Last year I read the Nature press release reporting on the largest ever mathematical proof, which came in at ~200 TB. This article interested me, not for the solution to the problem itself, which was the boolean Pythagorean Triples problem, but rather the process for proving such a theorem. Of course, computer science began with logic and mathematical proofs, but I was interested to know more about how a modern computational proof was formulated. The paper that the news article references is Solving and verifying the Boolean Pythagorean Triples via cube-and-conquer by M. Hule, O. Kullmann and V. Marek, and is what I will be covering.
The article presents a solution to the Boolean Pythagorean triples problem. A Pythagorean triple is a set of three natural numbers satisfying the following equation:
Pythagorean triples have stirred a lot of interest in the field of mathematics. YouTuber Mathologer recently released an interesting video on proofs for the Pythagorean identity.
The Boolean Pythagorean triple problem asks whether the set of natural numbers up to can be divided into two subsets, each of which contain no Pythagorean triples. The “Boolean” in the name of the problem refers to the fact that each number will belong to one of the two subsets. The proof in this paper demonstrates that the set can be divided in this way into two subsets, but the set , or any larger , cannot.
This problem doesn’t immediately seem that hard to brute force… Only 7825? But the problem lies in the complexity class NP, and the exponential problem of combinatorics means that this is actually very difficult. As Marijn Huele, the author, writes:
“Even if we could place on every particle in the universe a super-computer, and they all would work perfectly together for the whole lifetime of the universe – by far not enough.
It gets a little verbose, but he continues:
Even not if inside every particle we could place a whole universe. Even if each particle in the inner universe becomes again itself a universe, with every particle carrying a super-computer, still by far not enough.”
So no brute forcing. This type of mathematics belongs to the subfield Extermal Combinatorics – which asks “Given that a set must satisfy some property, what is the largest or smallest (i.e., the most extreme) set that can do so?” The authors give another example of extremal combinatorics in the van der Waerden Theorm: which asks for a set , which will be split into subsets, what is the longest arithmetic progression guaranteed to appear. Recall that an arithmetic progression is a progression of numbers a constant difference apart ( or ). It is actually asked backwards, so that . So means that the numbers one to 1132, when split into two subsets, will always have an arithmetic progression six numbers long in one of the subsets. Or rather, for two subsets, in order to guarantee an arithmetic sequence of length six, you need numbers one to 1132.
Back to our Boolean Pythagorean triples problem: the authors note that for the first part of the proof, that we can successfully divide , is easy to show. We just have to find an example. But showing that is impossible, without inspecting all possibilities, is much harder. And thus ends the Introduction section. Phew!
Getting settled in with the Preliminaries
There are quite a few concepts to get your head around for the uninitiated, like myself. We are reducing everything in the proof to boolean formulas. This means all our variables (or rather, literals: this is different to programming terminology) are booleans, and use operations like:
- And ()
- Or ()
- Not ()
- Implication (), meaning if is satisfied, must also be. For example if and are true, then we know must be true, so .
If a formula reduces to 1 (or True) for some set of inputs, it is satisfiable (SAT), otherwise, if no possible set of inputs can make the formula result in True, it is unsatisfiable (UNSAT). Our formulas will be in conjunctive normal form (CNF) – basically we will set up our formulas at the top level to only have Ors, and each or-ed value will consist only of Ands. For example:
So we can think of our formula as a list of clauses, which will be reduced using the AND operation, and each of our clauses as a list of literals, which will be reduced by the OR operation. Another important detail to consider is “what if one of our clauses is an empty list?” If we think of a clause as a list of literals being reduced on the OR operation, then it is easily seen that False is the identity under this operation (anything OR False equals whatever that anything was), so the empty clause, denoted by , evaluates to False.
In order to prove a formula is unsatisfiable, we will add more and more clauses, each of which is consistent with the existing clauses in the formula, until we find a contradiction, or a conflict. This equates to a proof by conflict, and shows that the formula is inconsistent and cannot be satisfied.
