This is sequence A054261

The \$n\$th prime containment number is the lowest number which contains the first \$n\$ prime numbers as substrings. For example, the number \$235\$ is the lowest number which contains the first 3 primes as substrings, making it the 3rd prime containment number.

It is trivial to figure out that the first four prime containment numbers are \$2\$, \$23\$, \$235\$ and \$2357\$, but then it gets more interesting. Since the next prime is 11, the next prime containment number is not \$235711\$, but it is \$112357\$ since it's defined as the smallest number with the property.

However, the real challenge comes when you go beyond 11. The next prime containment number is \$113257\$. Note that in this number, the substrings 11 and 13 are overlapping. The number 3 is also overlapping with the number 13.

It is easy to prove that this sequence is increasing, since the next number needs to fulfill all criteria of the number before it, and have one more substring. However, the sequence is not strictly increasing, as is shown by the results for n=10 and n=11.

Challenge

Your goal is to find as many prime containment numbers as possible. Your program should output them in an ordered fashion, starting with 2 and going up.

Rules

  1. You are allowed to hard-code prime numbers.
  2. You are not allowed to hard-code prime containment numbers (2 is the only exception), or any magic numbers which makes the challenge trivial. Please be nice.
  3. You may use any language you wish. Please include a list of commands to get the environment ready for executing the code.
  4. You are free to use both CPU and GPU, and you may use multithreading.

Scoring

The official scoring will be from my laptop (dell XPS 9560). Your goal is to generate as many prime containment numbers as possible within 5 minutes.

Specs

  • 2.8GHz Intel Core i7-7700HQ (3.8GHz boost) 4 cores, 8 threads.
  • 16GB 2400MHz DDR4 RAM
  • NVIDIA GTX 1050
  • Linux Mint 18.3 64-bit

The numbers found so far, along with the last prime added to the number:

 1 =>                                                       2 (  2)
 2 =>                                                      23 (  3)
 3 =>                                                     235 (  5)
 4 =>                                                    2357 (  7)
 5 =>                                                  112357 ( 11)
 6 =>                                                  113257 ( 13)
 7 =>                                                 1131725 ( 17)
 8 =>                                               113171925 ( 19)
 9 =>                                              1131719235 ( 23)
10 =>                                            113171923295 ( 29)
11 =>                                            113171923295 ( 31)
12 =>                                           1131719237295 ( 37)
13 =>                                          11317237294195 ( 41)
14 =>                                        1131723294194375 ( 43)
15 =>                                      113172329419437475 ( 47)
16 =>                                     1131723294194347537 ( 53)
17 =>                                   113172329419434753759 ( 59)
18 =>                                  2311329417434753759619 ( 61)
19 =>                                231132941743475375961967 ( 67)
20 =>                               2311294134347175375961967 ( 71)
21 =>                              23112941343471735375961967 ( 73)
22 =>                             231129413434717353759619679 ( 79)
23 =>                           23112941343471735359619678379 ( 83)
24 =>                         2311294134347173535961967837989 ( 89)
25 =>                        23112941343471735359619678378979 ( 97)
26 =>                      2310112941343471735359619678378979 (101)
27 =>                    231010329411343471735359619678378979 (103)
28 =>                 101031071132329417343475359619678378979 (107)
29 =>              101031071091132329417343475359619678378979 (109)
30 =>              101031071091132329417343475359619678378979 (113)
31 =>           101031071091131272329417343475359619678378979 (127)
32 =>           101031071091131272329417343475359619678378979 (131)
33 =>         10103107109113127137232941734347535961967838979 (137)
34 =>      10103107109113127137139232941734347535961967838979 (139)
35 =>   10103107109113127137139149232941734347535961967838979 (149)
36 => 1010310710911312713713914923294151734347535961967838979 (151)

Thanks to Ardnauld, Ourous, and japh for extending this list.

Note that n = 10 and n = 11 are the same number, since \$113171923295\$ is the lowest number which contains all numbers \$[2, 3, 5, 7, 11, 13, 17, 19, 23, 29]\$, but it also contains \$31\$.

For reference, you can use the fact that the original Python script I wrote to generate this list above calculates the first 12 terms in about 6 minutes.

Additional rules

After the first results have come in, I have realized that there's a good chance that top results can end up having the same score. In case if a tie, the winner will be the one with the shortest time to generate their result. If two or more answers produce their results equally fast, it will simply be a tied victory.

Final note

The 5 minute runtime is only put to ensure a fair scoring. I'd be very interested in seeing if we can push the OEIS sequence further (right now it contains 17 numbers). With Ourous' code, I have generated all numbers until n = 26, but I plan on letting the code run for a longer period of time.

Scoreboard

  1. Python 3 + Google OR-Tools: 169
  2. Scala: 137 (unofficial)
  3. Concorde TSP solver: 84 (unofficial)
  4. C++ (GCC) + x86 assembly: 62
  5. Clean: 25
  6. JavaScript (Node.js): 24
  • 1
    I recently switched to the nouveau driver instead of the nvidia driver due to horrible cpu throttling while using nvidia. If anyone submits a cuda boosted solution, I won't be able to test it right away, but I'll try to test it within a reasonable amount of time. – maxb Nov 30 at 7:54
  • regarding rule 2: what if instead of hardcoding n, we hardcode n-1 and start searching from there? :) – ngn Nov 30 at 10:01
  • @ngn I might have to specify a bit closer what's allowed. You are of course allowed to save the previous result, which makes finding n=11 trivial since you just have to verify that n=10 also satisfies the new condition. I'd also argue that hard-coding only helps until n=17, since no numbers are known beyond that point as far as I've been able to find out. – maxb Nov 30 at 10:05
  • i meant hardcoding [1,22,234,2356,112356,113256,1131724,113171924,1131719234,113171923294,113171923294,1131719237294] and starting a search from each – ngn Nov 30 at 10:08
  • 4
    As far as I can tell, this is just a special case of the shortest common superstrings problem, and that's already known to be NP-complete, so this is basically a case of avoiding inefficiency. – Neil Nov 30 at 11:28

JavaScript (Node.js), score 24 in 241 seconds

Results

  • \$a(1)\$ to \$a(21)\$ are solved in ~15 seconds on TIO
  • \$a(22)=231129413434717353759619679\$ was found in ~70 seconds on my laptop
  • \$a(23)=23112941343471735359619678379\$ was found in approximately 2 minutes and 40 seconds on my laptop
  • \$a(1)\$ to \$a(24)\$ was found in 241 seconds for the official scoring (edited by maxb)

Algorithm

This is a recursive search which tries all possible ways of merging numbers together, and eventually sort the resulting lists in lexicographical order when a leaf node is reached.

A merge is attempted on \$x\$ and \$y\$ if the first \$k\$ digits of \$x\$ equal the last \$k\$ digits of \$y\$, or the first \$k\$ digits of \$y\$ equal the last \$k\$ digits of \$x\$.

At the beginning of each iteration, any entry that can be found in another entry is removed from the list.

A significant speed-up was achieved by keeping track of visited nodes, so that we can abort early when different operations lead to the same list.

A small speed-up was achieved by updating and restoring the list when possible rather than generating a copy, as suggested by an anonymous user Neil.

Example

Below are the steps that are executed for \$n=7\$, with the primes \$[2,3,5,7,11,13,17]\$.

[]                        // start with an empty list
[ 2 ]                     // append 2
[ 2, 3 ]                  // append 3
[ 2, 3, 5 ]               // append 5
[ 2, 3, 5, 7 ]            // append 7
[ 2, 3, 5, 7, 11 ]        // append 11
[ 2, 3, 5, 7, 11, 13 ]    // append 13
[ 2, 5, 7, 11, 13 ]       // remove 3, which appears in 13
  [ 2, 5, 7, 113, 13 ]    //   try to merge 11 and 13 into 113
  [ 2, 5, 7, 113 ]        //   remove 13, which now appears in 113
  [ 2, 5, 7, 113, 17 ]    //   append 17
  [ 2, 5, 113, 17 ]       //   remove 7, which appears in 17
  --> leaf node: 1131725  //   new best result
[ 2, 5, 7, 11, 13, 17 ]   // append 17
[ 2, 5, 11, 13, 17 ]      // remove 7, which appears in 17
  [ 2, 5, 113, 13, 17 ]   //   try to merge 11 and 13 into 113
  [ 2, 5, 113, 17 ]       //   remove 13, which now appears in 113
                          //   abort because this node was already visited
                          //   (it was a leaf node anyway, so we don't save much here)
  [ 2, 5, 117, 13, 17 ]   //   try to merge 11 and 17 into 117
  [ 2, 5, 117, 13 ]       //   remove 17, which now appears in 117
  --> leaf node: 1171325  //   not better than the previous one
--> leaf node: 11131725   // not better than the previous one

Code

Try it online!

let f = n => {
  let visited = {},
      a, d, k, best, search;

  // build the list of primes, as strings
  for(a = [ '2' ], n--, k = 3; n; k++) {
    for(d = k; k % (d -= 2);) {}
    d == 1 && n-- && a.push(k + '');
  }

  best = a.join('');

  // recursive search function
  (search = (a, n = 0, r = []) => {
    let x, y, i, j, k, s;

    // remove all entries in r[] that can be found in another entry
    r = r.filter((p, i) => !r.some((q, j) => i != j && ~q.indexOf(p)));

    // abort early if this node was already visited
    if(visited[r]) {
      return;
    }

    // otherwise, mark it as visited
    visited[r] = 1;

    // walk through all distinct pairs (x, y) in r[]
    for(i = 0; i < r.length; i++) {
      for(j = i + 1; j < r.length; j++) {
        x = r[i];
        y = r[j];

        // try to merge x and y if:
        // 1) the first k digits of x equal the last k digits of y
        for(k = 1; x.slice(0, k) == y.slice(-k); k++) {
          r[i] = y + x.slice(k);
          search(a, n, r);
        }

        // or:
        // 2) the first k digits of y equal the last k digits of x
        for(k = 1; y.slice(0, k) == x.slice(-k); k++) {
          r[i] = x + y.slice(k);
          search(a, n, r);
        }
        r[i] = x;
      }
    }

    if(x = a[n]) {
      // there are other primes to process, so go on with the next one
      search(a, n + 1, [...r, x]);
    }
    else {
      // this is a leaf node: see if we've improved our current score
      s = r.join('');

      if(s.length <= best.length) {
        s = r.sort().join('');

        if(s.length < best.length || s < best) {
          best = s;
        }
      }
    }
  })(a);

  return best;
}
  • 2
    Nice job finding (18). – ouflak Nov 30 at 17:25
  • Great answer! I'm not an expert in JavaScript, but the algorithm seems to be along the lines of what was linked by Kevin Cruijssen. Nice explanation of the algorithm, it's easy to see that you'll find the minimum value. I haven't personally done benchmarking in JS, can I run it in my browser or is there another preferred way of doing it? – maxb Nov 30 at 21:31
  • @maxb I would not recommend to run this in a browser, as it's going to freeze it. It's intended to be run with Node.js (like it does on TIO). – Arnauld Nov 30 at 21:40

C++ (GCC) + x86 assembly, score 32 36 62 in 259 seconds (official)

Results computed so far. My computer runs out of memory after 65.

1 2
2 23
3 235
4 2357
5 112357
6 113257
7 1131725
8 113171925
9 1131719235
10 113171923295
11 113171923295
12 1131719237295
13 11317237294195
14 1131723294194375
15 113172329419437475
16 1131723294194347537
17 113172329419434753759
18 2311329417434753759619
19 231132941743475375961967
20 2311294134347175375961967
21 23112941343471735375961967
22 231129413434717353759619679
23 23112941343471735359619678379
24 2311294134347173535961967837989
25 23112941343471735359619678378979
26 2310112941343471735359619678378979
27 231010329411343471735359619678378979
28 101031071132329417343475359619678378979
29 101031071091132329417343475359619678378979
30 101031071091132329417343475359619678378979
31 101031071091131272329417343475359619678378979
32 101031071091131272329417343475359619678378979
33 10103107109113127137232941734347535961967838979
34 10103107109113127137139232941734347535961967838979
35 10103107109113127137139149232941734347535961967838979
36 1010310710911312713713914923294151734347535961967838979
37 1010310710911312713713914915157232941734347535961967838979
38 1010310710911312713713914915157163232941734347535961967838979
39 10103107109113127137139149151571631672329417343475359619798389
40 10103107109113127137139149151571631672329417343475359619798389
41 1010310710911312713713914915157163167173232941794347535961978389
42 101031071091131271371391491515716316717323294179434753596181978389
43 101031071091131271371391491515716316723294173434753596181917978389
44 101031071091131271371391491515716316717323294179434753596181919383897
45 10103107109113127137139149151571631671731792329418191934347535961978389
46 10103107109113127137139149151571631671731791819193232941974347535961998389
47 101031071091271313714915157163167173179181919321139232941974347535961998389
48 1010310710912713137149151571631671731791819193211392232941974347535961998389
49 1010310710912713137149151571631671731791819193211392232272941974347535961998389
50 10103107109127131371491515716316717317918191932113922322722941974347535961998389
51 101031071091271313714915157163167173179181919321139223322722941974347535961998389
52 101031071091271313714915157163167173179181919321139223322722923941974347535961998389
53 1010310710912713137149151571631671731791819193211392233227229239241974347535961998389
54 101031071091271313714915157163167173179211392233227229239241819193251974347535961998389
55 101031071091271313714915157163167173179211392233227229239241819193251972574347535961998389
56 101031071091271313714915157163167173179211392233227229239241819193251972572634347535961998389
57 101031071091271313714915157163167173179211392233227229239241819193251972572632694347535961998389
58 101031071091271313714915157163167173179211392233227229239241819193251972572632694347535961998389
59 1010310710912713137149151571631671731792113922332277229239241819193251972572632694347535961998389
60 101031071091271313714915157163167173211392233227722923924179251819193257263269281974347535961998389
61 1010310710912713137149151571631671732113922332277229239241792518191932572632692819728343475359619989
62 10103107109127131371491515716316717321139223322772293239241792518191932572632692819728343475359619989
63 1010307107109127131371491515716316717321139223322772293239241792518191932572632692819728343475359619989
64 10103071071091271311371391491515716316721173223322772293239241792518191932572632692819728343475359619989
65 10103071071091271311371491515716313916721173223322772293239241792518191932572632692819728343475359619989

These all agree with the output of the Concorde-based solver, so they stand a good chance of being correct.

