Necklace splitting problem explained

Necklace splitting is a picturesque name given to several related problems in combinatorics and measure theory. Its name and solutions are due to mathematicians Noga Alon[1] and Douglas B. West.[2]

The basic setting involves a necklace with beads of different colors. The necklace should be divided between several partners (e.g. thieves), such that each partner receives the same amount of every color. Moreover, the number of cuts should be as small as possible (in order to waste as little as possible of the metal in the links between the beads).

Variants

The following variants of the problem have been solved in the original paper:

  1. Discrete splitting:[1] The necklace has

kn

beads. The beads come in

t

different colors. There are

kai

beads of each color

i

, where

ai

is a positive integer. Partition the necklace into

k

parts (not necessarily contiguous), each of which has exactly

ai

beads of color i. Use at most

(k-1)t

cuts. Note that if the beads of each color are contiguous on the necklace, then at least

k-1

cuts must be done inside each color, so

(k-1)t

is optimal.
  1. Continuous splitting:[1] The necklace is the real interval

[0,kn]

. Each point of the interval is colored in one of

t

different colors. For every color

i

, the set of points colored by

i

is Lebesgue-measurable and has length

kai

, where

ai

is a non-negative real number. Partition the interval to

k

parts (not necessarily contiguous), such that in each part, the total length of color

i

is exactly

ai

. Use at most

(k-1)t

cuts.
  1. Measure splitting:[1] The necklace is a real interval. There are

t

different measures on the interval, all absolutely continuous with respect to length. The measure of the entire necklace, according to measure

i

, is

kai

. Partition the interval to

k

parts (not necessarily contiguous), such that the measure of each part, according to measure

i

, is exactly

ai

. Use at most

(k-1)t

cuts. This is a generalization of the Hobby–Rice theorem, and it is used to get an exact division of a cake.

Each problem can be solved by the next problem:

[0,kn]

in which each interval of length 1 is colored by the color of the corresponding bead. In case the continuous splitting tries to cut inside beads, the cuts can be slid gradually such that they are made only between beads.[1]

t

colors can be converted to a set

t

measures, such that measure

i

measures the total length of color

i

. The opposite is also true: measure splitting can be solved by continuous splitting, using a more sophisticated reduction.[1]

Proof

The case

k=2

can be proved by the Borsuk-Ulam theorem.[2]

When

k

is an odd prime number, the proof involves a generalization of the Borsuk-Ulam theorem.[3]

When

k

is a composite number, the proof is as follows (demonstrated for the measure-splitting variant). Suppose

k=pq

. There are

t

measures, each of which values the entire necklace as

pqai

. Using

(p-1)t

cuts, divide the necklace to

p

parts such that measure

i

of each part is exactly

qai

. Using

(q-1)t

cuts, divide each part to

q

parts such that measure

i

of each part is exactly

ai

. All in all, there are now

pq

parts such that measure

i

of each part is exactly

ai

. The total number of cuts is

(p-1)t

plus

p(q-1)t

which is exactly

(pq-1)t

.

Further results

Splitting random necklaces

In some cases, random necklaces can be split equally using fewer cuts. Mathematicians Noga Alon, Dor Elboim, Gábor Tardos and János Pach studied the typical number of cuts required to split a random necklace between two thieves.[4] In the model they considered, a necklace is chosen uniformly at random from the set of necklaces with t colors and m beads of each color. As m tends to infinity, the probability that the necklace can be split using cuts or less tends to zero while the probability that it's possible to split with cuts is bounded away from zero. More precisely, letting X = X(t,m) be the minimal number of cuts required to split the necklace. The following holds as m tends to infinity. For any

s<(t+1)/2

P(X=s)=\Theta(ms-(t+1)/2).

For any

(t+1)/2<s\leqt

P(X=s)=\Theta(1).

Finally, when

t

is odd and

s=(t+1)/2

P(X=s)=\Theta((logm)-1).