As we add clauses in our proof, we have to be sure that the new clauses are consistent with the formula. In our above example for implication, we saw that . Imagine that and and actually clauses, not just literals, . We could see that if the LHS is SAT, it would mean that both and are satisfiable, so we would know then that the RHS is also SAT. So transforming via implication is SAT-preserving. However, if the LHS is UNSAT, then we can’t say whether the RHS is SAT or UNSAT. In order to construct our proofs, we need to ensure that all the clauses are SAT-equivalent, that they don’t change the satisfiability or unsatisfiability of the formula as a whole.
Resolution and Extended Resolution
It is my understanding that resolution was an early technique for automated satisfiability computation, and extended resolution a generalisation; and that the technique used in the proofs in this paper, resolution asymmetric tautology (RAT) is a further generalisation. Therefore, you can safely skip through this section which I believe is just for historical reasons.
Both resolution and extended resolution allow you to add new, consistent and SAT-equivalent clauses to a proof.
In resolution, we can add the resolvent, , of two clauses and , where , , , and is the compliment of . It can be seen that , by considering that for to be true, at least one of and must be true since only one of and is true. We said before that implication is SAT-preserving, and we can convince ourselves that clause addition (by AND) should be UNSAT-preserving, so:
So if we have and in our formula, we can safely add as a part of out proof.
In extended resolution, there is an additional option to add a new variable, . We define the new variable as , where and are literals in the current formula. The definition is added by adding the clauses:
This can be seen to equivalent to defining by considering the two option of being True or False:
However, I am not entirely sure how introducing such clauses can help lead to a contradiction.
If a formula contains clauses with a single literal, they make other clauses within the formula containing the same literal redundant. Consider the following:
In order for the formula to be True, must true, so the value of becomes redundant, as does the entire second clause. If the formula in UNSAT, still the second clause makes no difference. So:
Where means unit propagates to. Similarly, if a formula contains a clause with a single literal, then the compliment of the literal becomes redundant in other clauses, since to be SAT the literal must be True, and so the compliment must be False. So:
The last example shows that this process can be iterated until no further changes are found.
If a conflict is found, where two complimentary unit clauses are produced, the second rule says that the literals can be eliminated from the clauses, leaving empty clauses:
We say that this derives a conflict, or symbolically . Such a formula is UNSAT. In the above formula, , no combination of assigning True or False to and will result in True.
I wrote a simple Python function that is able to perform unit propagation here.
Resolution Asymmetric Tautology
In this work, the authors use a more flexible technique for introducing clauses, called resolution asymmetric tautology, or RAT. They provide a definition for RAT which allows introduction of clauses which needn’t be logically implied by a formula. This introduces a flexible way to add new clauses. Although needn’t be logically implied by the formula, it is required that for all in which , where is the formula, is the clause to be introduced, is a subset of containing , is a pivot element, , is set subtraction, and the negation of a clause, is defined as .
This is all quite a mouthful, I wrote a Python function to implement this check that I find easier to read here.
DRAT and DIMACS
DIMACS CNF is a format
for storing formulae in a plaintext, machine-readable format. The first line is
a header, which is
p cnf <n-variables> <n-clauses>, which defines the number
of variables and clauses. Subsequent lines defines a clauses, each terminated
with a 0. Each value indexes (starting from 1) a variable, and negative values
indicate the compliment of a literal.
The example given in the paper could be worded:
- , and can’t all be the same,
- , and can’t all be the same,
- , , and can’t all be the same,
- and , and can’t all be the same.
This can be captured by the formula:
Or in DIMACS CNF:
p cnf 4 8 1 2 -3 0 -1 -2 3 0 2 3 -4 0 -2 -3 4 0 -1 2 4 0 1 -2 -4 0 1 3 4 0 -1 -3 -4 0
The authors also present a DRAT proof to prove the formula is UNSAT. The proof might say:
- The clause has RAT on w.r.t. .