Changelog:

  • Wrong calculation for needed context length. The earlier version was 1 too large, and also had a bug. Score: 32 34

  • Added equal-context-group optimisation. Score: 34 36

  • Overhauled the algorithm to use context-free strings properly, plus some other optimizations. Score: 36 62

  • Added a proper write-up.

  • Added the prime numbers variant.

How it works

Warning: this is a brain dump. Scroll to the end if you just want the code.

Abbreviations:

This program basically uses the textbook dynamic programming algorithm for the TSP.

  1. Plus a reduction from PCN/SCS, the problem we're actually solving, to TSP.
  2. Plus using item contexts instead of all digits in each item.
  3. Plus subdividing the problem based on primes that cannot overlap with the ends of other primes.
  4. Plus merging calculations for primes with the same start/end digits.
  5. Plus precomputed lookup tables and a custom hash table.
  6. Plus some low-level prefetching and bit-packing.

That's a lot of potential bugs. After playing around with anselm's entry and failing to coax any wrong results from it, I should at least prove that my overall approach is correct.

Although the Concorde-based solution is (much, much) faster, it is based on the same reduction, so this explanation applies to both. Additionally, this solution can be adapted for OEIS A054260, the prime-containing primes sequence; I don't know how to solve that efficiently in the TSP framework. So it's still somewhat relevant.

TSP reduction

Let's start by actually proving that reducing to TSP is correct. We have a set of strings, say

A = 13, 31, 37, 113, 137, 211

and we want to find the smallest superstring that contains these items.

Knowing the length is enough

For the PCN, if there are multiple shortest strings, we have to return the lexicographically smallest one. But we will look at a different (and easier) problem.

  • SCS: Given an initial prefix and a set of items, find any shortest string that contains all the items as substrings, and starts with that prefix.
  • SCS-Length: Just find the length of the SCS.

If we can solve SCS-Length, we can reconstruct the smallest solution and obtain the PCN. If we know that the smallest solution starts with our prefix, we try to extend it by appending each item, in lexicographic order, and solving for the length again. When we find the smallest item for which the solution length is the same, we know that this must be the next item in the smallest solution (why?), so add it and recurse on the remaining items. This method of reaching the solution is called self-reduction.

Touring the maximal-overlap graph

Suppose we started solving SCS for the above example by hand. We would probably:

  • Get rid of 13 and 37, because they're already substrings of the other items. Any solution that contains 137, for example, must also contain 13 and 37.
  • Start considering the combinations 113,137 → 1137, 211,113 → 2113, etc.

This is in fact the right thing to do, but let's prove it for the sake of completeness. Take any SCS solution; for example, a shortest superstring for A is

2113137

and it can be decomposed into a concatenation of all items in A:

211
 113
   31
    137

(We ignore the redundant items 13, 37.) Observe that:

  1. The start and end positions of each item increase by at least 1.
  2. Each item is overlapped with the previous item to the greatest extent possible.

We will show that every shortest superstring can be decomposed this way:

  1. For every pair of adjacent items x,y, y starts and ends at later positions than x. If this is not true, then either x is a substring of y or vice versa. But we already removed all items which are substrings, so that cannot happen.

  2. Suppose adjacent items in the sequence have less-than-maximal overlap, e.g. 21113 instead of 2113. But that would make the extra 1 redundant. No later item needs the initial 1 (as in 21113), because it occurs earlier than 113, and all items that appear after 113 cannot start with a digit before 113 (see point 1). A similar argument prevents the last extra 1 (as in 21113) being used by any item before 211. But our shortest superstring, by definition, won't have redundant digits, so such non-maximal overlaps will not occur.

With these properties, we can convert any SCS problem into a TSP:

  1. Remove all items which are substrings of other items.
  2. Create a directed graph which has one vertex for each item.
  3. For each pair of items x, y, add an edge from x to y whose weight is the number of extra symbols added by appending y to x with maximal overlap. For example, we would add an edge from 211 to 113 with weight 1, because 2113 adds one more digit over 211. Repeat for the edge from y to x.
  4. Add a vertex for the initial prefix, and edges from it to all the other items.

Any path on this graph, from the initial prefix, corresponds to a maximal-overlap concatenation of all items on that path, and the total weight of the path equals the concatenated string length. Therefore, every lowest-weight tour, that visits all the items at least once, corresponds to a shortest superstring.

And that's the reduction from SCS (and SCS-Length) to TSP.

Dynamic programming algorithm

This is a classic algorithm, but we will modify it quite a bit, so here's a quick reminder.

(I've written this as an algorithm for SCS-Length instead of TSP. They're essentially equivalent, but the SCS vocabulary helps when we get to the SCS-specific optimizations.)

Call the set of input items A and the given prefix P. For every k-element subset S in A, and every element e of S, we calculate the length of the shortest string that starts with P, contains all of S, and ends with e. This involves storing a table from values of (S, e) to their SCS-Lengths.

When we get to each subset S, the table needs to already contain the results for S - {e} for all e in S. As the table can get quite large, I calculate the results for all k-element subsets, then k+1, etc. For this, we only need to store the results for k and k+1 at any one time. This reduces memory usage by a factor of roughly sqrt(|A|).

One more detail: instead of calculating the minimum SCS-Length, I actually calculate the maximum total overlap between the items. (To get the SCS-Length, just subtract the total overlap from the sum of the items' lengths.) Using overlaps helps some of the following optimizations.

[2.] Item contexts

A context is the longest suffix of an item that can overlap with following items. If our items are 113,211,311, then 11 is the context for 211 and 311. (It's also the prefix context for 113, which we'll look at in part [4.])

In the DP algorithm above, we kept track of SCS solutions that end with each item, but we don't actually care which item an SCS ends in. All we need to know is the context. Thus, for example, if two SCSs for the same set end in 23 and 43, any SCS that continues from one will also work for the other.

This is a significant optimization, because non-trivial primes end in only the digits 1 3 7 9. The four single-digit contexts 1,3,7,9 (plus the empty context) are in fact sufficient to compute the PCNs for primes up to 131.

[3.] Context-free items

Others have already pointed out that many primes begin with the digits 2,4,5,6,8, such as 23,29,41,43.... None of these can overlap with a previous prime (aside from 2 and 5, primes cannot end in these digits; 2 and 5 will already have been removed as redundant). In the code, these are referred to as context-free strings.

If our input has context-free items, every SCS solution can be split into blocks

<prefix>... 23... 29... 41... 43...

and the overlaps in each block are independent of the other blocks. We can shuffle the blocks or swap items between blocks that have the same context, without changing the SCS length.

Thus, we only need to keep track of the possible multisets of contexts, one for each block.

Full example: for the primes less than 100, we have 11 context-free items and their contexts:

23 29 41 43 47 53 59 61 67 83 89
 3  9  1  3  7  3  9  1  7  3  9

Our initial multiset context:

1 1 3 3 3 3 7 7 9 9 9

The code refers to these as combined contexts, or ccontexts. Then, we only need to consider subsets of the remaining items:

11 13 17 19 31 37 71 73 79 97

[4.] Context merging

Once we get to primes with 3 digits or more, there are more redundancies:

 101 151 181 191 ...
 107 127 157 167 197 ...
 109 149 1009 ...

These groups share the same starting and ending contexts (usually—it depends on which other primes are in the input), so they are indistinguishable when overlapping other items. We only care about overlaps, so we can treat primes in these equal-context groups as indistinguishable. Now our DP subsets are condensed into multisubsets

4 × 1_1
5 × 1_7
3 × 1_9

(This is also why the solver maximizes overlap length instead of minimizing SCS length: this optimization preserves overlap length.)

Summary: the high-level optimizations

Running with INFO debug output will print statistics like

solve: N=43, N_search=26, ccontext_size=18, #contexts=7, #eq_context_groups=16

This particular line is for the SCS-Length of the first 62 primes, 2 to 293.

  • After removing redundant items, we're left with 43 primes that are not substrings of each other.
  • There are 7 unique contexts: 1,3,7,11,13,27 plus the empty string.
  • 17 of the 43 primes are context-free: 43,47,53,59,61,89,211,223,227,229,241,251,257,263,269,281,283. These and the given prefix (in this case, empty string) form the basis of the initial combined context.
  • In the remaining 26 items (N_search), there are 16 nontrivial equal-context groups.

By exploiting these structures, the SCS-Length calculation only needs to check 8498336 (multiset, ccontext) combinations. Straightforward dynamic programming would take 43×2^43 > 3×10^14 steps, and brute forcing the permutations would take 6×10^52 steps. The program still needs to run SCS-Length several more times to reconstruct the PCN solution, but that doesn't take much longer.

[5., 6.] The low-level optimizations

Instead of doing string operations, the SCS-Length solver works with indices of items and contexts. I also precompute the overlap amount between each context and item pair.

The code initially used GCC's unordered_map, which seems to be a hash table with linked list buckets and prime hash sizes (i.e. expensive divisions). So I wrote my own hash table with linear probing and power-of-two sizes. This nets a 3× speedup and 3× reduction in memory.

Each table state consists of a multiset of items, a combined context and an overlap count. These are packed into 128-bit entries: 8 for the overlap count, 56 for the multiset (as a bitset with run-length encoding), and 64 for the ccontext (1-delimited RLE). Encoding and decoding the ccontext was the trickiest part and I ended up using the new PDEP instruction (it's so new, GCC doesn't have an intrinsic for it yet).

Finally, accessing a hash table is really slow when N gets large, because the table doesn't fit in cache any more. But the only reason we write to the hash table, is to update the best known overlap count for each state. The program splits this step off into a prefetch queue, and the inner loop prefetches each table lookup a few iterations before actually updating that slot. Another 2× speedup on my computer.

Bonus: further improvements

AKA How is Concorde so fast?

I don't know much about TSP algorithms, so here is a rough guess.

Concorde uses the branch-and-cut method to solve TSPs.

  • It encodes the TSP as an integer linear program
  • It uses linear programming methods, as well as initial heuristics, to obtain lower and upper bounds on the optimal tour distance
  • These bounds are then fed into a branch and bound recursive algorithm that searches for the optimal solution. Large parts of the search tree can be pruned, if the computed lower bound for a subtree exceeds a known upper bound
  • It also searches for cutting planes to tighten the LP relaxation and obtain better bounds. Typically, these cuts encode knowledge of the fact that the decision variables must be integers

Obvious ideas we could try:

  • Pruning in the SCS-Length solver, especially when reconstructing the PCN solution (at that point, we already know what the solution length is)
  • Deriving some easy-to-calculate lower bounds for SCS, that can be used to help pruning
  • Finding more symmetries or redundancies in the prime number distribution to exploit

However, the branch-and-cut combination is very powerful, so we might not be able to beat a state-of-the-art solver like Concorde, for large values of N.

Bonus bonus: the prime containment primes

Unlike the Concorde-based solution, this program can be modified to find the smallest containing primes (OEIS A054260). This involves three changes:

  1. When reconstructing the solution from SCS-Length, test whether the solution is also a prime number. If not, backtrack and try some other ordering. There are reasonably many prime numbers (\$1/\ln(n)\$ density), so this usually succeeds quickly. However, if the PCN happens to have a digit sum divisible by 3, then the PCN (and many of its sibling permutations) will be divisible by 3. To avoid getting stuck in this situation, we also…

  2. Modify the SCS-Length solver code to categorize solutions based on whether their digit sums are divisible by 3. This involves adding another entry, the digit sum mod 3, to each DP state. This greatly reduces the odds of the main solver getting stuck with non-prime permutations. This is the change that I couldn't work out how to translate to TSP. It can be encoded with ILP, but then I'd have to learn about this thing called “subtour inequality” and how to generate those.

  3. It may be that all of the shortest PCNs are divisible by 3. In that case, the smallest prime containment prime must be at least one digit longer than the PCN. If our SCS-Length solver detects this, the solution reconstruction code has the option to add one extra digit at any point in the process. It tries adding each possible digit 0..9 and each remaining item to the current solution prefix, in lexicographic order as before.

With these changes, I can obtain the solutions up to N=62. Except for 47, where the reconstruction code gets stuck and gives up after 1 million steps (I don't know why, yet). The prime containment primes are:

1 2
2 23
3 523
4 2357
5 112573
6 511327
7 1135217
8 1113251719
9 11171323519
10 113171952923
11 113171952923
12 11131951723729
13 11317237419529
14 1131723294375419
15 113172329541947437
16 1131723294195343747
17 1113172329419434753759
18 11231329417437475361959
19 231132941743475375967619
20 2311294134347175967619537
21 23112941343471735967619537
22 231129413434717359537679619
23 23112941343471735375961983679
24 11231294134347173535961967983789
25 23112941343471735359679837619789
26 2310112941343471735359619783789679
27 231010329411343471735359619678379897
28 101031071132329417343475359619798376789
29 101031071091132329417343475359619767898379
30 101031071091132329417343475359619767898379
31 1010310710911131272329417343475359619678979837
32 1010310710911131272329417343475359619678979837
33 10103107109113127137232941734347535978961967983
34 10103107109113127137139232941734347535961967838979
35 10103107109113127137139149232941734347535961976798389
36 1010310710911312713713914923294151734347535976198389679
37 1010310710911312713713914915157232941734347535967619798389
38 10103107109111312713713914915157163232941734347535967897961983
39 10103107109113127137139149151571631672329417343475961979838953
40 10103107109113127137139149151571631672329417343475961979838953
41 10103107109111312713713914915157163167173232941794347535976198983
42 1010310710911131271371391491515716316717323294179434761819535989783
43 1010310710911131271371391491515716316723294173434753596181917989783
44 101031071091131271371391491515716316717323294179434753836181919389597
45 10103107109113127137139149151571631671731792329418191934347538961975983
46 101031071091113127137139149151571631671731791819193232941974347535989836199
47 (failed)
48 1010310710912713137149151571631671731791819193211392232941974347895359836199
49 10103107109112713137149151571631671731791819193211392232272941974347619983535989
50 10103107109127131371491515716316717317918191932113922322722941974347595389836199
51 101031071091271313714915157163167173179181919321139223322722941974347595389619983
52 101031071091271313714915157163167173179181919321139223322722923941974347538361995989
53 10103107109112713137149151571631671731791819193211392233227229239241974347619983538959
54 101031071091271313714915157163167173179211392233227229239241819193251974347619953835989
55 1010310710911271313714915157163167173179211392233227229239241819193251974325747596199538983
56 101031071091271313714915157163167173179211392233227229239241819193251972572634347619959895383
57 101031071091271313714915157163167173179211392233227229239241819193251972572632694359538983619947
58 101031071091271313714915157163167173179211392233227229239241819193251972572632694359538983619947
59 1010310710912713137149151571631671731792113922332277229239241819193251972572632694347535983896199
60 1010310710911271313714915157163167173211392233227722923924179251819193257263269281974347535961998389
61 1010310710912713137149151571631671732113922332277229239241792518191932572632692819728343538947619959
62 10103107109127131371491515716316717321139223322772293239241792518191932572632692819728343534759896199

Code

Compile with

g++ -std=c++14 -O3 -march=native pcn.cpp -o pcn

For the prime number version, also link with GMPlib, e.g.

g++ -std=c++14 -O3 -march=native pcn-prime.cpp -o pcn-prime -lgmp -lgmpxx

This program uses the PDEP instruction, which is only available on recent (Haswell+) x86 processors. Both my computer and maxb's support it. If yours doesn't, the program will compile in a slow software version. A compile warning will be printed when this happens.

