# On experimental mathematics

The term “experimental mathematics” refers to a contemporary practice in mathematics, where computers are used to inspire research. This can mean, among others:

• finding examples and counterexamples to definitions or conjectures,
• inspiring new conjectures and ensuring that no small counterexamples exist,
• informing proof strategies by counterexamples.

The last point sounds a bit vague. I hope to clarify it in the next section and then give examples of a strange new phenomenon in the use of the advanced mathematical software tools which we have today:

Sometimes the software is much smarter than the user and by the speed with which it refutes hand-crafted potential counterexamples, it apparently contains a fitting heuristic in its source code. This heuristic might translate into a proof of the conjecture — but the software doesn’t tell you what it is!

This is as marvellous an achievement of human engineering as it is frustrating if you can’t extract the heuristic from the source code and learn the missing part of math to prove your conjecture.

Note: I will generally assume that the software works correctly. Certifiability of yes/no answers found by software is certainly related but it is a bigger can of worms than what I want to point out here.

## Positive-definite points on certain varieties of matrices

My colleague Andreas Kretschmer and I have been working on a question asked by Aida Maraj about two weeks ago. I don’t want to go into too many details but it concerns solutions to certain systems of polynomial equations over the positive-definite matrices.

One thing I like to use computers for is to assess which parts of the conjecture are necessary. That means, when we remove certain assumptions from the conjecture, do we find an easy counterexample? Such data points about the conjecture tell us which kinds of proof methods are doomed to fail. For example, is positive definiteness necessary? A real symmetric matrix is positive-definite if and only if all of its principal minors are positive. A relatively natural generalization of this requires that all principal minors are non-zero. This condition is called principal regularity.

The reason for this relaxation, which might not be obvious to the non-algebraist, is that we switch to an easier theory. With positive definiteness, the set of counterexamples to Aida’s conjecture is a semialgebraic set. These are theoretically tractable on a computer, but we have already ruled out all potential counterexamples which are small enough for these general methods to terminate in any reasonable amount of time. But with principal regularity, all polynomial constraints are either equations or inequations; there are no inequalities anymore. Hence, we can put the problem into the algebraically closed complex numbers and the set of counterexamples becomes constructible. Constructible sets tend to be somewhat easier to deal with on a compuer than semialgebraic ones and we can indeed explore some bigger examples — hoping that among the counterexamples we may find, there is one which transfers back into the real numbers of our original conjecture.

As it turns out, we can find counterexamples to the relaxed conjecture where positive definiteness is replaced by principal regularity. Here is the most impressive counterexample I managed to find. After a long computation in Macaulay2, we find that the Zariski closure of the constructible set we are interested in, which contains the principally regular counterexamples to the conjecture, is described by the prime ideal generated by the following polynomials on six variables $x_1, \dots, x_6$:

$x_2 x_4+6 x_1 x_6-x_2 x_6, \\ 28 x_4^2+21 x_1 x_6+21 x_2 x_6+8 x_3 x_6-8 x_4 x_6-48 x_5 x_6+28 x_6^2, \\ 28 x_1 x_4-24 x_3 x_5+38 x_1 x_6+18 x_2 x_6+32 x_3 x_6+3 x_4 x_6-24 x_5 x_6+3 x_6^2, \\ 4 x_3^2-24 x_3 x_5+21 x_1 x_6+21 x_2 x_6+28 x_3 x_6+3 x_4 x_6-24 x_5 x_6+3 x_6^2, \\ 73794 x_2 x_5-1510376 x_3 x_5-36015 x_4 x_5-82320 x_5^2+1338582 x_1 x_6+1275030 x_2 x_6+1768998 x_3 x_6+179193 x_4 x_6-1824711 x_5 x_6+189987 x_6^2+8873205, \\ 73794 x_1 x_5-1510376 x_3 x_5+37779 x_4 x_5-82320 x_5^2+1264788 x_1 x_6+1348824 x_2 x_6+1832250 x_3 x_6+189735 x_4 x_6-1835253 x_5 x_6+179445 x_6^2+8873205, \\ 12299 x_3 x_4-536256 x_3 x_5+294 x_4 x_5+169344 x_5^2+421596 x_1 x_6+425010 x_2 x_6+626563 x_3 x_6+72030 x_4 x_6-891114 x_5 x_6+72114 x_6^2+2957735, \\ 12299 x_2 x_3-1510376 x_3 x_5-36015 x_4 x_5-82320 x_5^2+1338582 x_1 x_6+1348824 x_2 x_6+1842792 x_3 x_6+179193 x_4 x_6-1824711 x_5 x_6+189987 x_6^2+8873205, \\ 12299 x_1 x_3-2046632 x_3 x_5+38073 x_4 x_5+87024 x_5^2+1760178 x_1 x_6+1773834 x_2 x_6+2471112 x_3 x_6+261765 x_4 x_6-2726367 x_5 x_6+251559 x_6^2+11830940, \\ 24598 x_2^2+451920 x_3 x_5-86387 x_4 x_5-169344 x_5^2-358344 x_1 x_6-361758 x_2 x_6-543984 x_3 x_6-49189 x_4 x_6+819077 x_5 x_6-73871 x_6^2-2957735, \\ 24598 x_1 x_2-620592 x_3 x_5+86387 x_4 x_5+169344 x_5^2+484848 x_1 x_6+488262 x_2 x_6+712656 x_3 x_6+70273 x_4 x_6-987749 x_5 x_6+94955 x_6^2+2957735, \\ 344372 x_1^2-10833312 x_3 x_5+1554966 x_4 x_5+3048192 x_5^2+8732535 x_1 x_6+8793987 x_2 x_6+12926200 x_3 x_6+1222746 x_4 x_6-18032490 x_5 x_6+1667022 x_6^2+53239230.$

