Recall from an earlier blog post that the minimal superpermutation problem asks for the shortest string on the symbols “1”, “2”, …, “n” that contains every permutation of those symbols as a contiguous substring. For example, “121” is a minimal superpermutation on the symbols “1” and “2”, since it contains both “12” and “21” as substrings, and there is no shorter string with this property.

Until now, the length of minimal superpermutations has only been known when n ≤ 4: they have length 1, 3, 9, and 33 in these cases, respectively. It has been conjectured that minimal superpermutations have length \(\sum_{k=1}^n k!\) for all n, and I am happy to announce that Ben Chaffin has proved this conjecture when n = 5. More specifically, he showed that minimal superpermutations in the n = 5 case have length 153, and there are exactly 8 such superpermutations (previously, it was only known that minimal superpermutations have either length 152 or 153 in this case, and there are at least 2 superpermutations of length 153).

The Eight Minimal Superpermutations

The eight superpermutations that Ben found are available here (they’re too large to include in the body of this post). Notice that the first superpermutation is the well-known “easy-to-construct” superpermutation described here, and the second superpermutation is the one that was found in [1]. The other six superpermutations are new.

One really interesting thing about the six new superpermutations is that they are the first known minimal superpermutations to break the “gap pattern” that previously-known constructions have. To explain what I mean by this, consider the minimal superpermutation “123121321” on three symbols. We can think about generating this superpermutation greedily: we start with “123”, then we append the character “1” to add the permutation “231” to the string, and then we append the character “2” to add the permutation “312” to the string. But now we are stuck: we have “12312”, and there is no way to append just one character to this string in such a way as to add another permutation to it: we have to append the two characters “13” to get the new permutation “213”.

This phenomenon seemed to be fairly general: in all known small superpermutations on n symbols, there was always a point (approximately halfway through the superpermutation) where n-2 consecutive characters were “wasted”: they did not add any new permutations themselves, but only “prepared” the next symbol to add a new permutation.

However, none of the six new minimal superpermutations have this property: they all never have more than 2 consecutive “wasted” characters, whereas the two previously-known superpermutations each have a run of n-2 = 3 consecutive “wasted” characters. Thus these six new superpermutations are really quite different from any superpermutations that we currently know and love.

How They Were Found

The idea of Ben’s search is to do a depth-first search on the placement of the “wasted” characters (recall that “wasted” characters were defined and discussed in the previous section). Since the shortest known superpermutation on 5 symbols has length 153, and there are 120 permutations of 5 symbols, and the first n-1 = 4 characters of the superpermutation must be wasted, we are left with the problem of trying to place 153 – 120 – 4 = 29 wasted characters. If we can find a superpermutation with only 28 wasted characters (other than the initial 4), then we’ve found a superpermutation of length 152; if we really need all 29 wasted characters, then minimal superpermutations have length 153.

So now we do the depth-first search:

• Find (via brute-force) the maximum number of permutations that we can fit in a string if we are allowed only 1 wasted character: the answer is 10 permutations (for example, the string “123451234152341” does the job).
• Now find the maximum number of permutations that we can fit in a string if we are allowed 2 wasted characters. To speed up the search, once we have found a string that contains some number (call it p) of permutations, we can ignore all other strings that use a wasted character before p-10 permutations, since we know from the previous bullet point that the second wasted character can add at most 10 more permutations, for a total of (p-10)+10 = p permutations.
• We now repeat this process for higher and higher numbers of wasted characters: we find the maximum number of permutations that we can fit in a string with 3 wasted characters, using the results from the previous two bullets to speed up the search by ignoring strings that place 1 or 2 wasted characters too early.
• Etc.

The results of this computation are summarized in the following table:

Wasted characters	Maximum # of permutations
0	5
1	10
2	15
3	20
4	23
5	28
6	33
7	36
8	41
9	46
10	49
11	53
12	58
13	62
14	66
15	70
16	74
17	79
18	83
19	87
20	92
21	96
22	99
23	103
24	107
25	111
26	114
27	116
28	118
29	120

As we can see, it is not possible to place all 120 permutations in a string with 28 or fewer wasted characters, which proves that there is no superpermutation of length 152 in the n = 5 case. C code that computes the values in the above table is available here.

Update [August 18, 2014]: Robin Houston has found a superpermutation on 6 symbols of length 873 (i.e., the conjectured minimal length) with the interesting property that it never has more than one consecutive wasted character! The superpermutation is available here.

IMPORTANT UPDATE [August 22, 2014]: Robin Houston has gone one step further and disproved the minimal superpermutation conjecture for all n ≥ 6. See here.

