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We have a multiset $S$ of size $t$ with $r$ distinct elements, where $t$ is much larger than $r$. We want to reconstruct an ordering $s_1, s_2, ... s_t$ of the elements of $S$ given the values of $t$ and $r$ and, for some fixed positive integer $j$, the set of multisets $\{ s_k, s_{k+1}, ... s_{k+j-1} \}$ for $1 \le k \le t-j+1$. (Note that we are not given either the order of elements in each multiset or the order of the multisets.) We also know $s_1, ... s_{\lfloor pt \rfloor}$ for some fixed $0 < p < 1$.

For what values of $t, r, j, p$ is it possible to reconstruct the entire ordering $s_1, ... s_t$? For those values, what is the average- or worst-case complexity of doing so?


Edit (Qiaochu Yuan): Here's a restatement of the problem in the language Steve Huntsman is using. The generalized de Brujin graph $B(r, j)$ (it does not seem to have a standard name) has vertices the set of all words of length $j$ from an alphabet of size $r$ and edges defined as follows: the word $w_1, ... w_j$ has an edge directed to the word $w_2 ... w_j w_{j+1}$ for all possible choices of $w_{j+1}$. There is a natural equivalence relation on words where two words are equivalent if the same letters occur in them with the same frequency. What we are trying to do is reconstruct a walk on $B(r, j)$ of length $t - j + 1$ given only the set of equivalence classes of its vertices, and also given an initial segment of the walk which is some fixed proportion of the entire walk.

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Rob, there are some things about your question I don't understand. When you write "[0,1,...,R]", what do the square brackets mean? When you say that the ordered set S can be represented as a binary string, what do you mean? I wonder whether you're using the term "ordered set" in the usual mathematical sense, as for instance in en.wikipedia.org/wiki/Order_theory . If not, some clarification would be good. –  Tom Leinster Jan 18 '10 at 21:14
    
Dear Tom, the square brackets have no special meaning, I was using them because jsMath was making '{}' brackets disappear (I'll fix it because it's caused concern) - for [0,1,...,R] I'm simply referring to a set with up to 'R' unique elements in it. Also, I meant 'ordered set $S$' in a loose computer-science sense (again, sloppy) - I should say 'permutation of a set $S$'. The idea is that with two unique elements in $S$, a binary string with a length equal to the cardinality of $S$ would represent a particular permutation. –  Rob Grey Jan 18 '10 at 21:45
    
Rob, I'm still confused. How are you writing permutations as binary strings? –  Qiaochu Yuan Jan 18 '10 at 22:03
    
Qiaochu, I was talking about representing a unique permutation of two elements as a binary string (and only talking about binary strings as an illustrative example), not trying to enumerate permutations or list permutations. My objective here is to try to recover a unique permutation in S with the scrambled subsets. Does that make more sense to you, or should I try to rewrite the question? –  Rob Grey Jan 18 '10 at 22:09
    
Your notation is very confusing. What does R = 2 mean if there are T elements in your set? –  Qiaochu Yuan Jan 18 '10 at 22:11

6 Answers 6

up vote 2 down vote accepted

I have some unpublished notes that I think are relevant to this problem, and I am cherry-picking from them. (See also here for a related question.) Because the notes are unpublished I hope it will not be considered poor form to provide a rather lengthy response.

To save myself trouble while picking the relevant parts from them, I will write $q$ in place of $r$ and $k$ in place of $j$. Also, let $B = q^k$ and let $N$ be the length of a generic cyclic word (or necklace) over a $q$-ary alphabet $\mathcal{A}_q$.