#include <cassert>
#include <cstdlib>
#include <cstring>
#include <iostream>
#include <vector>
#include <unordered_map>
#include <string>
#include <algorithm>
#include <array>

using namespace std;

void debug_dummy(...) {
}

#ifndef INFO
//#  define INFO(...) fprintf(stderr, __VA_ARGS__)
#  define INFO debug_dummy
#endif

#ifndef DEBUG
//#    define DEBUG(...) fprintf(stderr, __VA_ARGS__)
#  define DEBUG debug_dummy
#endif

bool is_prime(size_t n)
{
    for (size_t d = 2; d * d <= n; ++d) {
        if (n % d == 0) {
            return false;
        }
    }
    return true;
}

// bitset, works for up to 64 strings
using bitset_t = uint64_t;
const size_t bitset_bits = 64;

// Find position of n-th set bit of x
uint64_t bit_select(uint64_t x, size_t n) {
#ifdef __BMI2__
    // Bug: GCC doesn't seem to provide the _pdep_u64 intrinsic,
    // despite what its manual claims. Neither does Clang!
    //size_t r = _pdep_u64(ccontext_t(1) << new_context, ccontext1);
    size_t r;
    // NB: actual operand order is %2, %1 despite the intrinsic taking %1, %2
    asm ("pdep %2, %1, %0"
         : "=r" (r)
         : "r" (uint64_t(1) << n), "r" (x)
         );
    return __builtin_ctzll(r);
#else
#  warning "bit_select: no x86 BMI2 instruction set, falling back to slow code"
    size_t k = 0, m = 0;
    for (; m < 64; ++m) {
        if (x & (uint64_t(1) << m)) {
            if (k == n) {
                break;
            }
            ++k;
        }
    }
    return m;
#endif
}

#ifndef likely
#  define likely(x) __builtin_expect(x, 1)
#endif
#ifndef unlikely
#  define unlikely(x) __builtin_expect(x, 0)
#endif

// Return the shortest string that begins with a and ends with b
string join_strings(string a, string b) {
    for (size_t overlap = min(a.size(), b.size()); overlap > 0; --overlap) {
        if (a.substr(a.size() - overlap) == b.substr(0, overlap)) {
            return a + b.substr(overlap);
        }
    }
    return a + b;
}

vector <string> dedup_items(string context0, vector <string> items)
{
    vector <string> items2;
    for (size_t i = 0; i < items.size(); ++i) {
        bool dup = false;
        if (context0.find(items[i]) != string::npos) {
                dup = true;
        } else {
            for (size_t j = 0; j < items.size(); ++j) {
                if (items[i] == items[j]?
                    i > j
                        : items[j].find(items[i]) != string::npos) {
                    dup = true;
                    break;
                }
            }
        }
        if (!dup) {
            items2.push_back(items[i]);
        }
    }
    return items2;
}

// Table entry used in main solver
const size_t solver_max_item_set = bitset_bits - 8;
struct Solver_entry
{
    uint8_t score : 8;
    bitset_t items : solver_max_item_set;
    bitset_t context;

    Solver_entry()
    {
        score = 0xff;
        items = 0;
        context = 0;
    }
    bool is_empty() const {
        return score == 0xff;
    }
};

// Simple hash table to avoid stdlib overhead
struct Solver_table
{
    vector <Solver_entry> t;
    size_t t_bits;
    size_t size_;
    size_t num_probes_;

    Solver_table()
    {
        // 256 slots initially -- this needs to be not too small
        // so that the load factor formula in update_score works
        t_bits = 8;
        size_ = 0;
        num_probes_ = 0;
        resize(t_bits);
    }
    static size_t entry_hash(bitset_t items, bitset_t context)
    {
        uint64_t h = 0x3141592627182818ULL;
        // Add context first, since its bits are generally
        // less well distributed than items
        h += context;
        h ^= h >> 23;
        h *= 0x2127599bf4325c37ULL;
        h ^= h >> 47;
        h += items;
        h ^= h >> 23;
        h *= 0x2127599bf4325c37ULL;
        h ^= h >> 47;
        return h;
    }
    size_t probe_index(size_t hash) const {
        return hash & ((size_t(1) << t_bits) - 1);
    }
    void resize(size_t t2_bits)
    {
        assert (size_ < size_t(1) << t2_bits);
        vector <Solver_entry> t2(size_t(1) << t2_bits);
        for (auto entry: t) {
            if (!entry.is_empty()) {
                size_t h = entry_hash(entry.items, entry.context);
                size_t mask = (size_t(1) << t2_bits) - 1;
                size_t idx = h & mask;
                while (!t2[idx].is_empty()) {
                    idx = (idx + 1) & mask;
                    ++num_probes_;
                }
                t2[idx] = entry;
            }
        }
        t.swap(t2);
        t_bits = t2_bits;
    }
    uint8_t update_score(bitset_t items, bitset_t context, uint8_t score)
    {
        // Ensure we can insert a new item without resizing
        assert (size_ < t.size());

        size_t index = probe_index(entry_hash(items, context));
        size_t mask = (size_t(1) << t_bits) - 1;
        for (size_t p = 0; p < t.size(); ++p, index = (index + 1) & mask) {
            ++num_probes_;
            if (likely(t[index].items == items && t[index].context == context)) {
                t[index].score = max(t[index].score, score);
                return t[index].score;
            }
            if (t[index].is_empty()) {
                // add entry
                t[index].score = score;
                t[index].items = items;
                t[index].context = context;
                ++size_;
                // load factor 4/5 -- ideally 2-3 average probes per lookup
                if (5*size_ > 4*t.size()) {
                    resize(t_bits + 1);
                }
                return score;
            }
        }
        assert (false && "bug: hash table probe loop");
    }
    size_t size() const {
        return size_;
    }
    void swap(Solver_table table)
    {
        t.swap(table.t);
        ::swap(size_, table.size_);
        ::swap(t_bits, table.t_bits);
        ::swap(num_probes_, table.num_probes_);
    }
};

/*
 * Main solver code.
 */
struct Solver
{
    // Inputs
    vector <string> items;
    string context0;
    size_t context0_index;

    // Mapping between strings and indices
    vector <string> context_to_string;
    unordered_map <string, size_t> string_to_context;

    // Items that have context-free prefixes, i.e. prefixes that
    // never overlap with the end of other items nor context0
    vector <bool> contextfree;

    // Precomputed contexts (suffixes) for each item
    vector <size_t> item_context;
    // Precomputed updates: (context, string) to overlap amount
    vector <vector <size_t>> join_overlap;

    Solver(vector <string> items, string context0)
        :items(items), context0(context0)
    {
        items = dedup_items(context0, items);
        init_context_();
    }

    void init_context_()
    {
        /*
         * Generate all relevant item-item contexts.
         *
         * At this point, we know that no item is a substring of
         * another, nor of context0. This means that the only contexts
         * we need to care about, are those generated from maximal join
         * overlaps between any two items.
         *
         * Proof:
         * Suppose that the shortest containing string needs some other
         * kind of context. Maybe it depends on a context spanning
         * three or more items, say X,Y,Z. But if Z ends after Y and
         * interacts with X, then Y must be a substring of Z.
         * This cannot happen, because we removed all substrings.
         *
         * Alternatively, it depends on a non-maximal join overlap
         * between two strings, say X,Y. But if this overlap does not
         * interact with any other string, then we could maximise it
         * and get a shorter solution. If it does, then call this
         * other string Z. We would get the same contradiction as in
         * the previous case with X,Y,Z.
         */
        size_t N = items.size();
        vector <size_t> max_prefix_overlap(N), max_suffix_overlap(N);
        size_t context0_suffix_overlap = 0;
        for (size_t i = 0; i < N; ++i) {
            for (size_t j = 0; j < N; ++j) {
                if (i == j) continue;
                string joined = join_strings(items[j], items[i]);
                size_t overlap = items[j].size() + items[i].size() - joined.size();
                string context = items[i].substr(0, overlap);
                max_prefix_overlap[i] = max(max_prefix_overlap[i], overlap);
                max_suffix_overlap[j] = max(max_suffix_overlap[j], overlap);

                if (string_to_context.find(context) == string_to_context.end()) {
                    string_to_context[context] = context_to_string.size();
                    context_to_string.push_back(context);
                }
            }

            // Context for initial join with context0
            {
                string joined = join_strings(context0, items[i]);
                size_t overlap = context0.size() + items[i].size() - joined.size();
                string context = items[i].substr(0, overlap);
                max_prefix_overlap[i] = max(max_prefix_overlap[i], overlap);
                context0_suffix_overlap = max(context0_suffix_overlap, overlap);

                if (string_to_context.find(context) == string_to_context.end()) {
                    string_to_context[context] = context_to_string.size();
                    context_to_string.push_back(context);
                }
            }
        }
        // Now compute all canonical trailing contexts
        context0_index = string_to_context[
                           context0.substr(context0.size() - context0_suffix_overlap)];
        item_context.resize(N);
        for (size_t i = 0; i < N; ++i) {
            item_context[i] = string_to_context[
                                items[i].substr(items[i].size() - max_suffix_overlap[i])];
        }

        // Now detect context-free items
        contextfree.resize(N);
        for (size_t i = 0; i < N; ++i) {
            contextfree[i] = (max_prefix_overlap[i] == 0);
            if (contextfree[i]) {
                DEBUG("  contextfree: %s\n", items[i].c_str());
            }
        }

        // Now compute all possible overlap amounts
        join_overlap.resize(context_to_string.size(), vector <size_t> (N));
        for (size_t c_index = 0; c_index < context_to_string.size(); ++c_index) {
            const string& context = context_to_string[c_index];
            for (size_t i = 0; i < N; ++i) {
                string joined = join_strings(context, items[i]);
                size_t overlap = context.size() + items[i].size() - joined.size();
                join_overlap[c_index][i] = overlap;
            }
        }
    }

    // Main solver.
    // Returns length of shortest string containing all items starting
    // from context0 (context0's length not included).
    size_t solve() const
    {
        size_t N = items.size();

        // Length, if joined without overlaps. We try to improve this by
        // finding overlaps in the main iteration
        size_t base_length = 0;
        for (auto s: items) {
            base_length += s.size();
        }

        // Now take non-context-free items. We will only need to search
        // over these items.
        vector <size_t> search_items;
        for (size_t i = 0; i < N; ++i) {
            if (!contextfree[i]) {
                search_items.push_back(i);
            }
        }
        size_t N_search = search_items.size();

        /*
         * Some groups of strings have the same context transitions.
         * For example "17", "107", "127", "167" all have an initial
         * context of "1" and a trailing context of "7", no other
         * overlaps are possible with other primes.
         *
         * We group these strings and treat them as indistinguishable
         * during the main algorithm.
         */
        auto eq_context = [&](size_t i, size_t j) {
            if (item_context[i] != item_context[j]) {
                return false;
            }
            for (size_t ci = 0; ci < context_to_string.size(); ++ci) {
                if (join_overlap[ci][i] != join_overlap[ci][j]) {
                    return false;
                }
            }
            return true;
        };
        vector <size_t> eq_context_group(N_search, size_t(-1));
        for (size_t si = 0; si < N_search; ++si) {
            for (size_t sj = si-1; sj+1 > 0; --sj) {
                size_t i = search_items[si], j = search_items[sj];
                if (!contextfree[j] && eq_context(i, j)) {
                    DEBUG("  eq context: %s =c= %s\n", items[i].c_str(), items[j].c_str());
                    eq_context_group[si] = sj;
                    break;
                }
            }
        }

        // Figure out the combined context size. A combined context has
        // one entry for each context-free item plus one for context0.
        size_t ccontext_size = N - N_search + 1;

        // Assert that various parameters all fit into our data types
        using ccontext_t = bitset_t;
        assert (context_to_string.size() + ccontext_size <= bitset_bits);
        assert (N_search <= solver_max_item_set);
        assert (base_length < 0xff);

        // Initial combined context.
        unordered_map <size_t, size_t> cc0_full;
        ++cc0_full[context0_index];
        for (size_t i = 0; i < N; ++i) {
            if (contextfree[i]) {
                ++cc0_full[item_context[i]];
            }
        }
        // Now pack into unary-encoded bitset. The bitset stores the
        // count for each context as <count> number of 0 bits,
        // followed by a 1 bit.
        ccontext_t cc0 = 0;
        for (size_t ci = 0, b = 0; ci < context_to_string.size(); ++ci, ++b) {
            b += cc0_full[ci];
            cc0 |= ccontext_t(1) << b;
        }

        // Map from (item set, context) to maximum achievable overlap
        Solver_table k_solns;
        // Base case: cc0 with empty set
        k_solns.update_score(0, cc0, 0);

        // Now start dynamic programming. k is current subset size
        size_t eq_context_groups = 0;
        for (size_t g: eq_context_group) eq_context_groups += (g != size_t(-1));
        if (context0.empty()) {
            INFO("solve: N=%zu, N_search=%zu, ccontext_size=%zu, #contexts=%zu, #eq_context_groups=%zu\n",
                 N, N_search, ccontext_size, context_to_string.size(), eq_context_groups);
        } else {
            DEBUG("solve: context=%s, N=%zu, N_search=%zu, ccontext_size=%zu, #contexts=%zu, #eq_context_groups=%zu\n",
                  context0.c_str(), N, N_search, ccontext_size, context_to_string.size(), eq_context_groups);
        }
        for (size_t k = 0; k < N_search; ++k) {
            decltype(k_solns) k1_solns;