References

1. N. Johnston. Non-uniqueness of minimal superpermutations. Discrete Mathematics, 313:1553–1557, 2013.

Suppose we are given n distinct positive real numbers \(a_1 > a_2 > \cdots > a_n > 0\). The question we are going to consider in this post is as follows:

Question. How many different possible orderings are there of the \(n(n+1)/2\) numbers \(\{a_ia_j\}_{1\leq i\leq j\leq n}\)?

To help illustrate what we mean by this question, consider the n = 2 case, where \(a_1 > a_2 > 0\). Then the 3 possible products of \(a_1\) and \(a_2\) are \(a_1^2, a_2^2, a_1a_2\), and it is straightforward to see that we must have \(a_1^2 > a_1a_2> a_2^2\), so there is only one possible ordering in the n = 2 case.

In the n = 3 case, we have \(a_1 > a_2 > a_3 > 0\) and 6 possible products: \(a_1^2, a_2^2, a_3^2, a_1a_2, a_1a_3, a_2a_3\). Some relationships between these 6 numbers are immediate, such as \(a_1^2 > a_1a_2 > a_1a_3 > a_2a_3 > a_3^2\). However, it could be the case that either \(a_2^2 > a_1a_3\) or \(a_1a_3 > a_2^2\) (we ignore the degenerate cases where two products are equal to each other), so there are two different possible orderings in this case:

\(a_1^2 > a_1a_2 > a_2^2 > a_1a_3 > a_2a_3 > a_3^2\quad\text{ or }\\ a_1^2 > a_1a_2 > a_1a_3 > a_2^2 > a_2a_3 > a_3^2.\)

In this post, we will consider the problem of how many such orderings exist for larger values of n. This problem arises naturally from a problem in quantum entanglement: the number of such orderings is exactly the minimum number of linear matrix inequalities needed to characterize the eigenvalues of quantum states that are “PPT from spectrum” [1].

A Rough Upper Bound

We now begin constructing upper bounds on the number of possible orderings of \(\{a_ia_j\}_{1\leq i\leq j\leq n}\). Since we are counting orderings between \(n(n+1)/2\) numbers, a trivial upper bound is given by \((n(n+1)/2)!\), since that is the number of possible orderings of \(n(n+1)/2\) arbitrary numbers. However, this quantity is a gross overestimate.

We can improve this upper bound by creating an \(n \times n\) matrix whose \((i,j)\)-entry is \(a_ia_j\) (note that this matrix is symmetric, positive semidefinite, and has rank 1, which is roughly how the connection to quantum entanglement arises). For example, in the n = 4 case, this matrix is as follows:

\(\begin{bmatrix}a_1^2 & a_1a_2 & a_1a_3 & a_1a_4 \\ * & a_2^2 & a_2a_3 & a_2a_4 \\ * & * & a_3^2 & a_3a_4 \\ * & * & * & a_4^2\end{bmatrix},\)

where we have used asterisks (*) to indicate entries that are determined by symmetry. The fact that \(a_1 > a_2 > \cdots > a_n > 0\) implies that the rows and columns of the upper-triangular part of this matrix are decreasing. Thus we can get an upper bound to the solution to our problem by counting the number of ways that we can place the numbers \(1, 2, \ldots, n(n+1)/2\) (exactly once each) in the upper-triangular part of a matrix in such a way that the rows and columns of that upper-triangular part are decreasing. For example, this can be done in 2 different ways in the n = 3 case:

\(\begin{bmatrix}6 & 5 & 4 \\ * & 3 & 2 \\ * & * & 1\end{bmatrix} \quad \text{and} \quad \begin{bmatrix}6 & 5 & 3\\ * & 4 & 2\\ * & * & 1\end{bmatrix}.\)

The matrix above on the left corresponds to the case \(a_1a_3 > a_2^2\) discussed earlier, while the matrix above on the right corresponds to the case \(a_2^2 > a_1a_3\).