As Gerhard suggests, the problem can be recast in terms of necklaces and their subwords of length $k$. We introduce the appellation dimgraph for “directed multigraph”. Let $[w]_k$ denote the generalized order $(k – 1)$ de Bruijn dimgraph of the $q$-ary word $w$: i.e., vertices correspond to the $(k – 1)$-tuples, and directed edges correspond to the $k$-tuples in $w$ in the usual way. If $[w′]_k = [w]_k$, we say that $w$ and $w′$ belong to the same order $k$ de Bruijn homology class. It is easy to see that this really is an equivalence relation, which we denote $\sim_k$ (suppressing subscripts when there is no risk of confusion). If the size of an equivalence class is greater than unity, it will be necessary to take into account the additional data associated to $p$, but I will not touch on that (or on the formula to determine the multiplicity of a nontrivial equivalence class, though if anyone wants this please leave a comment and I will try to reply with a PDF) here.

For convenience, assume first that $q = 2$ and define the frequency tuple $\alpha = \alpha(w)$ as the $B$-tuple of natural numbers indicating the frequency of occurrence in $w$ of the (lexicographically ordered) $k$-tuples over $\mathcal{A}_q$. For later convenience we take the frequency tuple to be an integral column vector. For example,

$w = 00011 \in \mathcal{A}_2^5, k = 3 \Rightarrow \alpha(w) = (\alpha_{000},\dots,\alpha_{111})^T \equiv (\alpha_0,\dots,\alpha_7)^T = (1,1,0,1,1,0,1,0)$.

Notice that we start the index at zero: this will be our default convention for all indices relating to $k$-tuples, and context should generally suffice to determine the indexing scheme for a particular object.

The Eulerian property of generalized de Bruijn graphs means that $\alpha$ is completely specified by the $B/2 + 1$ components $\alpha_0,\alpha_1,\dots,\alpha_{B/2-1},\alpha_{B-1}$: conversely, any such assignment completely specifies the corresponding generalized de Bruijn dimgraph—though notice here that we make no implicit requirement that such a dimgraph must as a matter of course be strongly connected (i.e., correspond to a word). We could make other choices for the “free” components: the essential thing is that the two “loop” components $\alpha_0$ and $\alpha_{B-1}$ must be free, and any other $B/2 – 1$ components will suffice to complete a specification. The reason for this is basically that the Eulerian property (besides its requirement of strong connectivity) provides $B/2$ independent equations in $B$ unknowns.

More explicitly, for k > 1, write the frequency tuple as a column vector:

$\alpha = (\alpha_0,\alpha_{[0]}^{(k)},\alpha_{[1]}^{(k)},\alpha_{B-1})^T; \quad \alpha_{[0]}^{(k)} = (\alpha_1,\dots,\alpha_{B/2-1})^T, \quad \alpha_{[1]}^{(k)} = (\alpha_{B/2},\dots,\alpha_{B-2})^T$.

Now there is an integral matrix—actually there are many integral matrices—satisfying

$\alpha_{[1]}^{(k)} = M_{[0 \rightarrow 1]}^{(k)} \alpha_{[1]}^{(k)}$.

There is an algorithm for computing these matrices for arbitrary $k$: it is just an exercise in index-juggling, however, so we omit the details. The point is that we can dispense with the unnecessary components of frequency tuples for the binary case, although some care must be taken. Also, it turns out to simplify matters considerably if we omit the words of all zeros and all ones from consideration: we will do so without comment when convenient. More generally, it is convenient to require that none of the components of the frequency tuple are zero (which also implies that none of them equal $N$), but we shall be explicit when making such an assumption.

We can treat some of the components of a frequency tuple in the manner just outlined as functions into the natural numbers. In turn, the matrix-tree and BEST theorems can be jointly recast as a recipe for another function—the BEST function—from this space into the natural numbers that gives the cardinality of the de Bruijn equivalence class.

...

With the binary case in hand, we seek to generalize to $k$-tuples over $\mathcal{A}_q$. The approach will be somewhat different here. Define ancestor and descendant matrices by

$M_\leftarrow := 1_{1 \times q} \otimes I_{B/q}, \quad M_\rightarrow := I_{B/q} \otimes 1_{1 \times q}$.