            // The main bottleneck of this program is updating k1_solns,
            // which (for larger N) becomes a huge table.
            // We use a prefetch queue to reduce memory latency.
            const size_t prefetch = 8;
            array <Solver_entry, prefetch> entry_queue;
            size_t update_i = 0;

            // Iterate every k-subset
            for (Solver_entry entry: k_solns.t) {
                if (entry.is_empty()) continue;

                bitset_t s = entry.items;
                ccontext_t ccontext = entry.context;
                size_t overlap = entry.score;

                // Try adding a new item
                for (size_t si = 0; si < N_search; ++si) {
                    bitset_t s1 = s | bitset_t(1) << si;
                    if (s == s1) {
                        continue;
                    }
                    // Add items in each eq_context_group sequentially
                    if (eq_context_group[si] != size_t(-1) &&
                        !(s & bitset_t(1) << eq_context_group[si])) {
                        continue;
                    }
                    size_t i = search_items[si]; // actual item index

                    size_t new_context = item_context[i];
                    // Increment ccontext's count for new_context.
                    // We need to find its delimiter 1 bit
                    size_t bit_n = bit_select(ccontext, new_context);
                    ccontext_t ccontext_n =
                        ((ccontext & ((ccontext_t(1) << bit_n) - 1))
                         | ((ccontext >> bit_n << (bit_n + 1))));

                    // Select non-empty sub-contexts to substitute for new_context
                    for (size_t ci = 0, bit1 = 0, count;
                         ci < context_to_string.size();
                         ++ci, bit1 += count + 1)
                    {
                        assert (ccontext_n >> bit1);
                        count = __builtin_ctzll(ccontext_n >> bit1);
                        if (!count
                            // We just added new_context; we can only remove an existing
                            // context entry there i.e. there must be at least two now
                            || (ci == new_context && count < 2)) {
                            continue;
                        }

                        // Decrement ci in ccontext_n
                        bitset_t ccontext1 =
                            ((ccontext_n & ((ccontext_t(1) << bit1) - 1))
                             | ((ccontext_n >> (bit1 + 1)) << bit1));

                        size_t overlap1 = overlap + join_overlap[ci][i];

                        // do previous prefetched update
                        if (update_i >= prefetch) {
                            Solver_entry entry = entry_queue[update_i % prefetch];
                            k1_solns.update_score(entry.items, entry.context, entry.score);
                        }

                        // queue the current update and prefetch
                        Solver_entry entry1;
                        size_t probe_index = k1_solns.probe_index(Solver_table::entry_hash(s1, ccontext1));
                        __builtin_prefetch(&k1_solns.t[probe_index]);
                        entry1.items = s1;
                        entry1.context = ccontext1;
                        entry1.score = overlap1;
                        entry_queue[update_i % prefetch] = entry1;

                        ++update_i;
                    }
                }
            }

            // do remaining queued updates
            for (size_t j = 0; j < min(update_i, prefetch); ++j) {
                Solver_entry entry = entry_queue[j];
                k1_solns.update_score(entry.items, entry.context, entry.score);
            }

            if (context0.empty()) {
                INFO("  hash stats: |solns[%zu]| = %zu, %zu lookups, %zu probes\n",
                     k+1, k1_solns.size(), update_i, k1_solns.num_probes_);
            } else {
                DEBUG("  hash stats: |solns[%zu]| = %zu, %zu lookups, %zu probes\n",
                      k+1, k1_solns.size(), update_i, k1_solns.num_probes_);
            }
            k_solns.swap(k1_solns);
        }

        // Overall solution
        size_t max_overlap = 0;
        for (Solver_entry entry: k_solns.t) {
            if (entry.is_empty()) continue;
            max_overlap = max(max_overlap, size_t(entry.score));
        }
        return base_length - max_overlap;
    }
};

// Wrapper for Solver that also finds the smallest solution string
string smallest_containing_string(vector <string> items)
{
    items = dedup_items("", items);

    size_t soln_length;
    {
        Solver solver(items, "");
        soln_length = solver.solve();
    }
    DEBUG("Found solution length: %zu\n", soln_length);

    string soln;
    vector <string> remaining_items = items;
    while (remaining_items.size() > 1) {
        // Add all possible next items, in lexicographic order
        vector <pair <string, size_t>> next_solns;
        for (size_t i = 0; i < remaining_items.size(); ++i) {
            const string& item = remaining_items[i];
            next_solns.push_back(make_pair(join_strings(soln, item), i));
        }
        assert (next_solns.size() == remaining_items.size());
        sort(next_solns.begin(), next_solns.end());

        // Now try every item in order
        bool found_next = false;
        for (auto ns: next_solns) {
            size_t i;
            string next_soln;
            tie(next_soln, i) = ns;
            DEBUG("Trying: %s + %s -> %s\n",
                  soln.c_str(), remaining_items[i].c_str(), next_soln.c_str());
            vector <string> next_remaining;
            for (size_t j = 0; j < remaining_items.size(); ++j) {
                if (next_soln.find(remaining_items[j]) == string::npos) {
                    next_remaining.push_back(remaining_items[j]);
                }
            }

            Solver solver(next_remaining, next_soln);
            size_t next_size = solver.solve();
            DEBUG("  ... next_size: %zu + %zu =?= %zu\n", next_size, next_soln.size(), soln_length);
            if (next_size + next_soln.size() == soln_length) {
                INFO("  found next item: %s\n", remaining_items[i].c_str());
                soln = next_soln;
                remaining_items = next_remaining;
                // found lexicographically smallest solution, break now
                found_next = true;
                break;
            }
        }
        assert (found_next);
    }
    soln = join_strings(soln, remaining_items[0]);

    return soln;
}

int main()
{
    string prev_soln;
    vector <string> items;
    size_t p = 1;
    for (size_t N = 1;; ++N) {
        for (++p; items.size() < N; ++p) {
            if (is_prime(p)) {
                char buf[99];
                snprintf(buf, sizeof buf, "%zu", p);
                items.push_back(buf);
                break;
            }
        }

        // Try to reuse previous solution (this works for N=11,30,32...)
        string soln;
        if (prev_soln.find(items.back()) != string::npos) {
            soln = prev_soln;
        } else {
            soln = smallest_containing_string(items);
        }
        printf("%s\n", soln.c_str());
        prev_soln = soln;
    }
}

Try it online!

And the prime-only version on TIO. Sorry, but I didn't golf these programs and there's a post length limit.

  • Unrelated: Instead of debug_dummy, you can use #define DEBUG(x) void(0). – user202729 Dec 4 at 11:06
  • Amazing! I have been hoping for a C/C++ answer. I'll try running it as soon as possible! How much RAM do you have on your machine? I'll try to maximize the amount available for your script when I benchmark it properly. – maxb Dec 4 at 11:09
  • user: I use debug_dummy because I want the arguments to be type checked and evaluated even when debugging is off. – japh Dec 4 at 11:11
  • @maxb: also 16GB. But N=32 only needs about 500MB, I think. – japh Dec 4 at 11:13
  • 1
    Great improvement! I'll run it later today. The code you pasted above doesn't include the main, but I found it from the TIO link. – maxb Dec 6 at 7:48

Concorde TSP solver, score 84 in 299 seconds

Well… I feel silly for only realising this now.

This whole thing is essentially a travelling salesman problem. For each pair of primes p and q, add an edge whose weight is the number of digits added by q (removing overlapping digits). Also, add an initial edge to every prime p, whose weight is the length of p. The shortest travelling salesman path matches the length of the smallest prime containment number.

Then an industrial grade TSP solver, such as Concorde, will make short work of this problem.

This entry should probably be considered non-competing.

Results

The solver gets to N=350 in about 20 CPU hours. The full results are too long for one SE post, and the OEIS doesn't want that many terms anyway. Here are the first 200:

1 2
2 23
3 235
4 2357
5 112357
6 113257
7 1131725
8 113171925
9 1131719235
10 113171923295
11 113171923295
12 1131719237295
13 11317237294195
14 1131723294194375
15 113172329419437475
16 1131723294194347537
17 113172329419434753759
18 2311329417434753759619
19 231132941743475375961967
20 2311294134347175375961967
21 23112941343471735375961967
22 231129413434717353759619679
23 23112941343471735359619678379
24 2311294134347173535961967837989
25 23112941343471735359619678378979
26 2310112941343471735359619678378979
27 231010329411343471735359619678378979
28 101031071132329417343475359619678378979
29 101031071091132329417343475359619678378979
30 101031071091132329417343475359619678378979
31 101031071091131272329417343475359619678378979
32 101031071091131272329417343475359619678378979
33 10103107109113127137232941734347535961967838979
34 10103107109113127137139232941734347535961967838979
35 10103107109113127137139149232941734347535961967838979
36 1010310710911312713713914923294151734347535961967838979
37 1010310710911312713713914915157232941734347535961967838979
38 1010310710911312713713914915157163232941734347535961967838979
39 10103107109113127137139149151571631672329417343475359619798389
40 10103107109113127137139149151571631672329417343475359619798389
41 1010310710911312713713914915157163167173232941794347535961978389
42 101031071091131271371391491515716316717323294179434753596181978389
43 101031071091131271371391491515716316723294173434753596181917978389
44 101031071091131271371391491515716316717323294179434753596181919383897
45 10103107109113127137139149151571631671731792329418191934347535961978389
46 10103107109113127137139149151571631671731791819193232941974347535961998389
47 101031071091271313714915157163167173179181919321139232941974347535961998389
48 1010310710912713137149151571631671731791819193211392232941974347535961998389
49 1010310710912713137149151571631671731791819193211392232272941974347535961998389
50 10103107109127131371491515716316717317918191932113922322722941974347535961998389
51 101031071091271313714915157163167173179181919321139223322722941974347535961998389
52 101031071091271313714915157163167173179181919321139223322722923941974347535961998389
53 1010310710912713137149151571631671731791819193211392233227229239241974347535961998389
54 101031071091271313714915157163167173179211392233227229239241819193251974347535961998389
55 101031071091271313714915157163167173179211392233227229239241819193251972574347535961998389
56 101031071091271313714915157163167173179211392233227229239241819193251972572634347535961998389
57 101031071091271313714915157163167173179211392233227229239241819193251972572632694347535961998389
58 101031071091271313714915157163167173179211392233227229239241819193251972572632694347535961998389
59 1010310710912713137149151571631671731792113922332277229239241819193251972572632694347535961998389
60 101031071091271313714915157163167173211392233227722923924179251819193257263269281974347535961998389
61 1010310710912713137149151571631671732113922332277229239241792518191932572632692819728343475359619989
62 10103107109127131371491515716316717321139223322772293239241792518191932572632692819728343475359619989
63 1010307107109127131371491515716316717321139223322772293239241792518191932572632692819728343475359619989
64 10103071071091271311371391491515716316721173223322772293239241792518191932572632692819728343475359619989
65 10103071071091271311371491515716313916721173223322772293239241792518191932572632692819728343475359619989
66 10103071071091271311371491515716313921167223317322772293239241792518191932572632692819728343475359619989
67 10103071071091271311371491515716313921167223317322772293239241792518191932572632692819728343475359619989
68 1010307107109127131137149151571631392116722331732277229323924179251819193257263269281972833743475359619989
69 1010307107109127131137149151571631392116722331732277229323924179251819193257263269281972833743475359619989
70 101030710710912713113714915157163139211672233173227722932392417925181919325726326928197283374347534959619989
71 101030710710912713113714915157163139211672233173227722932392417925181919325726337269281972834743534959619989
72 101030710710912713113714915157163139211672233173227722932392417925181919337257263472692819728349435359619989
73 10103071071091271311371491515716313921167223317322772293372392417925181919347257263492692819728353594367619989
74 101030710710912713113714915157163139211672233173227722932392417925181919337347257263492692819728353594367619989
75 1010307107109127131137313914915157163211672233173227722933792392417925181919347257263492692819728353594367619989
76 101030710710912713113731391491515716321167223317322772293379239241792518191934725726349269281972835359438367619989
77 101030710710912713113731391491515716321167223317337922772293472392417925181919349257263535926928197283674383896199
78 1010307107109127131137313914915157163211672233173379227722934723972417925181919349257263535926928197283674383896199
79 101030710710912713113731391491515721163223317337922772293472397241672517925726349269281819193535928367401974383896199
80 101030710710912713113731391491515721163223317337922772293472397241672517925726349269281819193535928367401974094383896199
81 101030710710912713113731391491515721163223317337922772293472397241916725179257263492692818193535928367401974094383896199
82 1010307107109127131137313914915157223317322772293379239724191634725167257263492692817928353594018193674094211974383896199
83 1010307107109127131137313914922331515722772293379239724191634725167257263492692817353592836740181938389409421197431796199
84 101030710710912713113731391492233151572277229323972419163472516725726349269281735359283674018193838940942119743179433796199
85 101030710710912713113731391492233151572277229323924191634725167257263492692817353592836740181938389409421197431794337943976199
86 1010307107109127131137313914922331515722772293239241916347251672572634926928173535928367401819383894094211974317943379443976199
87 1010307107109127131137313914922331515722772293239241916347251672572634926928173535928367401819383894094211974317943379443974496199
88 1010307107109127131137313914922331515722772293239241916347251672572634926928173535928367401819383894094211974317943379443974494576199
89 10103071071091271311373139149223315157227722932392419163472516725726349269281735359283674018193838940942119743179433794439744945746199
90 10103071071091271311373139149223315157227722932392419163251672572634726928173492835359401819367409421197431794337944397449457461994638389
91 10103071071091271311373139149223315157227722932392419163251672572634726928173492835359401819367409421197431794337944397449457461994638389467
92 101030710710912713113731391492233151572277229323924191632516725726347926928173492835359401819367409421197431794337944397449457461994638389467
93 101030710710912713113731391492233151572277229323924191632516725726347926928173492835359401819367409421197431794337944397449457461994638389467487
94 101030710710912713113731392233149151572277229323924191632516725726347926928173492835359401819367409421197431794337944397449457461994638389467487
95 1010307107109127131137313922331491515722772293239241916325167257263479269281734928353594018193674094211974317943379443974499457461994638389467487
96 1010307107109127131137313922331491515722772293239241916325167257263269281734792834940181935359409421197431794337944397449945746199463674674875038389
97 1010307107109127131137313922331491515722772293239241916325167257263269281734792834940181935359409421197431794337944397449945746199463674674875038389509
98 101030710710912713113732233139227722932392419149151572516325726326928167283479401734940942118193535943179433794439744994574619746367467487503838950952199
99 1010307107109127131137322331392277229324191491515725163257263269281672834794017349409421181935359431794337944394499457461974636746748750383895095219952397
100 101030710710922331127131373227722932414915157251632572632692816728347940173494094211394317943379443944994574618191935359463674674875038389509521975239754199
101 101030710710922331127131373227722932414915157251632572632692816728347401734940942113943179433794439449945746181919353594636746748750383895095219752397541995479
102 101030710710922331127131373227722932414915157251632572632692816728347401734940942113943179433794439449945746181919353594636746748750383895095219752397541995479557
103 101030710710922331127131373227722932414915157251632572632692816728340173474094211394317943379443944945746181919349946353594674875036750952197523975419954795575638389
104 101030710710922331127131373227722932414915157251632572632692816728340173474094211394317943379443944945746181919349946353594674875036750952197523975419954795575638389569
105 101030710722331109227127722932413137325149151571632572632692816728340173474094211394317943379443944945746181919349946353594674875036750952197523975419954795575638389569
106 1010307107223311092271277229324131373251491515716325726326928167283401734740942113943179433794439449457461819193499463535946748750367509521975239754199547955775638389569
107 1010307107223311092271277229324131373251491515716325726326928167283401734740942113943179433794439449457461819193499463535946748750367509521975239754199547955775638389569587
108 10103071072233110922712772293241313732514915157163257263269281672834017340942113943179433794439449457461819193474634994674875035359367509521975239754199547955775638389569587
109 10103071072233110922712772293241313732514915157163257263269281672834017340942113943179433794439449457461819193474634994674875035359367509521975239754199547955775638389569587599
110 1010307223311072271092293241277251313732571491515726326928163283401674094211394317343379443944945746179463474674875034995095218191935359367523975419754795577563838956958759960199
111 1010307223311072271092293241277251313732571491515726326928163283401674094211394317343379443944945746179463474674875034995095218191935359367523975419754795577563838956958759960199607
112 1010307223311072271092293241277251491515716325726326928167283401734094211313734317943379443944945746139463474674875034995095218191935359367523975419754795577563838956958759960199607
113 22331101030722710722932410925127725714915157263269281632834016740942113137343173433794439449457461394634746748750349950952181919353593675239754197547955775638389569587599601996076179
114 2233110103072271072293241092512571277263269281491515728340163409421131373431734337944394494574613946347467487503499509521675239754191819353593675479557756383895695875996019760761796199
115 22331010307227107229324109251257126311277269281491515728340163409421131373431734337944394494574613946347467487503499509521675239754191819353593675479557756383895695875996019760761796199
116 22331010307227107229324109251257126311269281277283401491515740942113137343173433794439449457461394634674875034750952163499523975416754795577563535936756958759960181919383896076179619764199
117 223310103072271072293241092512571263112692812772834014915157409421131373431734433794494574613946346748750347509521634995239541675479557756353593675695875996018191938389607617961976419964397
118 223310103072271072293241092512571263112692812772834014915157409421131373431734433794494574613946346748750347509521634995239541675475577563535936756958759960181919383896076179619764199643976479
119 223310103072271072293241092512571263112692812772834014915157409421131373431734433794494574613946346748750347509521634995239541675475577563535695875935996018191936760761796197641996439764796538389
120 2233101030722710722932410925125712631126928127728340149151574094211313734317344337944945746139463467487503475095216349952395416754755775635356958760181919359367607617961976419964397647965383896599
121 22331010307227107229324109251257126311269281277283401491515740942113137343173443379449457461394634674875034750952163499523954167547557756353569587601819193593676076179641976439764796538389659966199
122 223310103072271072293241092512571263112692812772834014915157409421131373431734433794494574613946346734748750349950952163523954167547557756353569587601819193593676076179641976439764796538389659966199
123 2233101030722710722932410925125712631126928127728340149151574094211313734317344337944945746139463467347487503499509521635239541675475577563535695876018191935936776076179641976439764796538389659966199
124 2233101030722710722932410925125712631126928127728340149151574094211313734317344337944945746139463467347487503499509521635239541675475577563535695876018191935936076179641976439764796536776599661996838389
125 22331010307227107229324109251257126311269127728128340149151574094211313734317344337944945746139463467347487503499509521635239541675475577563535695876018191935936076179641976439764796536776599661996838389
126 2233101030701072271092293241251257126311269127728128340149151574094211313734317344337944945746139463467347487503499509521635239541675475577563535695876018191935936076179641976439764796536776599661996838389
127 223310103070107092271092293241251257126311269127728128340149151574094211313734317344337944945746139463467347487503499509521635239541675475577563535695876018191935936076179641976439764796536776599661996838389
128 223310103070107092271092293241251257191263112691277281283401491515740942113137343173443379449457461394634673474875034995095216352395416754755775635356958760181935936076179641976439764796536776599661996838389
129 22331010307010709227109229324125125719126311269127277281283401491515740942113137343173443379449457461394634673474875034995095216352395416754755775635356958760181935936076179641976439764796536776599661996838389
130 223307010103227092293241072510925712631126912719128128340140942113137331491515727743173443379449457461394634673474875034995095216352395416754755775635356958760181935936076179641976439764796536776599661996838389
131 2233070101032270922932410725109257126311269127191281283401409421131373314915157277431734433794494574613946346739487503475095216349952395416754755775635356958760181935936076179641976439764796536776599661996838389
132 2233070101032270922932410725109257126311269127191281283401409421131373314915157277431734433794494574613946346739487503475095216349952395416754755775635356958760181935936076179641976439764796536776599661996838389
133 223307010103227092293241072510925712631126912719128128340140942113137331443173449149457277433794613946346739487503475095215157516349952395416754755775635356958760181935936076179641976439764796536776599661996838389
134 22330701010322709229324107251092571263112691271912812834014094211313733144317344914945727743379461394634673948750347509521515751634995239541675475575635356958757760181935936076179641976439764796536776599661996838389
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136 2233070101032270922932410725109257126311269127191281283401409421131373314431734491494572774337946139463467394875034750952151575163499523954167547557563535695875776018193593607617964197643976479653677696599661996838389
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Code

Here's a Python 3 script to call the Concorde solver over and over until it constructs the solutions.

Concorde is free for academic use. You can download an executable binary of Concorde built with their own linear programming package QSopt, or if you somehow have a license for IBM CPLEX, you could build Concorde from source to use CPLEX.

#!/usr/bin/env python3
'''
Find prime containment numbers (OEIS A054261) using the Concorde
TSP solver.

The n-th prime containment number is the smallest natural number
which, when written in decimal, contains the first n primes.
'''

import argparse
import itertools
import os
import sys
import subprocess
import tempfile

def join_strings(a, b):
  '''Shortest string that starts with a and ends with b.'''
  for overlap in range(min(len(a), len(b)), 0, - 1):
    if a[-overlap:] == b[:overlap]:
      return a + b[overlap:]
  return a + b

def is_prime(n):
  if n < 2:
    return False
  d = 2
  while d*d <= n:
    if n % d == 0:
      return False
    d += 1
  return True

def prime_list_reduced(n):
  '''First n primes, with primes that are substrings of other
     primes removed.'''
  primes = []
  p = 2
  while len(primes) < n:
    if is_prime(p):
      primes.append(p)
    p += 1

  reduced = []
  for p in primes:
    if all(p == q or str(p) not in str(q) for q in primes):
      reduced.append(p)
  return reduced

# w_med is an offset for actual weights
# (we use zero as a dummy weight when splitting nodes)
w_med = 10**4
# w_big blocks edges from being taken
w_big = 10**8

def gen_tsplib(prefix, strs, start_candidates):
  '''Generate TSP formulation in TSPLIB format.

     Returns a TSPLIB format string that encodes the length of the
     shortest string starting with 'prefix' and containing all 'strs'.

     start_candidates is the set of strings that solution paths are
     allowed to start with.
     '''
  N = len(strs)

  # Concorde only supports symmetric TSPs. Therefore we encode the
  # asymmetric TSP instances by doubling each node.
  node_in = lambda i: 2*i
  node_out = lambda i: node_in(i) + 1
  # 2*(N+1) nodes because we add an artificial node with index N
  # for the start/end of the tour. This node is also doubled.
  num_nodes = 2*(N+1)

  # Ensure special offsets are big enough
  assert w_med > len(prefix) + sum(map(len, strs))
  assert w_big > w_med * num_nodes

  weight = [[w_big] * num_nodes for _ in range(num_nodes)]
  def edge(src, dest, w):
    weight[node_out(src)][node_in(dest)] = w
    weight[node_in(dest)][node_out(src)] = w

  # link every incoming node with the matching outgoing node
  for i in range(N+1):
    weight[node_in(i)][node_out(i)] = 0
    weight[node_out(i)][node_in(i)] = 0

  for i, p in enumerate(strs):
    if p in start_candidates:
      prefix_w = len(join_strings(prefix, p))
      # Initial length
      edge(N, i, w_med + prefix_w)
    else:
      edge(N, i, w_big)
    # Link every str to the end to allow closed tours
    edge(i, N, w_med)

  for i, p in enumerate(strs):
    for j, q in enumerate(strs):
      if i != j:
        w = len(join_strings(p, q)) - len(p)
        edge(i, j, w_med + w)

  out = '''NAME: prime-containment-number
TYPE: TSP
DIMENSION: %d
EDGE_WEIGHT_TYPE: EXPLICIT
EDGE_WEIGHT_FORMAT: FULL_MATRIX
EDGE_WEIGHT_SECTION
''' % num_nodes

  out += '\n'.join(
    ' '.join(str(w) for w in row)
    for row in weight
  ) + '\n'

  out += 'EOF\n'
  return out

def parse_tour_soln(prefix, strs, text):
  '''This constructs the solution from Concorde's 'tour' output format.
     The format simply consists of a permutation of the graph nodes.'''
  N = len(strs)
  node_in = lambda i: 2*i
  node_out = lambda i: node_in(i) + 1
  nums = list(map(int, text.split()))

  # The file starts with the number of nodes
  assert nums[0] == 2*(N+1)
  nums = nums[1:]

  # Then it should list a permutation of all nodes
  assert len(nums) == 2*(N+1)

  # Find and remove the artificial starting point
  start = nums.index(node_out(N))
  nums = nums[start+1:] + nums[:start]
  # Also find and remove the end point
  if nums[-1] == node_in(N):
    nums = nums[:-1]
  elif nums[0] == node_in(N):
    # Tour printed in reverse order
    nums = reversed(nums[1:])
  else:
    assert False, 'bad TSP tour'
  soln = prefix
  for i in nums:
    # each prime appears in two adjacent nodes, pick one arbitrarily
    if i % 2 == 0:
      soln = join_strings(soln, strs[i // 2])
  return soln

def scs_length(prefix, strs, start_candidates, concorde_path, concorde_verbose):
  '''Find length of shortest containing string using one call to Concorde.'''
  # Concorde's small-input solver CCHeldKarp, tends to fail with the
  # cryptic error message 'edge too long'. Brute force instead
  if len(strs) <= 5:
    best = len(prefix) + sum(map(len, strs))
    for perm in itertools.permutations(range(len(strs))):
      if perm and strs[perm[0]] not in start_candidates:
        continue
      soln = prefix
      for i in perm:
        soln = join_strings(soln, strs[i])
      best = min(best, len(soln))
    return best

  with tempfile.TemporaryDirectory() as tempdir:
    concorde_path = os.path.join(os.getcwd(), concorde_path)
    with open(os.path.join(tempdir, 'prime.tsplib'), 'w') as f:
      f.write(gen_tsplib(prefix, strs, start_candidates))

    if concorde_verbose:
      subprocess.check_call([concorde_path, os.path.join(tempdir, 'prime.tsplib')],
                            cwd=tempdir)
    else:
      try:
        subprocess.check_output([concorde_path, os.path.join(tempdir, 'prime.tsplib')],
                                cwd=tempdir, stderr=subprocess.STDOUT)
      except subprocess.CalledProcessError as e:
        print('Concorde exited with error code %d\nOutput log:\n%s' %
              (e.returncode, e.stdout.decode('utf-8', errors='ignore')),
              file=sys.stderr)
        raise

    with open(os.path.join(tempdir, 'prime.sol'), 'r') as f:
      soln = parse_tour_soln(prefix, strs, f.read())
    return len(soln)

# Cache results from previous N's
pcn_solve_cache = {} # (prefix fragment, strs) -> soln

def pcn(n, concorde_path, concorde_verbose):
  '''Find smallest prime containment number for first n primes.'''
  strs = list(map(str, prime_list_reduced(n)))
  target_length = scs_length('', strs, strs, concorde_path, concorde_verbose)

  def solve(prefix, strs, target_length):
    if not strs:
      return prefix

    # Extract part of prefix that is relevant to cache
    prefix_fragment = ''
    for s in strs:
      next_prefix = join_strings(prefix, s)
      overlap = len(prefix) + len(s) - len(next_prefix)
      fragment = prefix[len(prefix) - overlap:]
      if len(fragment) > len(prefix_fragment):
        prefix_fragment = fragment
    fixed_prefix = prefix[:len(prefix) - len(prefix_fragment)]
    assert fixed_prefix + prefix_fragment == prefix

    cache_key = (prefix_fragment, tuple(strs))
    if cache_key in pcn_solve_cache:
      return fixed_prefix + pcn_solve_cache[cache_key]

    # Not in cache, we need to calculate it.
    soln = None

    # Try strings in ascending order until scs_length reports a
    # solution with equal length. That string will be the
    # lexicographically smallest extension of our solution.
    next_prefixes = sorted((join_strings(prefix, s), s)
                           for s in strs)