A formula for the number of such ways to place the integers \(1, 2, \ldots, n(n+1)/2\) in a matrix was derived in [2] (see also A003121 in the OEIS), which immediately gives us the following upper bound on the number of orderings of the products \(\{a_ia_j\}_{1\leq i\leq j\leq n}\):

\(\displaystyle(n(n+1)/2)! \frac{1! 2! \cdots (n-1)!}{1! 3! \cdots (2n-1)!}.\)

For n = 1, 2, 3, …, this formula gives the values 1, 1, 2, 12, 286, 33592, 23178480, …

A Better Upper Bound

Before improving the upper bound that we just presented, let’s first discuss why it is not actually a solution to the original question. In the n = 4 case, our best upper bound so far is 12, since there are 12 different ways to place the integers \(1,2,\ldots,10\) in the upper-triangular part of a \(4 \times 4\) matrix such that the rows and columns of that upper-triangular part are decreasing. However, one such placement is as follows:

\(\begin{bmatrix}10 & 9 & 7 & 6 \\ * & 8 & 5 & 3 \\ * & * & 4 & 2 \\ * & * & * & 1\end{bmatrix}.\)

The above matrix corresponds to the following inequalities in terms of \(\{a_ia_j\}_{1\leq i\leq j\leq n}\):

\(a_1^2 > a_1a_2 > a_2^2 > a_1a_3 > a_1a_4 > a_2a_3 > a_3^2 > a_2a_4 > a_3a_4 > a_4^2.\)

The problem here is that there actually do not exist real numbers \(a_1 > a_2 > a_3 > a_4 > 0\) that satisfy the above string of inequalities. To see this, notice in particular that we have the following three inequalities: \(a_2^2 > a_1a_3\), \(a_1a_4 > a_2a_3\), and \(a_3^2 > a_2a_4\). However, multiplying the first two inequalities together gives \(a_1a_2^2a_4 > a_1a_2a_3^2\), so \(a_2a_4 > a_3^2\), which contradicts the third inequality.

More generally, there can not be indices \(i,j,k,\ell,m,n\) such that we simultaneously have the following three inequalities:

\(a_ia_j > a_ka_\ell\), \(a_\ell a_m > a_j a_n\), and \(a_i a_m < a_k a_n\).

I am not aware of a general formula for the number integer matrices that do not lead to these types of “bad” inequalities, but I have computed this quantity for n ≤ 7 (C code is available here), which gives the following better upper bound on the number of possible orderings of the products \(\{a_ia_j\}_{1\leq i\leq j\leq n}\) for n = 1, 2, 3, …: 1,1,2,10,114,2612,108664, …, which we see is significantly smaller than the upper bound found in the previous section for n ≥ 5.

This Bound is Not Tight

It is straightforward to write a script that generates random numbers \(a_1 > a_2 > \cdots > a_n > 0\) and determines the resulting ordering of the pairwise products \(\{a_ia_j\}_{1\leq i\leq j\leq n}\). By doing this, we can verify that the upper bounds from the previous section are in fact tight when n ≤ 5. However, when n = 6, we find that 4 of the 2612 potential orderings do not seem to actually be attained by any choice of \(a_1 > a_2 > \cdots > a_6 > 0\). One of these “problematic” orderings is the one that arises from the following matrix:

\(\begin{bmatrix}21 & 20 & 19 & 18 & 17 & 11\\ * & 16 & 15 & 14 & 10 & 6\\ * & * & 13 & 12 & 8 & 5\\ * & * & * & 9 & 7 & 3\\ * & * & * & * & 4 & 2\\ * & * & * & * & * & 1\end{bmatrix}\)

The problem here is that the above matrix implies the following 5 inequalities:

\(a_1a_5 > a_2^2, \quad \ \ a_2a_4 > a_3^2, \quad \ \ a_2a_5 > a_4^2, \quad \ \ a_3a_4 > a_1a_6, \quad \text{and }\ \ \ a_3a_6 > a_5^2.\)

However, multiplying the first four inequalities gives \(a_1a_2^2a_3a_4^2a_5^2 > a_1a_2^2a_3^2a_4^2a_6\), so \(a_5^2 > a_3a_6\), which contradicts the fifth inequality above. We can similarly prove that the other 3 seemingly problematic orderings are in fact not attainable, so there are exactly 2608 possible orderings in the n = 6 case.

I haven’t been able to compute the number of orderings when n ≥ 7, as my methods for obtaining upper and lower bounds are both much too slow in these cases. The best bounds that I have in the n = 7 case say that the number of orderings is between 50900 and 108664, inclusive.

Update [Feb. 13, 2014]: Giovanni Resta has improved the lower bound in the n = 7 case to 107498, which narrows the n = 7 region down considerably. I’ve also improved the upper bound to 108146 (see this improved version of the C script). In all likelihood, 107498 is the correct number of orderings in this case, and it’s the upper bound 108146 that needs to be further improved.

Update [Feb. 14, 2014]: This sequence is now in the OEIS. See A237749.

Update [Feb. 18, 2014]: Hans Havermann has found a couple of references that talk about this problem (in the language of Golomb rulers) and compute all values for n ≤ 7. See [3] and [4].