Now the indegree/outdegree (I/O) or weak Eulerian property reduces to

$M_\leftarrow \alpha = M_\rightarrow \alpha$.

Nothing is lost here by forming the augmented ancestor and descendant matrices

$\tilde M_\leftarrow := \delta_1 \otimes M_\leftarrow + I_B, \quad \tilde M_\rightarrow := \delta_1 \otimes M_\rightarrow + I_B$

and characterizing the I/O property instead by

$\tilde M_\leftarrow \alpha = \tilde M_\rightarrow \alpha$.

The augmented matrices are invertible, and so we can rewrite the I/O property once more as

$M_{(I/O)}\alpha \equiv \left( \tilde M_\rightarrow^{-1} \tilde M_\leftarrow -I \right)\alpha = 0$.

In other words, a proper frequency tuple must belong to the kernel of the consistency matrix on the LHS above. The dimension $d$ of this in/out (I/O) kernel $\ker_{I/O}(q,k)$ is just the number of independent variables required to determine a frequency tuple.

This number is $d = B – B/q + 1$. A sketch of a proof is as follows: of the $B$ edges (including any with multiplicity zero) in an order $(k – 1)$ generalized de Bruijn dimgraph, $q$ are loops: the corresponding frequency tuple components obviously need to be treated as free variables. For the remaining edges, we have B/q independent linear equations in $B – q$ variables. This gives a net of $B – B/q$ independent variables for a minimal integral solution to the linear equations. The +1 is accounted for by scaling.

Accordingly, if the word length is fixed to $N$ then evidently there are $B – B/q$ free components.

For small $q, k$ an exact/integral basis for the in/out kernel can be formed easily (e.g., by using the null command in MATLAB with the r option), and since these are the only cases we can realistically address, we will not bother with an algorithm.

We have two comments at this point. The first is that the reader should be convinced (or take it as an exercise to prove) that the I/O kernel contains an integer lattice whose primitive cells are relatively small (a precise formulation and attendant proof are probably rather technical, and certainly uninteresting). The second is that the lattice can be generated by a positive integral basis: the few negative entries above can clearly be made to go away with a few elementary linear operations.

That is, we consider the I/O lattice

$\Lambda_{I/O}(q,k) :- \ker_{I/O}(q,k) \cap \mathbb{Z}^B$

and the associated lattice cone

$\Lambda_{I/O}^{0+}(q,k) := \ker_{I/O}(q,k) \cap \mathbb{N}^B$.

Except for some degenerate cases corresponding to disconnected generalized de Bruijn dimgraphs (to be addressed in the sequel), points in this cone correspond to valid frequency tuples. For computational purposes we want to be able to efficiently generate the frequency tuple simplices

$\Lambda_{I/O}^{(N)}(q,k) := \Lambda_{I/O}^{0+}(q,k) \cap N \Delta_B$

where a dilation of the standard simplex is indicated on the RHS. (It will be an implicit corollary of our construction of these sets that the I/O lattice is unimodular, but we do not use this.)

We first define a bucketspace (“$s$ balls in $r$ buckets”) $X_{r,s} := \{\alpha \in \mathbb{N}^r : \sum_{j=1}^r \alpha_j = s\}$. Now it is clear that there exists a $B \times d$ integer matrix $M^{(\Lambda)}$ s.t.

$M^{(\Lambda)}X_{B,N} = \Lambda_{I/O}^{(N)}(q,k)$

(where the LHS is interpreted in the obvious way). In fact there may be several such integer matrices—each reflecting a particular choice of basis—but all we need is one. We will call it a bucketspace-I/O lattice matrix.

The problem of its construction appears subtle (but perhaps that is because we have focused on explicit examples). A useful heuristic is to invoke the LLL lattice basis reduction algorithm on an integer basis for the I/O kernel, then use the structure of the de Bruijn graph—in particular, a table of its cycles—to improve on LLL. Note that the cycle enumeration for de Bruijn graphs has only been effected for small values of $k$.

