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:
- Discrete splitting:[1] The necklace has
beads. The beads come in
different colors. There are
beads of each color
, where
is a positive integer. Partition the necklace into
parts (not necessarily contiguous), each of which has exactly
beads of color
i. Use at most
cuts. Note that if the beads of each color are contiguous on the necklace, then at least
cuts must be done inside each color, so
is optimal.
- Continuous splitting:[1] The necklace is the real interval
. Each point of the interval is colored in one of
different colors. For every color
, the set of points colored by
is
Lebesgue-measurable and has length
, where
is a non-negative real number. Partition the interval to
parts (not necessarily contiguous), such that in each part, the total length of color
is exactly
. Use at most
cuts.
- Measure splitting:[1] The necklace is a real interval. There are
different measures on the interval, all absolutely continuous with respect to length. The measure of the entire necklace, according to measure
, is
. Partition the interval to
parts (not necessarily contiguous), such that the measure of each part, according to measure
, is exactly
. Use at most
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:
- Discrete splitting can be solved by continuous splitting, since a discrete necklace can be converted to a coloring of the real interval
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] - Continuous splitting can be solved by measure splitting, since a coloring of an interval in
colors can be converted to a set
measures, such that measure
measures the total length of color
. The opposite is also true: measure splitting can be solved by continuous splitting, using a more sophisticated reduction.
[1] Proof
The case
can be proved by the
Borsuk-Ulam theorem.
[2] When
is an odd
prime number, the proof involves a generalization of the Borsuk-Ulam theorem.
[3] When
is a
composite number, the proof is as follows (demonstrated for the measure-splitting variant). Suppose
. There are
measures, each of which values the entire necklace as
. Using
cuts, divide the necklace to
parts such that measure
of each part is exactly
. Using
cuts, divide each part to
parts such that measure
of each part is exactly
. All in all, there are now
parts such that measure
of each part is exactly
. The total number of cuts is
plus
which is exactly
.
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
P(X=s)=\Theta(ms-(t+1)/2).
For any
Finally, when
is odd and
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
, a (
t - 1)-split exists such that:
, then partition 1 has more beads of colour
i than partition 2;
, then partition 2 has more beads of colour
i than partition 1;
- If colour i is in neither partition, both partitions have equally many beads of colour i.
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
there is a measurable
-coloring of the real line such that no interval can be fairly split using at most
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
- , an introductory video presenting the problem with its topological solution.
Notes and References
- 10.1016/0001-8708(87)90055-7. Alon. Noga. Splitting Necklaces. Advances in Mathematics. 1987. 63. 3. 247 - 253. free.
- 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.
- 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 .
- Random Necklaces require fewer cuts. Alon. Noga. Elboim. Dor. Tardos. Gábor. Pach. János. 2021. math.CO. 2112.14488.
- 10.37236/891. Simonyi. Gábor. Necklace bisection with one cut less than needed . Electronic Journal of Combinatorics. 15. 2008. N16. free.
- 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.
- 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.
- 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.