References

1. R. Hildebrand. Positive partial transpose from spectra. Phys. Rev. A, 76:052325, 2007. E-print: arXiv:quant-ph/0502170
2. R. M. Thrall. A combinatorial problem. Michigan Math. J., 1:81–88, 1952.
3. M. Beck, T. Bogart, and T. Pham. Enumeration of Golomb rulers and acyclic orientations of mixed graphs. Electron. J. Combin., 19:42, 2012. E-print: arXiv:1110.6154 [math.CO]
4. T. Pham. Enumeration of Golomb rulers. Master’s Thesis, San Francisco State University, 2011.

Imagine that there is a TV series that you want to watch. The series consists of n episodes, with each episode on a single DVD. Unfortunately, however, the DVDs have become mixed up and the order of the episodes is in no way marked (and furthermore, the episodes of the TV show are not connected by any continuous storyline – there is no way to determine the order of the episodes just from watching them).

Suppose that you want to watch the episodes of the TV series, consecutively, in the correct order. The question is: how many episodes must you watch in order to do this?

To illustrate what we mean by this question, suppose for now that n = 2 (i.e., the show was so terrible that it was cancelled after only 2 episodes). If we arbitrarily label one of the episodes “1” and the other episode “2”, then we could watch the episodes in the order “1”, “2”, and then “1” again. Then, regardless of which episode is really the first episode, we’ve seen the two episodes consecutively in the correct order. Furthermore, this is clearly minimal – there is no way to watch fewer than 3 episodes while ensuring that you see both episodes in the correct order.

So what is the minimal number of episodes we must watch for a TV show consisting of n episodes? Somewhat surprisingly, no one knows. So let’s discuss what is known.

Minimal Superpermutations

Rephrased a bit more mathematically, we are interested in finding a shortest possible string on the symbols “1”, “2”, …, “n” that contains every permutation of those symbols as a contiguous substring. We call a string that contains every permutation in this way a superpermutation, and one of minimal length is called a minimal superpermutation. Minimal superpermutations when n = 1, 2, 3, 4 are easily found via brute-force computer search, and are presented here:

n	Minimal Superpermutation	Length
1	1	1
2	121	3
3	123121321	9
4	123412314231243121342132413214321	33

By the time n = 5, the strings we are looking for are much too long to find via brute-force. However, the strings in the n ≤ 4 cases provide some insight that we can hope might generalize to larger n. For example, there is a natural construction that allows us to construct a short superpermutation on n+1 symbols from a short superpermutation on n symbols (which we will describe in the next section), and this construction gives the minimal superpermutations presented in the above table when n ≤ 4.

Similarly, the minimal superpermutations in the above table can be shown via brute-force to be unique (up the relabeling the characters – for example, we don’t count the string “213212312” as distinct from “123121321”, since they are related to each other simply by interchanging the roles of “1” and “2”). Are minimal superpermutations unique for all n?

Minimal Length

A trivial lower bound on the length of a superpermutation on n symbols is n! + n – 1, since it must contain each of the n! permutations as a substring – the first permutation contributes a length of n characters to the string, and each of the remaining n! – 1 permutations contributes a length of at least 1 character more.

It is not difficult to improve this lower bound to n! + (n-1)! + n – 2 (I won’t provide a proof here, but the idea is to note that when building the superpermutation, you can not add more than n-1 permutations by appending just 1 character each to the string – you eventually have to add 2 or more characters to add a permutation that is not already present). In fact, this argument can be stretched further to show that n! + (n-1)! + (n-2)! + n – 3 is a lower bound as well (a rough proof is provided here). However, the same arguments do not seem to extend to lower bounds like n! + (n-1)! + (n-2)! + (n-3)! + n – 4 and so on.

There is also a trivial upper bound on the length of a minimal superpermutation: n×n!, since this is the length of the string obtained by writing out the n! permutations in order without overlapping. However, there is a well-known construction of small superpermutations that provides a much better upper bound, which we now describe.

Suppose we know a small superpermutation on n symbols (such as one of the superpermutations provided in the table in the previous section) and we want to construct a small superpermutation on n+1 symbols. To do so, simply replace each permutation in the n-symbol superpermutation by: (1) that permutation, (2) the symbol n+1, and (3) that permutation again. For example, if we start with the 2-symbol superpermutation “121”, we replace the permutation “12” by “12312” and we replace the permutation “21” by “21321”, which results in the 3-symbol superpermutation “123121321”. The procedure for constructing a 4-symbol superpermutation from this 3-symbol superpermutation is illustrated in the following diagram:

It is a straightforward inductive argument to show that the above method produces n-symbol superpermutations of length \(\sum_{k=1}^nk!\) for all n. Although it has been conjectured that this superpermutation is minimal [1], this is only known to be true when n ≤ 4.