In practice there are some subtle considerations, which we sketch in the context of the two specific cases $q = 2$ and $k = 3, 4$. Since the I/O kernel is the span of the dimgraph cycle space (see the sequel), it makes sense to look for a set of cycles corresponding to a “nice” basis. A natural candidate is a reduced basis of positive lattice vectors with minimal length.

Once the simple cycles of the de Bruijn graph are enumerated, it is easy to produce such a positive basis with a greedy algorithm. Start with the cycle representatives as a proto-basis. Now pick one of the remaining simple cycles of minimal weight uniformly at random: if it is linearly independent of the existing proto-basis, add it. Keep going until a basis is formed.

For $q = 2, k = 3$ the 6 simple cycles and corresponding simple cycle vectors are

$(0) \leftrightarrow (1,0,0,0,0,0,0,0)^T$

$(001) \leftrightarrow (0,1,1,0,1,0,0,0)^T$

$(0011) \leftrightarrow (0,1,0,1,1,0,1,0)^T$

$(01) \leftrightarrow (0,0,1,0,0,1,0,0)^T$

$(011) \leftrightarrow (0,0,0,1,0,1,1,0)^T$

$(1) \leftrightarrow (0,0,0,0,0,0,0,1)^T$

and the greedy algorithm produces the unique minimal positive basis

$\{(0),(001),(01),(011),(1)\}$.

Similarly for $q=2, k=4$ we have the 19 simple cycles

$(0),(0001),(0001011),(00010111),(00011),(0001101),(000111),(00011101),(001)$, $(001011),(0010111),(0011),(001101),(00111),(0011101),(01),(011),(0111),(1)$

and the greedy algorithm produces either of the two minimal positive bases

$\{(0),(0001),(001),(001011),(0011),(01),(011),(0111),(1)\}$

$\{(0),(0001),(001),(0011),(001101),(01),(011),(0111),(1)\}$

Note that the simple vectors of length 5 are rejected by the greedy algorithm, as is the simple vector $(000111)$ of weight 6.

(Notice that Gordan’s lemma implies that the monoid [commutative semigroup with identity and cancellation] of integral points in a lattice cone is finitely generated. This in turn implies that we can use one of the minimal positive bases to produce frequency tuple simplices if we are willing to pay a large overhead factor over a more efficient [if less well-understood] method outlined below.)

By performing elementary column operations it is possible to find a workable bucketspace-I/O lattice matrix by trial and error (of course an algorithm would be preferable, but the cycle decomposition of de Bruijn graphs is known only for small values of $k$, suggesting that such a goal is too lofty). We find by direct experiment with $N = 16$ that the following matrices work:

alt text

The columns of the $k = 4$ matrix can be indicated graphically:

alt text

Finally, notice that the method outlined essentially boils down to finding the vertices of a frequency tuple simplex—but this appears to be more easily said than done.

...

Now that the I/O condition has been suitably developed, we move on to the question of (strong) connectivity. Let $B$ (with an abusive resue of notation) denote the incidence matrix of a dimgraph $G$. Without bothering to restate the common definition, we simple provide a relevant example: for $q = 2$ and $k = 4$ we have

alt text

Notice by way of passing that we can recast the I/O equations as

$B1 = 0$.

It can be shown that $\ker B$ is the cycle space of $G$.

In any event it is a standard result for dimgraphs that

$\mbox{rank}(B(G)) = |V(G)| - c(G)$

where on the RHS we indicate the number of vertices minus the number of components. In short, the rank of the incidence matrix allows us to judge whether or not the corresponding dimgraph is (strongly) connected.

Our intent here is that a generalized de Bruijn dimgraph has only a single component by definition: although we have not really produced a proper mathematical definition, this intent should suffice for the remainder. Now the requirement on a frequency tuple is that

$|V(\alpha)| - \mbox{rank}(B(\alpha)) = 1$.