One can also consider the case in which the number of colors tends to infinity. When m=1 and the t tends to infinity, the number of cuts required is at most 0.4t and at least 0.22t with high probability. It is conjectured that there exists some 0.22 < c < 0.4 such that X(t,1)/t  converges to c in distribution.

One cut fewer than needed

In the case of two thieves [i.e. ''k'' = 2] and t colours, a fair split would require at most t cuts. If, however, only t - 1 cuts are available, Hungarian mathematician Gábor Simonyi[5] shows that the two thieves can achieve an almost fair division in the following sense.

If the necklace is arranged so that no t-split is possible, then for any two subsets D1 and D2 of, not both empty, such that

D1\capD2=\varnothing

, a (t - 1)-split exists such that:

i\inD1

, then partition 1 has more beads of colour i than partition 2;

i\inD2

, then partition 2 has more beads of colour i than partition 1;

This means that if the thieves have preferences in the form of two "preference" sets D1 and D2, not both empty, there exists a (t - 1)-split so that thief 1 gets more beads of types in his preference set D1 than thief 2; thief 2 gets more beads of types in her preference set D2 than thief 1; and the rest are equal.

Simonyi credits Gábor Tardos with noticing that the result above is a direct generalization of Alon's original necklace theorem in the case k = 2. Either the necklace has a (t - 1)-split, or it does not. If it does, there is nothing to prove. If it does not, we may add beads of a fictitious colour to the necklace, and make D1 consist of the fictitious colour and D2 empty. Then Simonyi's result shows that there is a t-split with equal numbers of each real colour.

Negative result

For every

k\geq1

there is a measurable

(k+3)

-coloring of the real line such that no interval can be fairly split using at most

k

cuts.[6]

Splitting multidimensional necklaces

The result can be generalized to n probability measures defined on a d dimensional cube with any combination of n(k - 1) hyperplanes parallel to the sides for k thieves.[7]

Approximation algorithm

An approximation algorithm for splitting a necklace can be derived from an algorithm for consensus halving.[8]

See also

External links

Notes and References

  1. 10.1016/0001-8708(87)90055-7. Alon. Noga. Splitting Necklaces. Advances in Mathematics. 1987. 63. 3. 247 - 253. free.
  2. Alon. Noga. West . Douglas B.. The Borsuk-Ulam theorem and bisection of necklaces. Proceedings of the American Mathematical Society. December 1986. 98. 4 . 623 - 628. 10.1090/s0002-9939-1986-0861764-9. Borsuk-Ulam theorem. free.
  3. On a topological generalization of a theorem of Tverberg . I.Barany and S.B.Shlosman and A.Szucs . Journal of the London Mathematical Society . 1981 . 2 . 23 . 158–164 . 10.1112/jlms/s2-23.1.158. 10.1.1.640.1540 .
  4. Random Necklaces require fewer cuts. Alon. Noga. Elboim. Dor. Tardos. Gábor. Pach. János. 2021. math.CO. 2112.14488.
  5. 10.37236/891. Simonyi. Gábor. Necklace bisection with one cut less than needed . Electronic Journal of Combinatorics. 15. 2008. N16. free.
  6. Proceedings of the American Mathematical Society. Splitting necklaces and measurable colorings of the real line. November 25, 2008. 10.1090/s0002-9939-08-09699-8. free. 1088-6826. 137. 5. 1593–1599 . Alon . Noga. 1412.7996.
  7. 10.1016/j.aim.2008.02.003 . free . de Longueville. Mark. Rade T. Živaljević. Splitting multidimensional necklaces. Advances in Mathematics. 2008. 218 . 3. 926 - 939 . math/0610800.
  8. Simmons. Forest W.. Su. Francis Edward. February 2003. Mathematical Social Sciences. Consensus-halving via theorems of Borsuk-Ulam and Tucker. 45. 1. 15–25. 10.1016/s0165-4896(02)00087-2. 10.1.1.203.1189.