Uniqueness

As a result of minimal superpermutations being unique when n ≤ 4, it has been conjectured that they are unique for all n [1]. However, it turns out that there are in fact many superpermutations of the conjectured minimal length – the main result of [2] shows that there are at least

\(\displaystyle\prod_{k=1}^{n-4}(n-k-2)^{k\cdot k!}\)

distinct n-symbol superpermutations of the conjectured minimal length. For n ≤ 4, this formula gives the empty product (and thus a value of 1), which agrees with the fact that minimal superpermutations are unique in these cases. However, the number of distinct superpermutations then grows extremely quickly with n: for n  = 5, 6, 7, 8, there are at least 2, 96, 8153726976, and approximately 3×10⁵⁰ superpermutations of the conjectured minimal length. The 2 such superpermutations in the n = 5 case are as follows (each superpermutation has length 153 and is written on two lines):

12345123415234125341235412314523142531423514231542312453124351243152431254312
1345213425134215342135421324513241532413524132541321453214352143251432154321

and

12345123415234125341235412314523142531423514231542312453124351243152431254312
1354213524135214352134521325413251432513425132451321543215342153241532145321

Similarly, a text file containing all 96 known superpermutations of the expected minimal length 873 in the n = 6 case can be viewed here. It is unknown, however, whether or not these superpermutations are indeed minimal or if there are even more superpermutations of the conjectured minimal length.

Update [Aug. 13, 2014]: Ben Chaffin has shown that minimal superpermutations in the n = 5 case have length 153, and he has also shown that there are exactly 8 (not just 2) distinct minimal superpermutations in this case. See the write up here.

IMPORTANT UPDATE [August 22, 2014]: Robin Houston has disproved the minimal superpermutation conjecture for all n ≥ 6. See here.

References

1. D. Ashlock and J. Tillotson. Construction of small superpermutations and minimal injective superstrings. Congressus Numerantium, 93:91–98, 1993.
   
2. N. Johnston. Non-uniqueness of minimal superpermutations. Discrete Mathematics, 313:1553–1557, 2013.

Other Random Links Related to This Problem

1. A180632 – the main OEIS entry for this problem
2. Permutation Strings – a short note written by Jeffrey A. Barnett about this problem
3. Generate sequence with all permutations – a stackoverflow post about this problem
4. What is the shortest string that contains all permutations of an alphabet? – a mathexchange post about this problem
5. The shortest string containing all permutations of n symbols – an (archived) XKCD forums post that I made about this problem a couple years ago

Final Fantasy XIII-2 is a role-playing game, released last week in North America, that contains an abundance of mini-games. One of the more interesting mini-games is the “clock puzzle”, which presents the user with N integers arranged in a circle, with each integer being from 1 to \(\lfloor N/2 \rfloor\).

The way the game works is as follows:

1. The user may start by picking any of the N positions on the circle. Call the number in this position M.
2. You now have the option of picking either the number M positions clockwise from your last choice, or M positions counter-clockwise from your last choice. Update the value of M to be the number in the new position that you chose.
3. Repeat step 2 until you have performed it N-1 times.

You win the game if you choose each of the N positions exactly once, and you lose the game otherwise (if you are forced to choose the same position twice, or equivalently if there is a position that you have not chosen after performing step 2 a total of N-1 times). During the game, N ranges from 5 to 13, though N could theoretically be as large as we like.

Example

To demonstrate the rules in action, consider the following simple example with N = 6 (I have labelled the six positions 0 – 5 in blue for easy reference):

If we start by choosing the 1 in position 1, then we have the option of choosing the 3 in either position 0 or 2. Let’s choose the 3 in position 0. Three moves either clockwise or counter-clockwise from here both give the 1 in position 3, so that is our only possible next choice. We continue on in this way, going through the N = 6 positions in the order 1 – 0 – 3 – 4 – 2 – 5, as in the following image:

We have now selected each position exactly once, so we are done – we solved the puzzle! In fact, this is the unique solution for the given puzzle.

Counting Clock Puzzles

Let’s work on determining how many different clock puzzles there are of a given size. As mentioned earlier, a clock puzzle with N positions has an integer in the interval \([1, \lfloor N/2 \rfloor]\) in each of the  positions. There are thus \(\lfloor N/2 \rfloor^N\) distinct clock puzzles with N positions, which grows very quickly with N – its values for N = 1, 2, 3, … are given by the sequence 0, 1, 1, 16, 32, 729, 2187, 65536, 262144, … (A206344 in the OEIS).