This might look scary, but Mathematica finds a generic point in this variety in about two seconds on my laptop, which is indeed principally regular. The solution coordinates look something like this, involving invocations to Mathematica’s Root function, which represents real algebraic numbers exactly by univariate polynomials and an index of the root to pick.

x1 -> -(
    (274690496000177606838447793146325447500965895245837010731843554043039615306319
     Root[
       1775380433750000000 - 78478332364687500000 #1^2 +
       891090422891055078125 #1^4 - 849232777556096718750 #1^6 +
       871885597934472187500 #1^8 - 643355496779169825000 #1^10 +
       96277889059372440000 #1^12 - 5847789209959296000 #1^14 +
       235403285409331200 #1^16 - 6977138838405120 #1^18 +
       95569451679744 #1^20 &, 1]) /
     6728554820402784834716554295293520236450118616607950097000909229587745883956)
     + ...


There are ten such summands in the solution coordinate $x_1$. It looks similar for the other coordinates. Thus our system has a solution which is principally regular. If we replace the principal regularity constraints by positive definiteness constraints and call FindInstance, then Mathematica knows after five seconds that there exists no solution. So, our search for a counterexample was not successful: we were able to check a promising example of larger size by relaxing positive definiteness to an algebraic condition, but the counterexamples to the relaxation yielded no counterexample to the original conjecture, in this case.

This tells the experimental mathematician two things. First, if the conjecture is true, positive definiteness is necessary but the algebraic version of it, principal regularity, does not suffice. The conjecture cannot be proved by mere algebraic manipulation of the equations alone. Positivity has to be used at some point, because otherwise the conjecture would be true in the principally regular setting, too.

Second, experience tells that a system of eleven equations of degree 2 in six variables plus positive definiteness constraints are never ever solved in five seconds by general methods like Positivstellensatz, CAD or Gröbner bases. Mathematica needs to have a heuristic whose preconditions are quick to verify and which leads to an insolvability proof very quickly. And, by the way, the same pattern should apply to the principally regular system as well. Mathematica must have had a systematic way of finding these insane algebraic numbers shown above.

This gives us hope that the conjecture might be true. The situation presents itself to me as if some mathematician or engineer at Wolfram Inc. figured out a pattern of polynomial systems once which implies structure on the solution set which can be used to produce a solution quickly or prove that none exists. This pattern “obviously” applies to our system, but we have no idea what it is! We only know that there is a (proprietary!) software which knows. But of course, it could also be that our example was just not big enough to yield a real counterexample because whatever structure is exploited here is not always present in the situation of our conjecture. This situation would be easier to judge if we knew what Mathematica saw that made this computation so easy.

## Orientability of LUBF-gaussoids

But this is not a nuisance of proprietary software alone. A while ago I computed the orientability of LUBF-gaussoids on ground sets of size up to eight. I didn’t talk about it in the article, because it was about the symmetry reduction, but the same phenomenon of software seeing things the human doesn’t occurred in the stage that actually decided orientability. At the end of the symmetry reduction, there were 222 LUBF-gaussoids, which means 222 Boolean formulas for orientability, on average in 546 variables and with 33335 clauses. The venerable MiniSAT solved them in total time of five seconds. This includes startup time, parsing the formulas and output processing in my shell script — all done sequentially. MiniSAT can obviously tell extremely, extremely quickly which of these formulas have a solution.

Experience tells that this cannot happen unless the SAT solver contains a fitting heuristic. The good news is that MiniSAT is open source software, so we can look inside. The bad news is that it does not contain much more than a “metaheuristic”. MiniSAT is a rather clean solver, written for educational purposes and reusability, but it also happens to be really fast. It does not contain a library of special-case heuristics for formulas of a certain structure, but implements conflict-driven clause learning. According to the MiniSAT paper, the solver explores the space of all assignments to the variables and tries to deduce, or learn, more information about the formula from (partial) assignments which fail to satisfy it.

Thus, the source code is of no great help in finding why LUBF leads to apparently easy orientability problems. The solver is intelligent enough on its own to discover the reason in realtime by just trying out some assignments. I, for one, still don’t know the reason. I suppose if I really wanted to, I could inspect the database of learnt clauses and try to infer which special structure the solver discovered. That’s a plus for free software.

In conclusion, I want to emphasize that, compared to mathematical practice at large, this is a completely new phenomenon brought to us by the extensive collaboration and expertise that goes into today’s advanced mathematical software.

Addendum (29 Sep 2021): The question I mentioned above turned out to have an affirmative answer for positive-definite matrices (although it is false for principally regular real matrices). See Theorem 3.23 of The geometry of Gaussian double Markovian distributions.