The number of vertices is just the number of $(k – 1)$-tuples making an appearance; the rank can be computed by Gaussian elimination. For $q = 2$, it can quickly be seen that the rank is just the number of nonzero components in $(\alpha_1,\dots,\alpha_{B/2-1})$.

With that in hand, define the tuple canonical ensemble (TCE) by

$\hat \Lambda_{I/O}^{(N)}(q,k) := \Lambda_{I/O}^{(N)}(q,k) \cap \{\alpha: |V(\alpha)|-\mbox{rank}(B(\alpha)) = 1\}$.

The TCE gives all the frequency tuples for a given length, and no garbage points, although some care is required in its construction.

In conclusion: check to see if a given point is in the TCE. If it is, then use the matrix-tree and BEST theorems (again, ask me how and I'll provide a PDF if I see the comment) to determine its cardinality. Providing an enumeration and selecting $p$ minimal completes the solution to the problem, although I'm not sure about the value of $p$, complexity, etc.

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added some matrix-tree/BEST details in a separate answer. –  Steve Huntsman Jan 19 '10 at 5:25
    
Steve, thanks so much for your notes! It's going to take me a little bit to go through and understand the argument. I should mention however that I'm most interested in the case where the permutation of the multiset S is a long random sequence rather than a debruijn sequence... –  Rob Grey Jan 19 '10 at 12:44
    
The sequences in question are arbitrary. I use the term "generalized de Bruijn" to emphasize the connection between sequences and Eulerian paths on certain associated graphs, etc. –  Steve Huntsman Jan 19 '10 at 13:35
    
Steve, got it, thanks that makes sense after more carefully reading your post. –  Rob Grey Jan 19 '10 at 15:27

(First, apologies to the administrators for not yet registering.)

If you consider S to be a debruijn sequence (a short sequence where each pair of letters from an alphabet occurs as a contiguous subword, e.g. 0120210), you will have a high amount of symmetry in the information given, and not be able to distinguish which sequence to reconstruct, even if you are told that it is a debruijn sequence. (You might be able to if you were given a long enough initial segment of the sequence.) Also, you may not be able to distinguish the string from its reverse, given only the information you list above. So I think a unique reconstruction will be impossible.

I am looking at a similar problem where I want to compress a long list of numbers by looking at all sublists of length j for small j. Here I have your information plus the ordering on all subsets of length j, but I have not found a way to reconstruct the list because for each length (j-1) prefix, I have several choices to complete the length j list. It seems I will need large j to do the reconstruction uniquely, which will defeat the intent of compressing the sequence.

Gerhard "Ask Me About System Design" Paseman, 2010.01.18

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Gerhard, I'm imagining very long sequences, where the cardinality of $S$ is in the hundreds to thousands. Also, I should add a note that one is allowed some short initial stretch of the permutation in $S$. –  Rob Grey Jan 18 '10 at 22:04
    
You have an interesting problem, I will think about it. –  Rob Grey Jan 18 '10 at 22:04
    
To compress the list, save the adjacency matrix of the induced generalized de Bruijn graph and a number indicating which of the members of the generalized equivalence class it corresponds to. I have some notes from 2003 on this in the context of Kolmogorov complexity and its applications to phylogenetic analysis using mitochondrial DNA if you're interested. –  Steve Huntsman Jan 19 '10 at 5:00

Generally speaking, I think these sequences should be non-reconstructible, at least when $j \leq r$.

For instance, consider the following two sequences:

01230123012301

01320132013201

These are indistinguishable (when j = 4) if we only look at the multisets and don't know the first few elements of the sequence. However, we can get around this problem for some fixed $p > 0$ by appending each of these sequences to some other sequence s.t. the initial fixed sequence has length $pt$. The only difference is that the first one has a consecutive subsequence of the form "x012" where the other one has one of the form "x013"; this can be remedied by appending an "x" to the end of both sequences.