However, this rather crude count of the number of clock puzzles ignores the fact that some clock puzzles have no solution. To illustrate this fact, we present the following simple proposition:

Proposition. There are unsolvable clock puzzles with N positions if and only if N = 4 or N ≥ 6.

To prove this proposition, first note that the clock puzzles for N = 2 or N = 3 are trivially solvable, since each number in the puzzle is forced to be \(\lfloor N/2 \rfloor = 1\). The 32 clock puzzles in the N = 5 case can all easily be shown to be solvable via computer brute force (does anyone have a simple or elegant argument for this case?).

In the N = 4 case, exactly 3 of the 16 clock puzzles are unsolvable:

To complete the proof, it suffices to demonstrate an unsolvable clock puzzle for each N ≥ 6. To this end, we begin by considering the following clock puzzle in the N = 6 case:

The above puzzle is unsolvable because the only way to reach position 0 is to select it first, but from there only one of positions 2 or 4 can be reached – not both. This example generalizes in a straightforward manner to any N ≥ 6 simply by adding more 1’s to the bottom: it will still be necessary to choose position 0 first, and then it is impossible to reach both position 2 and position N-2 from there.

There doesn’t seem to be an elegant way to count the number of solvable clock puzzles with N positions (which is most likely related to the apparent difficulty of solving these puzzles, which will be discussed in the next section), so let’s count the number of solvable clock puzzles via brute force. Simply constructing each of the \(\lfloor N/2 \rfloor^N\) clock puzzles and determining which of them are solvable (via the MATLAB script linked at the end of this post) shows that the number of solvable clock puzzles for N = 1, 2, 3, … is given by the sequence 0, 1, 1, 13, 32, 507, 1998, 33136, 193995, … (A206345 in the OEIS).

This count of puzzles is perhaps still unsatisfying, though, since it counts puzzles that are simply mirror images or rotations of each other multiple times. Again, there doesn’t seem to be an elegant counting argument for enumerating the solvable clock puzzles up to rotation and reflection, so we compute this sequence by brute force: 0, 1, 1, 4, 8, 72, 236, 3665, 19037, … (A206346 in the OEIS).

Solving Clock Puzzles

Clock puzzles are one of the most challenging parts of Final Fantasy XIII-2, and with good reason: they are a well-studied graph theory problem in disguise. We can consider each clock puzzle with N positions as a directed graph with N vertices. If position N contains the number M, then there is a directed edge going from vertex N to the vertices M positions clockwise and counter-clockwise from it. In other words, we consider a clock puzzle as a directed graph on N vertices, where the directed edges describe the valid moves around the circle.

The directed graph corresponding to the earlier (solvable) N = 6 example

The problem of solving a clock puzzle is then exactly the problem of finding a directed Hamiltonian path on the associated graph. Because finding a directed Hamiltonian path in general is NP-hard, this seems to suggest that solving clock puzzles might be as well. There of course is the problem that the directed graphs relevant to this problem have very special structure – in particular, every vertex has outdegree ≤ 2, and the graph has a symmetry property that results from clockwise/counter-clockwise movement allowed in the clock puzzles.

The main result of [1] shows that the fact that the outdegree of each vertex is no larger than 2 is no real help: finding directed Hamiltonian paths is still NP-hard given such a promise. However, the symmetry condition seems more difficult to characterize in graph theoretic terms, and could potentially be exploited to produce a fast algorithm for solving these puzzles.

Regardless of the problem’s computational complexity, the puzzles found in the game are quite small (N ≤ 13), so they can be easily solved by brute force. Attached is a MATLAB script (solve_clock.m) that can be used to solve clock puzzles. The first input argument is a vector containing the numeric values in each of the positions, starting from the top and reading clockwise. By default, only one solution is computed. To compute all solutions, set the second (optional) input argument to 1.

The output of the script is either a vector of positions (labelled 0 through N-1, with 0 referring to the top position, 1 referring to one position clockwise from there, and so on) describing an order in which you can visit the positions to solve the puzzle, or 0 if there is no solution.

