philzook
Refinement E-Graphs
Basically all optimizing compilers have a simplifier or rewriter in them. I don't see it as a requirement or even necessarily a priori desirable to frame their discussion or formulation in terms of high barrier mathematical language. Do so if it is fun or useful. Sometimes it is. It is to my subjective taste to do so. Compiler writers are a pretty clever group by and large and are aware of a decent amount of useful math.
There is also a tendency to underestimate where 10 years of study and effort applied to a subject can bring you and attribute it totally to some intrinsic intelligence.
Term rewriting _is_ really neat. Realizing and remembering what you find neat and exciting is important. I like equations.
Many applications are naturally expressed using bound variables (lambdas, summation expressions, einstein indices, integrals, loops) but the basic e-graph doesn't really support the concept. One can instead model using combinators (SKI combinators, relation algebra, categorical combinators, other) but this tends to be not entirely natural and tends to explodes in the search space of different ways to encode the same concept using combinators.
Lifting e-graphs are a variant of sorts of slotted e-graphs, which are techniques to supports binders in egraphs. It's more subtle to do so than one might think. Lambda microegg also adds a surface syntax to play around more easily.
Lambda MicroEgg
I also had some fun modelling the chips in verilog and model checking a verilog interpreter against them. https://www.philipzucker.com/td4_ebmc/ George Rennie got a similar thing working using the yosys toolchain https://github.com/georgerennie/philip_zucker_sby_demo
Yesterday I proved the infinitude of primes, which I was pretty happy with. https://www.philipzucker.com/knuckle_primes/ A trivial theorem in the scheme of things, but one for which z3 certainly can't do it on it's own.
My maybe inaccurate understanding of recursive CTEs was that they only allow linear occurrences of the recursively defined relation in the query and do not allow many mutually recursively defined relations. These might be per implementation restrictions. These are pretty harsh restrictions from a datalog perspective. I should explore what Postgres offers more. It's just so easy to try stuff out on sqlite.
Compositional Datalog on SQL: Relational Algebra of the Environment
A Python CLI for Verifying Assembly
I'll note there is a really shallow version of naive datalog I rather like if you're willing to compromise on syntax and nonlinear variable use.
edge = {(1,2), (2,3)}
path = set()
for i in range(10):
# path(x,y) :- edge(x,y).
path |= edge
# path(x,z) :- edge(x,y), path(y,z).
path |= {(x,z) for x,y in edge for (y1,z) in path if y == y1}
Similarly it's pretty easy to hand write SQL in a style that looks similar and gain a lot of functionality and performance from stock database engines. https://www.philipzucker.com/tiny-sqlite-datalog/I wrote a small datalog from the Z3 AST to sqlite recently along these lines https://github.com/philzook58/knuckledragger/blob/main/kdrag...
A Python Frozenset Interpretation of Dependent Type Theory
"Verified" "Compilation" of "Python" with Knuckledragger, GCC, and Ghidra
A Small Prolog on the Z3 AST
But to be a bit more specific, I've been involved in the egraphs community https://github.com/philzook58/awesome-egraphs and we don't currently have a shared database of rewrite rules for benchmarking, nor do we collect the rules from different projects. Seeing the actual files is helpful.
On a slightly different front, I'm also trying to collect rules or interesting theories up in my python interactive theorem prover Knuckledragger https://github.com/philzook58/knuckledragger . Rewrite rule or equational theory looking things are easier to work with than things with deeply nested notions of quantifiers or tons of typeclass / mathematical abstraction like you find when you try to translate out of Coq, Lean, Isabelle.
There are also different approaches to declarative rewrite rules in major compilers. GCC, LLVM, and cranelift have their own declarative rewrite rule systems for describing instruction selection and peephole optimizations but also of course lots of code that programatically does this. I also want to collate these sorts of things. Working on fun clean systems while not confronting the ugly realities of the real and useful world is ultimately empty feeling. Science is about observing and systematizing. Computer science ought to include occasionally observing what people actually do or need.
The question at hand is motivating and getting benchmarks for different approaches or engines to equational reasoning / rewriting. I sometimes lose sight of motivations and get discouraged. Doing associativity and commutativity and constant fusion for the nth time I start to become worried it's all useless.
I was surprised and intrigued to find Wolfram's name when I was reading background literature doing this blog post https://www.philipzucker.com/cody_sheffer/ where I translated my friend's Lean proof of the correspondence of Sheffer stroke to Boolean algebra in my python proof assistant knuckledragger https://github.com/philzook58/knuckledragger (an easier result than Wolfram's single axiom one).
I was also intrigued reading about the equational theorem prover Waldmeister https://www.mpi-inf.mpg.de/departments/automation-of-logic/s... that it may have been acquired by Mathematica? It's hard to know how Mathematica's equational reasoning system is powered behind the scenes.
Mathematica can really make for some stunning visuals. Quite impressive.