    # Try first string -- often works
    next_prefix, _ = next_prefixes[0]
    next_prefixes = next_prefixes[1:]
    next_strs = [s for s in strs if s not in next_prefix]
    next_length = scs_length(next_prefix, next_strs, next_strs,
                             concorde_path, concorde_verbose)
    if next_length == target_length:
      soln = solve(next_prefix, next_strs, next_length)
    else:
      # If not, do a weighted binary search on remaining strings
      while len(next_prefixes) > 1:
        split = (len(next_prefixes) + 2) // 3
        group = next_prefixes[:split]
        group_length = scs_length(prefix, strs, [s for _, s in group],
                                  concorde_path, concorde_verbose)
        if group_length == target_length:
          next_prefixes = group
        else:
          next_prefixes = next_prefixes[split:]
      if next_prefixes:
        next_prefix, _ = next_prefixes[0]
        next_strs = [s for s in strs if s not in next_prefix]
        check = True
        # Uncomment if paranoid
        #next_length = scs_length(next_prefix, next_strs, next_strs,
        #                         concorde_path, concorde_verbose)
        #check = (next_length == target_length)
        if check:
          soln = solve(next_prefix, next_strs, target_length)

    assert soln is not None, (
      'solve failed! prefix=%r, strs=%r, target_length=%d' %
      (prefix, strs, target_length))

    pcn_solve_cache[cache_key] = soln[len(fixed_prefix):]
    return soln

  return solve('', strs, target_length)

parser = argparse.ArgumentParser()
parser.add_argument('--concorde', type=str, default='concorde',
                    help='path to Concorde binary')
parser.add_argument('--verbose', action='store_true',
                    help='dump all Concorde output')
parser.add_argument('--start', type=int, metavar='N', default=1,
                    help='start at this N')
parser.add_argument('--end', type=int, metavar='N', default=1000000,
                    help='stop after this N')
parser.add_argument('--one', type=int, metavar='N',
                    help='solve for a single N and exit')

def main():
  opts = parser.parse_args(sys.argv[1:])

  if opts.one is not None:
    opts.start = opts.one
    opts.end = opts.one

  prev_soln = ''
  for n in range(opts.start, opts.end+1):
    primes = map(str, prime_list_reduced(n))
    if all(p in prev_soln for p in primes):
      soln = prev_soln
    else:
      soln = pcn(n, opts.concorde, opts.verbose)

    print('%d %s' % (n, soln))
    sys.stdout.flush()
    prev_soln = soln

if __name__ == '__main__':
  main()

  • This is just incredible. Since the problem is NP-complete, I knew that you could transform it to TSP theoretically. But straight up using a TSP solver is really clever! I'll have to benchmark it later today, but I'm quite sure that this will be the fastest solution so far. – maxb Dec 10 at 7:45
  • I also made sure to verify that both your solutions give the same result for the first 62 numbers. How much memory does this solution require? I might put my old laptop to work for a few days crunching numbers. – maxb Dec 10 at 7:57
  • I'm as amazed as you are. Before this, my mental model of TSP solvers was limited to scenarios involving Euclidean distance tours of cities, airports, warehouses, etc. Finding these strings is a challenging combinatorial problem (the edge weights are all 1, 2 and 3). Concorde slices through them like warm butter. – japh Dec 10 at 8:02
  • The Concorde solver even uses less RAM than the Python script that oversees it. – japh Dec 10 at 8:03
  • Awesome results! I've already visited the Concorde site because of this challenge before you posted this, but then still thought that it's probably not worth trying it. Anyway, I'm quite sure OEIS is interested in all your results. Just give them as b-file for results with at most 1000 digits, and as a-file for longer results. – Christian Sievers Dec 10 at 13:55

Clean, score 25 in 231 seconds (official score)

Results

  • 1 < n <= 23 in 42 36 seconds on TIO
  • n = 24 (2311294134347173535961967837989) in 32 24 seconds locally
  • n = 25 (23112941343471735359619678378979) in 210 160 seconds locally
  • n = 1 to n = 25 was found in 231 seconds for the official scoring (edited by maxb)

This uses a similar approach to Arnauld's JS solution based on recursive permutation rejection, using a specialized tree-set to gain a lot of speed.

For every prime that needs to fit in the number:

  1. check if the prime is a sub-string of another prime, and if so, remove it
  2. sort the current list of prime sub-strings, join it, and add it to the balanced tree set
  3. check if any primes fit on the front of any other ones, and if so, join them - ignoring adjacent already-ordered elements that are tested by the rejection step anyway

Then, for each pair of sub-strings that we joined, remove any sub-strings of that joined pair from the list of sub-strings and recurse on it.

Once no more sub-strings can be joined to any other sub-strings on any arm of our recursion, we use the already-ordered tree set to quickly find the lowest number containing the sub-strings.

Things to be improved / added:

  • Move away from permuting the entire search space, generate candidates instead
  • Prefix / Suffix -based candidate generation to enable memoization
  • Multithreading, split work over prefixes evenly to the number of threads

There were large performance drops between 19 -> 20 and 24 -> 25 due to duplicate handling by the merge trial step and the candidate rejection step, but these have been fixed.

Optimizations:

  • removeOverlap is designed to always give a set of sub-strings already in the optimal order
  • uInsertMSpec reduces check-if-is-member and insert-new-member to one set traversal
  • containmentNumbersSt checks if the previous solution works for a new number
module main
import StdEnv,StdOverloadedList,_SystemEnumStrict
import Data.List,Data.Func,Data.Maybe,Data.Array
import Text,Text.GenJSON

// adapted from Data.Set to work with a single specific type, and persist uniqueness
:: Set a = Tip | Bin !Int a !.(Set a) !.(Set a)
derive JSONEncode Set
derive JSONDecode Set

delta :== 4
ratio :== 2

:: NumberType :== String

:: SetType :== NumberType

//uSingleton :: SetType -> Set
uSingleton x :== (Bin 1 x Tip Tip)

// adapted from Data.Set to work with a single specific type, and persist uniqueness
uFindMin :: !.(Set .a) -> .a
uFindMin (Bin _ x Tip _) = x
uFindMin (Bin _ _ l _)   = uFindMin l

uSize set :== case set of
	Tip = (0, Tip)
	s=:(Bin sz _ _ _) = (sz, s)
	
uMemberSpec :: String !u:(Set String) -> .(.Bool, v:(Set String)), [u <= v]
uMemberSpec x Tip = (False, Tip)
uMemberSpec x set=:(Bin s y l r)
	| sx < sy || sx == sy && x < y
		# (t, l) = uMemberSpec x l
		= (t, Bin s y l r)
		//= (t, if(t)(\y` l` r` = Bin sz y` l` r`) uBalanceL y l r)
	| sx > sy || sx == sy && x > y
		# (t, r) = uMemberSpec x r
		= (t, Bin s y l r)
		//= (t, if(t)(\y` l` r` = Bin sz y` l` r`) uBalanceR y l r)
	| otherwise = (True, set)
where
	sx = size x
	sy = size y

uInsertM :: !(a a -> .Bool) -> (a u:(Set a) -> v:(.Bool, w:(Set a))), [v u <= w]
uInsertM cmp = uInsertM`
where
	//uInsertM` :: a (Set a) -> (Bool, Set a)
	uInsertM` x Tip = (False, uSingleton x)
	uInsertM` x set=:(Bin _ y l r)
		| cmp x y//sx < sy || sx == sy && x < y
			# (t, l) = uInsertM` x l
			= (t, uBalanceL y l r)
			//= (t, if(t)(\y` l` r` = Bin sz y` l` r`) uBalanceL y l r)
		| cmp y x//sx > sy || sx == sy && x > y
			# (t, r) = uInsertM` x r
			= (t, uBalanceR y l r)
			//= (t, if(t)(\y` l` r` = Bin sz y` l` r`) uBalanceR y l r)
		| otherwise = (True, set)
		
uInsertMCmp :: a !u:(Set a) -> .(.Bool, v:(Set a)) | Enum a, [u <= v]
uInsertMCmp x Tip = (False, uSingleton x)
uInsertMCmp x set=:(Bin _ y l r)
	| x < y
		# (t, l) = uInsertMCmp x l
		= (t, uBalanceL y l r)
		//= (t, if(t)(\y` l` r` = Bin sz y` l` r`) uBalanceL y l r)
	| x > y
		# (t, r) = uInsertMCmp x r
		= (t, uBalanceR y l r)
		//= (t, if(t)(\y` l` r` = Bin sz y` l` r`) uBalanceR y l r)
	| otherwise = (True, set)

uInsertMSpec :: NumberType !u:(Set NumberType) -> .(.Bool, v:(Set NumberType)), [u <= v]
uInsertMSpec x Tip = (False, uSingleton x)
uInsertMSpec x set=:(Bin sz y l r)
	| sx < sy || sx == sy && x < y
		#! (t, l) = uInsertMSpec x l
		= (t, uBalanceL y l r)
		//= (t, if(t)(\y` l` r` = Bin sz y` l` r`) uBalanceL y l r)
	| sx > sy || sx == sy && x > y
		#! (t, r) = uInsertMSpec x r
		= (t, uBalanceR y l r)
		//= (t, Bin sz y l r)
		//= (t, if(t)(\y` l` r` = Bin sz y` l` r`) uBalanceR y l r)
	| otherwise = (True, set)
where
	sx = size x
	sy = size y

// adapted from Data.Set to work with a single specific type, and persist uniqueness
uBalanceL :: .a !u:(Set .a) !v:(Set .a) -> w:(Set .a), [v u <= w]
//a .(Set a) .(Set a) -> .(Set a)
uBalanceL x Tip Tip
	= Bin 1 x Tip Tip
uBalanceL x l=:(Bin _ _ Tip Tip) Tip
	= Bin 2 x l Tip
uBalanceL x l=:(Bin _ lx Tip (Bin _ lrx _ _)) Tip
	= Bin 3 lrx (Bin 1 lx Tip Tip) (Bin 1 x Tip Tip)
uBalanceL x l=:(Bin _ lx ll=:(Bin _ _ _ _) Tip) Tip
	= Bin 3 lx ll (Bin 1 x Tip Tip)
uBalanceL x l=:(Bin ls lx ll=:(Bin lls _ _ _) lr=:(Bin lrs lrx lrl lrr)) Tip
	| lrs < ratio*lls
		= Bin (1+ls) lx ll (Bin (1+lrs) x lr Tip)
	# (lrls, lrl) = uSize lrl
	# (lrrs, lrr) = uSize lrr
	| otherwise
		= Bin (1+ls) lrx (Bin (1+lls+lrls) lx ll lrl) (Bin (1+lrrs) x lrr Tip)
uBalanceL x Tip r=:(Bin rs _ _ _)
	= Bin (1+rs) x Tip r
uBalanceL x l=:(Bin ls lx ll=:(Bin lls _ _ _) lr=:(Bin lrs lrx lrl lrr)) r=:(Bin rs _ _ _)
	| ls > delta*rs
		| lrs < ratio*lls
			= Bin (1+ls+rs) lx ll (Bin (1+rs+lrs) x lr r)
		# (lrls, lrl) = uSize lrl
		# (lrrs, lrr) = uSize lrr
		| otherwise
			= Bin (1+ls+rs) lrx (Bin (1+lls+lrls) lx ll lrl) (Bin (1+rs+lrrs) x lrr r)
	| otherwise
		= Bin (1+ls+rs) x l r
uBalanceL x l=:(Bin ls _ _ _) r=:(Bin rs _ _ _)
	= Bin (1+ls+rs) x l r

// adapted from Data.Set to work with a single specific type, and persist uniqueness
uBalanceR :: .a !u:(Set .a) !v:(Set .a) -> w:(Set .a), [v u <= w]
uBalanceR x Tip Tip
	= Bin 1 x Tip Tip
uBalanceR x Tip r=:(Bin _ _ Tip Tip)
	= Bin 2 x Tip r
uBalanceR x Tip r=:(Bin _ rx Tip rr=:(Bin _ _ _ _))
	= Bin 3 rx (Bin 1 x Tip Tip) rr
uBalanceR x Tip r=:(Bin _ rx (Bin _ rlx _ _) Tip)
	= Bin 3 rlx (Bin 1 x Tip Tip) (Bin 1 rx Tip Tip)
uBalanceR x Tip r=:(Bin rs rx rl=:(Bin rls rlx rll rlr) rr=:(Bin rrs _ _ _))
	| rls < ratio*rrs
		= Bin (1+rs) rx (Bin (1+rls) x Tip rl) rr
	# (rlls, rll) = uSize rll
	# (rlrs, rlr) = uSize rlr
	| otherwise
		= Bin (1+rs) rlx (Bin (1+rlls) x Tip rll) (Bin (1+rrs+rlrs) rx rlr rr)
uBalanceR x l=:(Bin ls _ _ _) Tip
	= Bin (1+ls) x l Tip
uBalanceR x l=:(Bin ls _ _ _) r=:(Bin rs rx rl=:(Bin rls rlx rll rlr) rr=:(Bin rrs _ _ _))
	| rs > delta*ls
		| rls < ratio*rrs
			= Bin (1+ls+rs) rx (Bin (1+ls+rls) x l rl) rr
		# (rlls, rll) = uSize rll
		# (rlrs, rlr) = uSize rlr
		| otherwise
			= Bin (1+ls+rs) rlx (Bin (1+ls+rlls) x l rll) (Bin (1+rrs+rlrs) rx rlr rr)	
	| otherwise
		= Bin (1+ls+rs) x l r
uBalanceR x l=:(Bin ls _ _ _) r=:(Bin rs _ _ _)
	= Bin (1+ls+rs) x l r
		
primes :: [Int]
primes =: [2: [i \\ i <- [3, 5..] | let
		checks :: [Int]
		checks = TakeWhile (\n . i >= n*n) primes
	in All (\n . i rem n <> 0) checks]]

primePrefixes :: [[NumberType]]
primePrefixes =: (Scan removeOverlap [|] [toString p \\ p <- primes])

removeOverlap :: !u:[NumberType] NumberType -> v:[NumberType], [u <= v]
removeOverlap [|] nsub = [|nsub]
removeOverlap [|h: t] nsub
	| indexOf h nsub <> -1
		= removeOverlap t nsub
	| nsub > h
		= [|h: removeOverlap t nsub]
	| otherwise
		= [|nsub, h: Filter (\s = indexOf s nsub == -1) t]

tryMerge :: !NumberType !NumberType -> .Maybe .NumberType
tryMerge a b = first_prefix (max (size a - size b) 0)
where
	sa = size a - 1
	max_len = min sa (size b - 1)
	first_prefix :: !Int -> .Maybe .NumberType
	first_prefix n
		| n > max_len
			= Nothing
		| b%(0,sa-n) == a%(n,sa)
			= Just (a%(0,n-1) +++. b)
		| otherwise
			= first_prefix (inc n)

mergeString :: !NumberType !NumberType -> .NumberType
mergeString a b = first_prefix (max (size a - size b) 0) 
where
	sa = size a - 1
	first_prefix :: !Int -> .NumberType
	first_prefix n
		| b%(0,sa-n) == a%(n,sa)
			= a%(0,n-1) +++. b
		| n == sa
			= a +++. b
		| otherwise
			= first_prefix (inc n)
	
// todo: keep track of merges that we make independent of the resulting whole number
mapCandidatePermsSt :: ![[NumberType]] !u:(Set .NumberType) -> v:(Set NumberType), [u <= v]
mapCandidatePermsSt [|] returnSet = returnSet
mapCandidatePermsSt [h:t] returnSet
	#! (mem, returnSet) = uInsertMSpec (foldl mergeString "" h) returnSet
	= let merges = [removeOverlap h y \\ [x:u=:[_:v]] <- tails h, (Just y) <- Map (tryMerge x) v ++| Map (flip tryMerge x) u]
	in (mapCandidatePermsSt t o if(mem) id (mapCandidatePermsSt merges)) returnSet

containmentNumbersSt =: Tl (containmentNumbersSt` primePrefixes "")
where
	containmentNumbersSt` [p:pref] prev
		| all (\e = indexOf e prev <> -1) p
			= [prev: containmentNumbersSt` pref prev]
		| otherwise
			#! next = uFindMin (mapCandidatePermsSt [p] Tip)
			= [next: containmentNumbersSt` pref next]

minFinder :== (\a b = let sa = size a; sb = size b in if(sa == sb) (a < b) (sa < sb))

Start = [(i, ' ', n, "\n") \\ i <- [1..] & n <- containmentNumbersSt]

Try it online!

