gcc([X1,..Xn],l,u) enforces values used by Xi to occur between l and u times All-different([X1,..Xn]) = Gcc([X1,..Xn],1,1)
• Regin’s algorithm enforces GAC on Gcc in O(n^2.d) Regin’s papers are tough to follow but this
seems to beat his algorithm for all-different!?
Dual model
• What are the constraints? Gcc([D1,…Dk*n],k,k) Distance constraints:
• Di=j then Di+j+1=j
Combined model
• Primal and dual variables
• Channelling to link them What do the channelling constraints look like?
Solving choices?
• Which variables to assign? Xij or Di, doesn’t seem to matter
• Which variable ordering heuristic? Fail First or Lex?
Solving choices?
• Which variables to assign? Xij or Di, doesn’t seem to matter
• Which variable ordering heuristic? Fail First very marginally better than Lex