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I'm just about to walk out the door, but (quickly) I believe it's true that with P(1/r^j) - i.e. where $r$ the number of unique elements in the multiset and $j$ is the size of any subset - we can create a non-uniquely reconstructable sequence. I'm interested in the limit where P(1/r^j) << 1. –  Rob Grey Jan 19 '10 at 3:50
    
Ah, and thanks! –  Rob Grey Jan 19 '10 at 3:53
    
The necklace ATAGTC contains the 2-lets AT, TA, AG, GT, and TC. There is another necklace that has the same initial and terminal singlets and the same multiset of doublets: namely, AGTATC. –  Steve Huntsman Jan 19 '10 at 14:37

No "palindromic sandwich" is reconstructible. By this I mean an ordering of the form $aba'$ where $a'$ is $a$ reversed and $a$ is at least the length of the peek of the initial segment you get. This ordering cannot be distinguished from $ab'a'$ regardless of the value of $j$; in other words, the problem cannot be solved when $p < \frac{1}{2}$. Perhaps you want instead an algorithm that generates all possible orderings?

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Dear Qiaochu, I don't expect every sequence to be perfectly reconstructible given arbitrary computational resources and you make a good point about the palindromic sandwiches. "Perhaps you want instead an algorithm that generates all possible orderings?" - Yes, it would be terrific to have an efficient algorithm to enumerate all 'legal' orderings so that probabilities could be assigned to them, or at least to see the growth in the number of orderings as a function of (t, r, j, p). –  Rob Grey Jan 19 '10 at 15:34
    
And please note that I still need to take time to think about Steve Huntsman's answer. –  Rob Grey Jan 19 '10 at 15:37

Dealing with the cardinality of the equivalence classes naturally fits into another answer, which I'm covering here.

We begin with the binary case. The first step in this direction is to notice that the adjacency matrix of a generalized binary de Bruijn graph has a very simple structure:

$A_{ij}(\alpha) = \alpha_{2i} \delta_{2i \mod (B/q),j} + \alpha_{2i+1} \delta_{(2i+1) \mod (B/q),j}$

The corresponding matrix/combinatorial Laplacian is prepared from the recipe

$\mathcal{L}(A) := \Delta(A1) - A$

where $\Delta$ denotes any of the various diagonal operations (context should suffice to determine which). The matrix-tree theorem gives that the number of directed spanning trees is any principal minor (they are all equal):

$t(A) = \hat{\mathcal{L}}_{i,i}(A), \quad \forall i$.

It does not appear to be worth working out an explicit formula for such a determinant for $k$ generic, even with $q = 2$. A key argument supporting this pessimism is that any finite Eulerian (hence strongly connected) dimgraph can be embedded in some generalized de Bruijn graph (just take an Euler trail to see this). In the example $q = 2$, $k = 4$ we have

$\hat{\mathcal{L}}_{B,B}(\alpha) = \alpha_1 \alpha_7 (\alpha_8 + \alpha_9)(\alpha_{12} + \alpha_{13})(\alpha_2 \alpha_5 \alpha_{11} + \alpha_3 \alpha_4 \alpha_{10} + \alpha_3 \alpha_4 \alpha_{11} + \alpha_3 \alpha_5 \alpha_{11})$

and this takes several pages to derive by hand using expansion by minors. It is not hard to see that there is no way to perform row or column permutations to cast the matrix into block diagonal form (except in degenerate cases), and in the minor expansion enough terms will probably crop up to yield (at best) a Pyrrhic victory. For special cases where a large set of terms possessing symmetries might be set to zero, searching for a formula might be fruitful: however, we will not pursue any such line here.

The BEST theorem states that the number of unlabeled Euler circuits in a de Bruijn equivalence class is given by the BEST function

$f_{BEST}(\alpha) := t(\alpha) \cdot \prod_{i=0}^{B/q-1} (\deg_i(\alpha) - 1)!$

where the vertex in- or out-degrees (they are the same) are indicated. Evaluating this function quickly becomes quite demanding as the number of evaluations and $k$ increase. The factorial terms can only add to any computational demands this function makes.