Save to main.icl and compile with: clm -fusion -b -IL Dynamics -IL StdEnv -IL Platform main

This produces a file a.out which should be run as a.out -h <heap_size>M -s <stack_size>M, where <heap_size> + <stack_size> is the memory that will be used by the program in megabytes.
(I generally set the stack to 50MB, but I rarely have programs use even that much)

Python 3 + Google OR-Tools, score 169 in 295 seconds (official score)

How it works

After discarding redundant primes contained in other primes, draw a directed graph with an edge from each prime to each of its suffixes, with distance zero, and an edge to each prime from each of its prefixes, with distance defined by the number of added digits. We seek the lexicographically first shortest path through the graph starting at the empty prefix, passing through each prime (but not necessarily through each prefix or suffix), and ending at the empty suffix.

For example, here are the edges of the optimal path ε → 11 → 1 → 13 → 3 → 31 → 1 → 17 → ε → 19 → ε → 23 → ε → 29 → ε → 5 → ε for n = 11, corresponding to the output string 113171923295.

graph

Compared to the straightforward reduction to the travelling salesman problem, note that by connecting the primes indirectly through these extra suffix/prefix nodes, instead of directly to each other, we have dramatically reduced the number of edges we need to consider. But since the extra nodes need not be traversed exactly once, this is no longer an instance of TSP.

We use the incremental CP-SAT constraint solver of Google OR-Tools, first to minimize the total length of the path, then to minimize each group of added digits in order. We initialize the model with only local constraints: each prime precedes one suffix and succeeds one prefix, while each suffix/prefix precedes and succeeds the same number of primes. The resulting model could contain disconnected cycles; if so, we add additional connectivity constraints dynamically and rerun the solver.

Code

import multiprocessing
from ortools.sat.python import cp_model


def superstring(strings):
    def gen_prefixes(s):
        for i in range(len(s)):
            a = s[:i]
            if a in affixes:
                yield a

    def gen_suffixes(s):
        for i in range(1, len(s) + 1):
            a = s[i:]
            if a in affixes:
                yield a

    def solve():
        def find_string(s):
            found_strings.add(s)
            for i in range(1, len(s) + 1):
                a = s[i:]
                if (
                    a in affixes
                    and a not in found_affixes
                    and solver.Value(suffix[s, a])
                ):
                    found_affixes.add(a)
                    q.append(a)
                    break

        def cut(skip):
            model.AddBoolOr(
                skip
                + [
                    suffix[s, a]
                    for s in found_strings
                    for a in gen_suffixes(s)
                    if a not in found_affixes
                ]
                + [
                    prefix[a, s]
                    for s in unused_strings
                    if s not in found_strings
                    for a in gen_prefixes(s)
                    if a in found_affixes
                ]
            )
            model.AddBoolOr(
                skip
                + [
                    suffix[s, a]
                    for s in unused_strings
                    if s not in found_strings
                    for a in gen_suffixes(s)
                    if a in found_affixes
                ]
                + [
                    prefix[a, s]
                    for s in found_strings
                    for a in gen_prefixes(s)
                    if a not in found_affixes
                ]
            )

        def search():
            while q:
                a = q.pop()
                for s in prefixed[a]:
                    if (
                        s in unused_strings
                        and s not in found_strings
                        and solver.Value(prefix[a, s])
                    ):
                        find_string(s)
            return not (unused_strings - found_strings)

        while True:
            if solver.Solve(model) != cp_model.OPTIMAL:
                raise RuntimeError("Solve failed")

            found_strings = set()
            found_affixes = set()
            if part is None:
                found_affixes.add("")
                q = [""]
            else:
                part_ix = solver.Value(part)
                p, next_affix, next_string = parts[part_ix]
                q = []
                find_string(next_string)
            if search():
                break

            if part is not None:
                if part_ix not in partb:
                    partb[part_ix] = model.NewBoolVar("partb%s_%s" % (step, part_ix))
                    model.Add(part == part_ix).OnlyEnforceIf(partb[part_ix])
                    model.Add(part != part_ix).OnlyEnforceIf(partb[part_ix].Not())
                cut([partb[part_ix].Not()])
                if last_string is None:
                    found_affixes.add(next_affix)
                else:
                    find_string(last_string)
                q.append(next_affix)
                if search():
                    continue

            cut([])

    solver = cp_model.CpSolver()
    solver.parameters.num_search_workers = 4
    affixes = {s[:i] for s in strings for i in range(len(s))} & {
        s[i:] for s in strings for i in range(1, len(s) + 1)
    }
    prefixed = {}
    for s in strings:
        for a in gen_prefixes(s):
            prefixed.setdefault(a, []).append(s)
    suffixed = {}
    for s in strings:
        for a in gen_suffixes(s):
            suffixed.setdefault(a, []).append(s)
    unused_strings = set(strings)
    last_string = None
    part = None

    model = cp_model.CpModel()
    prefix = {
        (a, s): model.NewBoolVar("prefix_%s_%s" % (a, s))
        for a in affixes
        for s in prefixed[a]
    }
    suffix = {
        (s, a): model.NewBoolVar("suffix_%s_%s" % (s, a))
        for a in affixes
        for s in suffixed[a]
    }
    for s in strings:
        model.Add(sum(prefix[a, s] for a in gen_prefixes(s)) == 1)
        model.Add(sum(suffix[s, a] for a in gen_suffixes(s)) == 1)
    for a in affixes:
        model.Add(
            sum(suffix[s, a] for s in suffixed[a])
            == sum(prefix[a, s] for s in prefixed[a])
        )

    length = sum(prefix[a, s] * (len(s) - len(a)) for a in affixes for s in prefixed[a])
    model.Minimize(length)
    solve()
    model.Add(length == solver.Value(length))

    out = ""
    for step in range(len(strings)):
        in_parts = set()
        parts = []
        for a in [""] if last_string is None else gen_suffixes(last_string):
            for s in prefixed[a]:
                if s in unused_strings and s not in in_parts:
                    in_parts.add(s)
                    parts.append((s[len(a) :], a, s))
        parts.sort()
        part = model.NewIntVar(0, len(parts) - 1, "part%s" % step)
        partb = {}
        for part_ix, (p, a, s) in enumerate(parts):
            if last_string is not None:
                model.Add(part != part_ix).OnlyEnforceIf(suffix[last_string, a].Not())
            model.Add(part != part_ix).OnlyEnforceIf(prefix[a, s].Not())
        model.Minimize(part)
        solve()
        part_ix = solver.Value(part)
        model.Add(part == part_ix)
        p, a, last_string = parts[part_ix]
        unused_strings.remove(last_string)
        out += p
    return out


def gen_primes():
    yield 2
    n = 3
    d = {}
    for p in gen_primes():
        p2 = p * p
        d[p2] = 2 * p
        while n <= p2:
            if n in d:
                q = d.pop(n)
                m = n + q
                while m in d:
                    m += q
                d[m] = q
            else:
                yield n
            n += 2


def gen_inputs():
    num_primes = 0
    strings = []

    for new_prime in gen_primes():
        num_primes += 1
        new_string = str(new_prime)
        strings = [s for s in strings if s not in new_string] + [new_string]
        yield strings


with multiprocessing.Pool() as pool:
    for i, out in enumerate(pool.imap(superstring, gen_inputs())):
        print(i + 1, out, flush=True)

Results

Here are the first 1000 prime containment numbers, computed in 1½ days on an 8-core/16-thread system.

  • Fantastic solution! Using the specifics of the problem in a clever way is exactly what I wanted from the answers to this question. I ran it on my laptop just now for an unofficial scoring, and I got to 153 within 5 minutes. I'll give you your official scoring later today, and make sure that your output seems correct. It seems that you are in the lead, congratulations! – maxb Dec 12 at 12:05

Scala, score 137

Edit:

The code here oversimplifies the problem.

Thus, the solution works for many inputs, but not for all.


Original Post:

Basic Idea

Simpler problem

Let's simplify the problem first: We seek for a string containing all \$n\$ first primes, as short as possible. (not necessarily the lowest number)

First, we generate the set of primes, and remove all, that are already substrings of others. Then, we can apply multiple rules, i.e. if there is only one string ending in a sequence and only one starting with that same sequence, we can merge them. Another one would be that if a string starts and ends with the same sequence (as 101 does), we can append/prepend it to another string without changing that's ends. (Those rules only yield under certain conditions, so be careful when to apply them)

If we have no remaining equal ends/starts of strings, we can just concatenate them and have a minimal-length string containing all \$n\$ first primes.

Those rules are not trivial to figure out, but most of the time, they are sufficient to solve this problem in (I think..) \$O(n^4)\$ or less.

There are cases (i.e. in the generation for \$n=128\$), where those rules are not sufficient. There, we have to fall back to an algorithm taking NP time.

The real problem

With the algorithm from above, we can calculate the length \$k\$ of the result. Imagine we had a god-given start of the sequence:

10103..............
     ^ we want to know this digit

Then we can just take our algorithm from the simplified problem to test if there is a sequence starting with 101030, containing all \$n\$ primes and having length \$k\$. If there is, we can continue with the next digit, because the smallest number seeked cannot be bigger than that. If not, we increase the last digit, so we get 101031 and test with that. Starting with the empty string, we can generate the wanted number in \$O(n\cdot\log(n))\times\text{the time for the simpler algorithm}\$.

Thus, if the rules in the algorithm above were always sufficient, the problem would have been shown not to be NP-hard.

The "TSP-solving" part in my program is done only by simplification, if possible (that is possible for the first 127 numbers). (When it is possible, we could translate the tail-recursive findSeq to a loop, so we could prove it not to be NP-hard). It only gets tricky, if simplification is not sufficient, what happens the first time for \$n=128\$.

Try online

Scastie timeouts after 30s, so it stops at \$n\approx75\$
https://scastie.scala-lang.org/Y9aPRusTRY2ve4avaKAsrA

Code

import scala.annotation.tailrec

object Better {
  var primeLength: Int = 3
  var knownLengths: Map[(String,List[String]), Int] = Map()

  def main(args: Array[String]): Unit = {
    val start = System.currentTimeMillis()
    var last = ""
    Stream.from(1).foreach { i =>
      primeLength = primeList(i-1).toString.length
      val pcn = if (last.contains(primeList(i-1).toString)) last else calcPrimeContainingNumber(i)
      last = pcn
      if (System.currentTimeMillis() - start > 300 * 1000) // reached the time limit while calculating the last number, so, discard it and exit
        return
      println(i + ": " + pcn)
    }
  }

  def calcPrimeContainingNumber(n: Int): String = {
    val numbers = relevantNumbers(n)
    generateIntegerContainingSeq(numbers, numOfDigitsRequired(numbers, "X"), "X").tail
  }

  def relevantNumbers(n: Int): List[String] = {
    val primesRaw = primeList.take(n)
    val primes = primesRaw.map(_.toString).foldRight(List[String]())((i, l) => if (l.exists(_.contains(i))) l else i +: l)
    primes.sorted
  }

  @tailrec
  def generateIntegerContainingSeq(numbers: List[String], maxDigits: Int, soFar: String): String = {
    if (numbers.isEmpty)
      return soFar
    val nextDigit = (0 to 9).find(i => numOfDigitsRequired(numbers.filterNot((soFar + i).contains), soFar + i) == maxDigits).get
    generateIntegerContainingSeq(numbers.filterNot((soFar + nextDigit).contains), maxDigits, soFar + nextDigit)
  }

  def numOfDigitsRequired(numbers: List[String], soFar: String): Int = {
    soFar.length +
      knownLengths.getOrElse((soFar.takeRight(primeLength - 1), numbers), {
        val len = findAnySeq(soFar :: numbers).length - soFar.length
        knownLengths += (soFar.takeRight(primeLength - 1), numbers) -> len
        len
      })
  }

  def findAnySeq(numbers: List[String]): String = {
    val tails = numbers.flatMap(_.tails.drop(1).toSeq.dropRight(1)).distinct
      .filter(t => numbers.exists(n1 => n1.startsWith(t) && numbers.exists(n2 => n1 != n2 && n2.endsWith(t)))) // require different strings for start & end
      .sorted.sortBy(-_.length)
    val safeTails = tails.filterNot(t1 => tails.exists(t2 => t1 != t2 && t2.contains(t1))) // all those which are not substring of another tail