- NB: The clause in will not play a role in the remainder of this proof, so you may choose to ignore it.
- The clause has RAT on w.r.t. .
- , derives a conflict and is unsatisfiable.
In DRAT format, this proof would be:
-1 0 d -1 2 4 0 2 0 0
It should be highlighted that deletions are not integral to the proof, but just allow acceleration of the verification.
We can manually perform a small amount of the check that the first clause, has RAT. We are working with pivot , which is taken to be the first literal presented of each proof clause. First we find , which is the subset of which contains the pivot, in this case . For each we should assert that . Note that the confusing little part in the brackets just means that we swap the signs of the literals except for the pivot, and swap the ORs to ANDs. We will only manually check for .
This process is repeated for all to ensure that the clause indeed has RAT w.r.t. the formula. After this has been ensured, the clause can be added to the formula and the next clause checked. After all clauses have been checked, the formula should derive a conflict after unit propagation in order to UNSAT to be demonstrated.
Again, a Python implementation of the process can be found here.
This certainly concludes what I found to be the trickiest and most interesting part of the paper (at least to someone outside of the field – it is likely this was actually very fundamental and routine).
Cube and Conquer
The cube and conquer strategy used to solve sounds very similar to the techniques used in dynamic programming. The problem is broken down into chunks using heuristics, which can be solved independently. Actually, the problem is broken down into chunks in two stages: at the first level each chunk is to be solved independently. Each chunk is then broken into smaller chunks, which are solved sequentially so that learned heuristics from previous sub-chunks can be used to accelerate solving.
The framework for solving is divided into five steps. In the first, the problem must be encoded into DIMACS CNF format. But first: how can we encode the problem? We want each of our two subsets of to not contain any entire Pythagorean triple. But another way of thinking of this is that at least one number in every Pythagorean triple should belong to each subset. If we say one subset is values that we assign to True, and one subset is values that we assign to False, then we can use the following encoding for the triple :
Which says “At least one of 3, 4, or 5 must belong to the True subset, and at least on of 3, 4, or 5 must belong to the False subset.” Marijn provides the C code used for this step:
In order to simplify the solving, the authors iteratively remove any clauses where one of the literals only appears twice (the positive and negative form of the clause). Since the literal doesn’t appear in other triples, it can trivially be assigned to ensure the colouring isn’t all the same. They also break the symmetry in which one could swap all True labels with False and vice-versa, by adding a clause that makes the most common literal True.
The details of the heuristics used aren’t focused on in this review. In short, variables are set to True or False, and the importance of the resulting new clauses after unit propagation are estimated. Then a tree is formed which branches on the most important literals, and the cubes produces from this.
The result of the splitting stage are “assumptions”, , which will be used to create the new subproblems, .
Each of the subproblems are fed into a CDCL solver. The details of how these solvers actually determine which possible DRAT clauses should be used to provide efficient refutations of the problems is beyond my current understanding.
Importantly, all of the above steps must be validated to ensure the proof is correct. The encoding phase is manually validated. Transforms and splitting is able to be validated using properties of the results. We covered earlier how the solving stage is able to be validated.
Most of the results presented aren’t of interest to the non-expert, with the obvious exception of the overall results that the Boolean Pythagorean triple problem is solvable for , but not solvable for . The authors do also note an additional result that I believe is interesting.
One of the criticisms of this type of proof is that it does not provide intuition as to why the result is so. Indeed, no human could ever trawl through the 200 TB proof. As we saw earlier, it is a headache to try and understand a single RAT clause. However, the authors did manage to find some intuition as to the results.
They were able to identify a backbone in the proof, a variable that is assigned the same value in all solutions. For , variables for 5180, and 5865 were always True, whereas the variables for 625 and 7800 were always False. However, . So these requirements finally conflict at .
This might not be entirely satisfying, but the authors note that it just might be the case that this is the only explanation and there may be no “human-readable” proof.