Actually, things get even worse: the unlabeled Euler circuits enumerated by the BEST function are in a many-to-one correspondence with necklaces. The correspondence is nontrivial for periodic necklaces, so dividing the BEST function by the obvious product of factorials does not quite work. The proper formula for the necklace function is (as J. Jonsson pointed out on sci.math.research)

$f_{neck}(\alpha) := \sum_{d|\gcd \alpha}\frac{\phi(d) f_{BEST}(\alpha/d)}{d\cdot(\alpha/d)!}$

where the Euler phi function is indicated and the functions on tuples are defined in the obvious (componentwise) way. In lieu of a detailed proof we merely offer a brief explanation: for each term in the summation, the $f_{BEST}(\alpha/d)$ contribution gives the number of labeled “divisor-Euler” circuits with the commensurate length; the factorial terms account for removing the labels. The $d$ term in the denominator accounts for the concatenation of these “divisor-Euler” circuits into an actual Euler circuit. Finally, the phi function accounts for inequivalent shifts amongst these various “divisor-Euler” circuits.

We can move on from here to get the q-ary BEST and necklace functions. The adjacency matrix of a generalized q-ary de Bruijn graph is not much different than in the binary case:

$A_{ij} = \sum_{\ell=0}^{q-1} \alpha_{qi+\ell} \delta_{(qi+\ell) \mod (B/q), j}$.

It is an exercise in notation to show that the Laplacian is

$\hat{\mathcal{L}}_{ij}(\alpha) = \sum_{\ell = 0}^{q-1} \alpha_{qi+\ell}\left(\delta_{ij} - \delta_{(qi+\ell) \mod (B/q),j} \right)$

and the $q$-ary form of the necklace function has the same form as the binary version.

As an example, consider the case $q = 2$: it is easy to get that

$f_{BEST}(\alpha) = \frac{\alpha_1(\alpha_0 + \alpha_1)!(\alpha_1 + \alpha_3)!}{(\alpha_0 + \alpha_1)(\alpha_1 + \alpha_3)}$

and from this that

$f_{neck}(\alpha) = \frac{\alpha_1}{(\alpha_0 + \alpha_1)(\alpha_1 + \alpha_3)} \sum_{d|\gcd(\alpha_0, \alpha_1, \alpha_3)} \phi(d) \binom{\frac{\alpha_0 + \alpha_1}{d}}{\frac{\alpha_1}{d}}\binom{\frac{\alpha_1 + \alpha_3}{d}}{\frac{\alpha_1}{d}}$.

To take the example further, let us fix the word length at 16 and compute away. We compute nontrivial values of the necklace function over the $\alpha_0$-$\alpha_1$ plane for $0 \le \alpha_0 \le 14$ and $1 \le \alpha_1 \le 7$:

4   7   4                                               
22  56  75  56  22                                      
42  126 210 245 210 126 42                              
43  120 212 280 309 280 212 120 43                      
22  55  90  120 140 147 140 120 90  55  22              
7   12  17  20  23  24  25  24  23  20  17  12  7       
1   1   1   1   1   1   1   1   1   1   1   1   1   1   1

Notice also that the necklace function equals unity for $(\alpha_0, \alpha_1) = (0, 0)$, $(0, 8)$, and $(16, 0)$.

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I don't think that any p<1 is large enough to work for all t. The hardest case to reconstruct is 2 distinct elements (if r>2, there is nothing to stop you from using only 2 of them or putting the rest once each in the front). Even if you have the first pt terms there are $2^{(1-p)t}$ ways to finish. There are only j+1 multisets of size j so the information you get back is just an ordered list of j+1 integers which add to t-j. These number about $\binom{t}{j}$ which is less that $t^j$ which will be outstripped by any exponential. But maybe I don't understand, what about sequences which are all 0 with a single 1 out past the initial part you are given (but at least j away from the end)? I don't think that even having the multiset of ordered j-substrings would be much help there.

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