    @inline def merge(e: String, s: String, i: Int): String = findAnySeq((numbers diff List(e, s)) :+ (e + s.drop(i)))

    safeTails.foreach { overlap =>
      val ending = numbers.filter(_.endsWith(overlap))
      val starting = numbers.filter(_.startsWith(overlap))
      if (ending.nonEmpty && starting.nonEmpty) {
        if (ending.size == 1 && starting.size == 1 && ending != starting) { // there is really only one way
          return merge(ending.head, starting.head, overlap.length)
        }
        val startingAndEnding = ending.filter(_.startsWith(overlap))
        if (startingAndEnding.nonEmpty && ending.size > 1) {
          return merge(ending.filter(_ != startingAndEnding.head).head, startingAndEnding.head, overlap.length)
        } else if (startingAndEnding.nonEmpty && starting.size > 1) {
          return merge(startingAndEnding.head, starting.filter(_ != startingAndEnding.head).head, overlap.length)
        }
      }
    }

    @inline def startsRelevant(n: String): Boolean = tails.exists(n.startsWith)

    @inline def endsRelevant(n: String): Boolean = tails.exists(n.endsWith)

    safeTails.foreach { overlap =>
      val ending = numbers.filter(_.endsWith(overlap))
      val starting = numbers.filter(_.startsWith(overlap))
      ending.find(!startsRelevant(_)).foreach { e =>
        starting.find(endsRelevant)
          .orElse(starting.headOption) // if there is no relevant starting, take head (ending is already shown to be irrelevant)
          .foreach { s =>
          return merge(e, s, overlap.length)
        }
      }
      ending.find(startsRelevant).foreach { e =>
        starting.find(!endsRelevant(_)).foreach { s =>
          return merge(e, s, overlap.length)
        }
      }
    }
    safeTails.foreach { overlap =>
      val ending = numbers.filter(_.endsWith(overlap))
      val starting = numbers.filter(_.startsWith(overlap))
      return ending
        .flatMap(e => starting.filter(_ != e).map(s => merge(e, s, overlap.length)))
        .minBy(_.length)
    }

    if (tails.nonEmpty)
      throw new Error("that was unexpected :( " + numbers)

    numbers.mkString("")
  }


  // 1k primes
  val primeList = Seq(2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71
    , 73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127, 131, 137, 139, 149, 151, 157, 163, 167, 173
    , 179, 181, 191, 193, 197, 199, 211, 223, 227, 229, 233, 239, 241, 251, 257, 263, 269, 271, 277, 281
    , 283, 293, 307, 311, 313, 317, 331, 337, 347, 349, 353, 359, 367, 373, 379, 383, 389, 397, 401, 409
    , 419, 421, 431, 433, 439, 443, 449, 457, 461, 463, 467, 479, 487, 491, 499, 503, 509, 521, 523, 541
    , 547, 557, 563, 569, 571, 577, 587, 593, 599, 601, 607, 613, 617, 619, 631, 641, 643, 647, 653, 659
    , 661, 673, 677, 683, 691, 701, 709, 719, 727, 733, 739, 743, 751, 757, 761, 769, 773, 787, 797, 809
    , 811, 821, 823, 827, 829, 839, 853, 857, 859, 863, 877, 881, 883, 887, 907, 911, 919, 929, 937, 941
    , 947, 953, 967, 971, 977, 983, 991, 997, 1009, 1013, 1019, 1021, 1031, 1033, 1039, 1049, 1051, 1061, 1063, 1069
    , 1087, 1091, 1093, 1097, 1103, 1109, 1117, 1123, 1129, 1151, 1153, 1163, 1171, 1181, 1187, 1193, 1201, 1213, 1217, 1223
    , 1229, 1231, 1237, 1249, 1259, 1277, 1279, 1283, 1289, 1291, 1297, 1301, 1303, 1307, 1319, 1321, 1327, 1361, 1367, 1373
    , 1381, 1399, 1409, 1423, 1427, 1429, 1433, 1439, 1447, 1451, 1453, 1459, 1471, 1481, 1483, 1487, 1489, 1493, 1499, 1511
    , 1523, 1531, 1543, 1549, 1553, 1559, 1567, 1571, 1579, 1583, 1597, 1601, 1607, 1609, 1613, 1619, 1621, 1627, 1637, 1657
    , 1663, 1667, 1669, 1693, 1697, 1699, 1709, 1721, 1723, 1733, 1741, 1747, 1753, 1759, 1777, 1783, 1787, 1789, 1801, 1811
    , 1823, 1831, 1847, 1861, 1867, 1871, 1873, 1877, 1879, 1889, 1901, 1907, 1913, 1931, 1933, 1949, 1951, 1973, 1979, 1987
    , 1993, 1997, 1999, 2003, 2011, 2017, 2027, 2029, 2039, 2053, 2063, 2069, 2081, 2083, 2087, 2089, 2099, 2111, 2113, 2129
    , 2131, 2137, 2141, 2143, 2153, 2161, 2179, 2203, 2207, 2213, 2221, 2237, 2239, 2243, 2251, 2267, 2269, 2273, 2281, 2287
    , 2293, 2297, 2309, 2311, 2333, 2339, 2341, 2347, 2351, 2357, 2371, 2377, 2381, 2383, 2389, 2393, 2399, 2411, 2417, 2423
    , 2437, 2441, 2447, 2459, 2467, 2473, 2477, 2503, 2521, 2531, 2539, 2543, 2549, 2551, 2557, 2579, 2591, 2593, 2609, 2617
    , 2621, 2633, 2647, 2657, 2659, 2663, 2671, 2677, 2683, 2687, 2689, 2693, 2699, 2707, 2711, 2713, 2719, 2729, 2731, 2741
    , 2749, 2753, 2767, 2777, 2789, 2791, 2797, 2801, 2803, 2819, 2833, 2837, 2843, 2851, 2857, 2861, 2879, 2887, 2897, 2903
    , 2909, 2917, 2927, 2939, 2953, 2957, 2963, 2969, 2971, 2999, 3001, 3011, 3019, 3023, 3037, 3041, 3049, 3061, 3067, 3079
    , 3083, 3089, 3109, 3119, 3121, 3137, 3163, 3167, 3169, 3181, 3187, 3191, 3203, 3209, 3217, 3221, 3229, 3251, 3253, 3257
    , 3259, 3271, 3299, 3301, 3307, 3313, 3319, 3323, 3329, 3331, 3343, 3347, 3359, 3361, 3371, 3373, 3389, 3391, 3407, 3413
    , 3433, 3449, 3457, 3461, 3463, 3467, 3469, 3491, 3499, 3511, 3517, 3527, 3529, 3533, 3539, 3541, 3547, 3557, 3559, 3571
    , 3581, 3583, 3593, 3607, 3613, 3617, 3623, 3631, 3637, 3643, 3659, 3671, 3673, 3677, 3691, 3697, 3701, 3709, 3719, 3727
    , 3733, 3739, 3761, 3767, 3769, 3779, 3793, 3797, 3803, 3821, 3823, 3833, 3847, 3851, 3853, 3863, 3877, 3881, 3889, 3907
    , 3911, 3917, 3919, 3923, 3929, 3931, 3943, 3947, 3967, 3989, 4001, 4003, 4007, 4013, 4019, 4021, 4027, 4049, 4051, 4057
    , 4073, 4079, 4091, 4093, 4099, 4111, 4127, 4129, 4133, 4139, 4153, 4157, 4159, 4177, 4201, 4211, 4217, 4219, 4229, 4231
    , 4241, 4243, 4253, 4259, 4261, 4271, 4273, 4283, 4289, 4297, 4327, 4337, 4339, 4349, 4357, 4363, 4373, 4391, 4397, 4409
    , 4421, 4423, 4441, 4447, 4451, 4457, 4463, 4481, 4483, 4493, 4507, 4513, 4517, 4519, 4523, 4547, 4549, 4561, 4567, 4583
    , 4591, 4597, 4603, 4621, 4637, 4639, 4643, 4649, 4651, 4657, 4663, 4673, 4679, 4691, 4703, 4721, 4723, 4729, 4733, 4751
    , 4759, 4783, 4787, 4789, 4793, 4799, 4801, 4813, 4817, 4831, 4861, 4871, 4877, 4889, 4903, 4909, 4919, 4931, 4933, 4937
    , 4943, 4951, 4957, 4967, 4969, 4973, 4987, 4993, 4999, 5003, 5009, 5011, 5021, 5023, 5039, 5051, 5059, 5077, 5081, 5087
    , 5099, 5101, 5107, 5113, 5119, 5147, 5153, 5167, 5171, 5179, 5189, 5197, 5209, 5227, 5231, 5233, 5237, 5261, 5273, 5279
    , 5281, 5297, 5303, 5309, 5323, 5333, 5347, 5351, 5381, 5387, 5393, 5399, 5407, 5413, 5417, 5419, 5431, 5437, 5441, 5443
    , 5449, 5471, 5477, 5479, 5483, 5501, 5503, 5507, 5519, 5521, 5527, 5531, 5557, 5563, 5569, 5573, 5581, 5591, 5623, 5639
    , 5641, 5647, 5651, 5653, 5657, 5659, 5669, 5683, 5689, 5693, 5701, 5711, 5717, 5737, 5741, 5743, 5749, 5779, 5783, 5791
    , 5801, 5807, 5813, 5821, 5827, 5839, 5843, 5849, 5851, 5857, 5861, 5867, 5869, 5879, 5881, 5897, 5903, 5923, 5927, 5939
    , 5953, 5981, 5987, 6007, 6011, 6029, 6037, 6043, 6047, 6053, 6067, 6073, 6079, 6089, 6091, 6101, 6113, 6121, 6131, 6133
    , 6143, 6151, 6163, 6173, 6197, 6199, 6203, 6211, 6217, 6221, 6229, 6247, 6257, 6263, 6269, 6271, 6277, 6287, 6299, 6301
    , 6311, 6317, 6323, 6329, 6337, 6343, 6353, 6359, 6361, 6367, 6373, 6379, 6389, 6397, 6421, 6427, 6449, 6451, 6469, 6473
    , 6481, 6491, 6521, 6529, 6547, 6551, 6553, 6563, 6569, 6571, 6577, 6581, 6599, 6607, 6619, 6637, 6653, 6659, 6661, 6673
    , 6679, 6689, 6691, 6701, 6703, 6709, 6719, 6733, 6737, 6761, 6763, 6779, 6781, 6791, 6793, 6803, 6823, 6827, 6829, 6833
    , 6841, 6857, 6863, 6869, 6871, 6883, 6899, 6907, 6911, 6917, 6947, 6949, 6959, 6961, 6967, 6971, 6977, 6983, 6991, 6997
    , 7001, 7013, 7019, 7027, 7039, 7043, 7057, 7069, 7079, 7103, 7109, 7121, 7127, 7129, 7151, 7159, 7177, 7187, 7193, 7207
    , 7211, 7213, 7219, 7229, 7237, 7243, 7247, 7253, 7283, 7297, 7307, 7309, 7321, 7331, 7333, 7349, 7351, 7369, 7393, 7411
    , 7417, 7433, 7451, 7457, 7459, 7477, 7481, 7487, 7489, 7499, 7507, 7517, 7523, 7529, 7537, 7541, 7547, 7549, 7559, 7561
    , 7573, 7577, 7583, 7589, 7591, 7603, 7607, 7621, 7639, 7643, 7649, 7669, 7673, 7681, 7687, 7691, 7699, 7703, 7717, 7723
    , 7727, 7741, 7753, 7757, 7759, 7789, 7793, 7817, 7823, 7829, 7841, 7853, 7867, 7873, 7877, 7879, 7883, 7901, 7907, 7919)
}

As Anders Kaseorg pointed out in the comments, this code may return suboptimal (thus, wrong) results.

Results

The results for \$n\in[1,200]\$ match those from japh except for 187, 188, 189, 193.

1: 2
2: 23
3: 235
4: 2357
5: 112357
6: 113257
7: 1131725
8: 113171925
9: 1131719235
10: 113171923295
11: 113171923295
12: 1131719237295
13: 11317237294195
14: 1131723294194375
15: 113172329419437475
16: 1131723294194347537
17: 113172329419434753759
18: 2311329417434753759619
19: 231132941743475375961967
20: 2311294134347175375961967
21: 23112941343471735375961967
22: 231129413434717353759619679
23: 23112941343471735359619678379
24: 2311294134347173535961967837989
25: 23112941343471735359619678378979
26: 2310112941343471735359619678378979
27: 231010329411343471735359619678378979
28: 101031071132329417343475359619678378979
29: 101031071091132329417343475359619678378979
30: 101031071091132329417343475359619678378979
31: 101031071091131272329417343475359619678378979
32: 101031071091131272329417343475359619678378979
33: 10103107109113127137232941734347535961967838979
34: 10103107109113127137139232941734347535961967838979
35: 10103107109113127137139149232941734347535961967838979
36: 1010310710911312713713914923294151734347535961967838979
37: 1010310710911312713713914915157232941734347535961967838979
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  • The shortest common supersequence problem is known to be NP-complete, so a non-backtracking polynomial time algorithm can’t possibly work in all cases, unless its correctness depends on some peculiar property of the distribution of primes (or P = NP). – Anders Kaseorg Dec 10 at 21:28
  • Good to know! But since we have very special sequences (for \$n>>0\$ none of the strings ends in 0,2,4,5,6 or 8, so we can freely swap strings starting with 0,2,4,5,6 or 8 around). With that, we can avoid cycles and -- most of the time (the only exception I found so far is in \$n=128\$, there I need to fall back to a NP-algorithm) -- reduce it to a P-hard problem. Would be interesting to know if those cases only occur a finite number of times (-> P) or not (may be NP). – anselm Dec 10 at 22:21
  • 1
    Given those caveats like “most of the time” and “found so far”, can you explain why we should trust that your output is correct? How can you be sure that one of your local simplifications won’t prevent you from finding the global optimum? – Anders Kaseorg Dec 10 at 23:25
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    For example: if you replace the first three primes with 1234, 3423, 2345, you generate 123453423 instead of the optimal 12342345. – Anders Kaseorg Dec 10 at 23:45
  • 1
    Also, here's a 3-digit problem case: 457, 571, 757 (all primes). findSeq would return 7574571 for this but the shortest length is 457571. So your approach is playing with fire. Upvoted for sheer audacity, though. – japh Dec 11 at 7:39

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