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# Case study 3: orthogonal Latin squares - PowerPoint PPT Presentation

Case study 3: orthogonal Latin squares. Modelled by Barbara Smith. 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.

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### Case study 3: orthogonal Latin squares

Modelled by Barbara Smith

• Many different ways to model even simple problems

• Combining models can be effective

• Channel between models

• Symmetry breaking

• Implied (but logically) redundant

• 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

2 1 4 3 3 4 1 2

3 4 1 2 4 3 2 1

4 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

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

• 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?”

• 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 are used in experimental design

• To ensure no dependency between independent variables

• 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

• Every pair of numbers occurs exactly once

Sum_kl Xijkl = 1

• Every cell contains exactly one pair of numbers

Sum_ij Xijkl = 1

Is there any symmetry?

• 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?

• Fix first row

11 22 33 …

• Fix first column

11

23

32

..

• Eliminates all this symmetry?

• 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

• 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?

• 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

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!

CSP model v O/1 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

• 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

• 4 subscripts in 0/1 model are interchangeable

• Suggests a dual model

• DSij = k if the pair (i,j) occurs in row k

• DTij = l if the pair (i,j) occurs in the row l

• DPij = k*n + l if the pair (i,j) occurs in row i col j

• Dual constraints to the primal model

• Primal and dual model together

• Channelling constraints to link them

• But new search decisions

• Do we assign both primal and dual variables?

• How do handle dual constraints (AC, GAC …)?

• Other dualities

• Any choice of 2 subscripts from 4

• Diminishing returns

• Many ways to model even simple problems

• Introduce auxiliary variables

• Reduce number of constraints, improve propagation

• Combining models often beneficial

• Need to deal with symmetry

• Don’t always use GAC on all-different constraints

• Choose a basic model

• Consider auxiliary variables

• To reduce number of constraints, improve propagation

• Consider combined models

• Channel between views

• Break symmetries

• To improve propagation

### Case study 4: template design

Again model due to Barbara Smith

• Prob002 at www.csplib.org

• Problem comes from a printing firm

• Cat food labels that need to be printed using templates

• Several designs (tuna, chicken, …) go on each template

• Different demand for each flavour

• Aside: where did cats get the taste for tuna?

• For Francesca’s benefit

• How else can I get a cat picture into my talk?

• Assume number of templates is fixed

• Variables

• Pij = number of slots on template i for design j

• Ri = run length for template i

• Constraints

• Sum_j Pij = s, number of slots on each template

• Sum_i Pij * Ri >= dj, total production equals demand

• Optimization problem

• Introduce variable to minimize

• Production = Sum_i Ri

• Solved as sequence of decision problems

Production < l1, Production < l2 …

l1 set to minimum number of printings with 1 template

• Does the model have any symmetry?

• If so, how can we eliminate it?

• The templates are indistinguishable and can be permuted

• Swap all designs on one template with all those on a second template

• Break this symmetry by distinguishing the templates

• R1 <= R2 <= R3 …

• Designs j, k with the same demand are indistinguishable

• We can break this symmetry

• [P1j,P2j,P3j,…] <lex [P1k,P2k,P3k,…]

• Efficient GAC algorithm for lex ordering constraint

• Symmetries can be subtle to spot!

• Consider designs j and j’ with demand for j less than for j’

• Suppose we produce more of j than j’

• We could swap j and j’ and still have solution

• Prevent this with constraint on production

• Sum_i Pij Ri <= Sum_i Pij’ Ri

• We should always look for implied constraints we can add to model

• Encourage constraint propagation

• 2 templates

• R1+R2 = Production

• R1 <= R2

• Hence

R1 <= Production/2

R2 >= Production/2

• 3 templates

• R1 + R2 + R3 = Production

• R1 <= R2 <= R3

• Hence

R1 <= Production/3

R2 <= Production/2

R3 >= Production/3

• Variable ordering

• As with Golomb ruler, assign variables to construct solution systematically

• Assign all designs on one template before moving on to a second template

• Encourages constraint propagation on runlength constraints

• Basic model

• Difficult to solve 2 or 3 template problems

• Full model

• Problem solved quickly

• Can solve much larger problems than feasible with the basic model

• Optimality can still be tough!

• Basic model often obvious

• To refine such a model we need:

• Consider dual/combined models

• Symmetries eliminated

• Implied constraints

• Variable ordering heuristics

• Hopefully you can start to see patterns in what we do!