For example, the script can be used to find our solution to the N = 6 example provided earlier:

>> solve_clock([3,1,3,1,2,3])

ans =
    1 0 3 4 2 5

Similarly, the script can be used to find all four solutions [Update, October 1, 2013: Whoops, there are six solutions! See the comments.] to the puzzle in the screenshot at the very top of this post:

>> solve_clock([6,5,1,4,2,1,6,4,2,1,5,2], 1)

ans =
    3 7 11 9 10 5 4 2 1 8 6 0
    7 3 11 9 10 5 4 2 1 8 6 0
    9 10 5 4 2 3 7 11 1 8 6 0
    9 8 10 5 4 2 3 7 11 1 6 0

Download

• solve_clock.m – a MATLAB script that solves clock puzzles

References

1. J. Plesnik. The NP-completeness of the Hamiltonian cycle problem in planar digraphs with degree bound two. Inform. Process. Lett., 8:199–201, 1979.

The Q-toothpick cellular automaton (defined earlier this month by Omar E. Pol) is described by the following simple rules:

1. On an infinite square grid, draw a quarter circle from one corner of a square to the opposite corner of that square:
2. Call an endpoint of a quarter circle (or a “Q-toothpick”) exposed if it does not touch the endpoint of any other quarter circle.
3. From each exposed endpoint, draw two more quarter circles, each of the same size as the first quarter circle you drew. Furthermore, the two quarter circles that you draw are the ones that can be drawn “smoothly” (without creating a 90° or 180° corner). Thus the next two generations of the automaton are (already-placed quarter circles are green, newly-added quarter circles are red):
   

The name “Q-toothpick” comes from its analogy to the more well-studied toothpick automaton (see Sloane’s A139250 and this paper), in which toothpicks (rather than quarter circles) are repeatedly placed on a grid where exposed ends of other toothpicks lie. In this post, we will examine how this automaton evolves over time, and in particular we will investigate the types of shapes that it produces.

Counting Q-Toothpicks

While the Q-toothpick automaton appears quite random and unpredictable for the first few generations, evolving past generation 6 or so reveals several patterns. The following image depicts the evolution of the automaton for its first 19 generations.

Perhaps the most notable pattern is that the grid is more or less filled up in an expanding square starting from the initial Q-toothpick. In fact, by inspecting generations 4, 6, 10, 18, we see that at generation 2ⁿ + 2 (n = 1, 2, 3, …) the automaton has roughly filled in a square of side length 2ⁿ⁺¹ + 1, and then evolution continues from there on out of the corners of that square. Also, the number of cells added (A187211) at these generations can now easily be computed:

A187211(2ⁿ + 2) = 16 + 8(2^n-1 – 1) for n ≥ 3.

Furthermore, the growth in the following generations repeats itself. In particular, we have:

A187211(2ⁿ + 3) = 22 for n ≥ 1,
A187211(2ⁿ + 4) = 40 for n ≥ 2,
A187211(2ⁿ + 5) = 54 for n ≥ 2.

Similarly, for n ≥ 3, the four values of A187211(2ⁿ + 6) through A187211(2ⁿ + 9) are similarly constant (their values are 56, 70, 120, and 134). In general, for n ≥ k the 2^k-1 values of A187211(2ⁿ + 2^k-1 + 2) through A187211(2ⁿ + 2^k + 1) are constant in n, though I am not aware of a general formula for what these constants are. If we ignore the first four generations and arrange the number of Q-toothpicks added in each generation in rows of length 2ⁿ, we obtain a table that begins as follows:

22, 20
22, 40, 54, 40
22, 40, 54, 56, 70, 120, 134, 72
22, 40, 54, 56, 70, 120, 134, 88, 70, 120, 150, 168, 246, 360, 326, 136

C scripts are provided at the end of this post for computing the values of A187210 and A187211 (and hence the values in the above table).

Shapes Traced Out by Q-Toothpicks

By far the most common of these shapes are circles, diamonds and hearts. The fourth shape appears only on the diagonal and it’s not difficult to see that it forever will make up the entirety of the diagonal (with the exception of the circle in the center). The fifth and sixth objects are the first two members of an infinite family of objects that appear as the automaton evolves. The fifth object first appears in generation 9, and sixth object (which is basically two copies of the fifth object) first appears in generation 17. The following object, which is basically made up of two copies of the sixth object (i.e., four copies of the fifth object) first appears in generation 33:

In general, a new object of this type (made of 2ⁿ copies of the fifth object above) first appears in generation 2ⁿ⁺³ + 1. In fact, these objects are the only ones that are traced out by this automaton. [Edit: this final claim is not true! See ebcube’s great post that shows a double-heart shape in generation 31.]

Update [March 28, 2011]: I have added a script that counts the number of circles, diamonds, and hearts in the nth generation of the Q-toothpick automaton, and another script that computes Sloane’s A187212.

Download:

• A187210.c – computes the total number of Q-toothpicks present in the nth generation
• A187211.c – computes the number of Q-toothpicks added in the nth generation
• A187212.c – computes the number of Q-toothpicks if we restrict them to the positive quadrant
• count_shapes.c – computes the number of circles, diamonds, and hearts in the nth generation

Entanglement is a browser-based game that has gained a fair bit of popularity lately due to its recent inclusion in Google’s Chrome Web Store and Chrome 9. The way the game works is probably best understood by actually playing it, but here is my brief attempt:

• You are given a hexagonal tile with six paths printed on it, with two path ends touching each side of the hexagon. One such tile is as follows:

• You may rotate, but not move the hexagon that has been provided to you.
• Once you have selected an orientation of the hexagon, a path is traced along that hexagon, and you are provided a new hexagon that you may rotate at the end of your current path.
• The goal of the game is to create the longest path possible without running into either the centre hexagon or the outer edge of the game board.

To make things a bit more interesting, the game was updated in November 2010 to include a new scoring system that gives you 1 + 2 + 3 + … + n (the nth triangular number) points on a turn if you extend the length of your path by n on that turn. This encourages clever moves that significantly extend the length of the path all at once. The question that I am going to answer today is what the maximum score in Entanglement is under this scoring system (inspired by this reddit thread).

On a Standard-Size Game Board

The standard Entanglement game board is made up of a hexagonal ring of 6 hexagons, surrounded by a hexagonal ring of 12 hexagons, surrounded by a hexagonal ring of 18 hexagons, for a total of 36 hexagons. In order to maximize our score, we want to maximize how much we increase the length of our path on our final move. Thus, we want to just extend our path by a length of one on each of our first 35 moves, and then score big on the 36th move.

Well, each hexagon that we lay has six paths on it, for a total of 6*36 = 216 paths on the board. 35 of those paths will be used up by our first 35 moves. It is not possible to use all of the remaining 181 paths, however, because many of them lead into the edge of the game board or the central hexagon, and connecting to such a path immediately ends the game. Because there are 12 path ends that touch the central hexagon and 84 path ends that touch the outer border, there must be at least (12+84)/2 – 1 = 47 unused paths on the game board (we divided by 2 because each unused path takes up two path ends and we subtracted 1 because one of the paths will be used by us).

Thus we can add a length of at most 181 – 47 = 134 to our path on the 36th and final move of the game, giving a total score of at most 35 (from the first 35 moves of the game) + 1 + 2 + 3 + … + 134 = 35 + 9045 = 9080. Not only is this an upper bound of the possible scores, but it is actually attainable, as demonstrated by the following optimal game board:

Paths in red are unused, the green line depicts the portion of the path laid by the first 35 moves of the game, and the blue line depicts the portion of the path (of length 134) gained on the 36th move. One fun property of the above game board is that it is actually completely “unentangled” – no paths cross over any other paths.

On a Larger or Smaller Game Board

Other than being a good size for playability purposes, there is no reason why we couldn’t play Entanglement on a game board of larger or smaller radius (by radius I mean the number of rings of hexagons around the central hexagon – the standard game board has a radius of 3). We will compute the maximum score simply by mimicking our previous analysis for the standard game board. If the board has radius n, then there are 6 + 12 + 18 + … + 6n = 3n(n+1) hexagons, each of which contains 6 paths. Thus there are 18n(n+1) lengths of path, 3n(n+1)-1 of which are used in the first 3n(n+1)-1 moves of the game, and we want to add as many as possible of the remaining 15n(n+1)+1 lengths of path in the final move of the game. There are 12 path ends that touch the central hexagon and 12 + 24n path ends that touch the outer edge of the game board. Thus there are at least (12 + 12 + 24n)/2 – 1 = 11 + 12n unused paths on the game board.

Tallying the numbers up, we see that on the final move, we can add at most 15n(n+1)+1 – (11 + 12n) = 15n² + 3n – 10 lengths of path. If T(n) = n(n+1)/2 is the nth triangular number, then we see that it’s not possible to obtain more than 3n(n+1)-1 + T(15n² + 3n – 10) = (225/2)n⁴ + 45n³ – 135n² – (51/2)n + 44 points. In fact, this score is obtainable via the exact same construction as the optimal board in the n = 3 case – just extend the (counter)clockwise rotation of the path in the obvious way. Thus, the maximum score for a game of Entanglement on a board of radius n for n = 1, 2, 3, … is given by the sequence 41, 1613, 9080, 29462, 72479, … (A180667 in the OEIS).