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- Hacker News
- > It was much better than I would have written myself, and I ended up learning from it.
Eventually shitting on LLM code will be seen by all as lazy cope. I too am aware of the existence of SAT but I really would struggle to immediately see through some problem i was having and interpret SAT unless I did it a bunch. Having agent suggest the "right thing" is clearly better. And hopefully would help my intuition in the future.
by nh23423fefe - > Instead of backtracking, Claude just imported an industrial-strength library made to solve these sorts of problems. OR-Tools CP-SAT is put out by Google and is made for solving constrained optimization problems, as well as satisfiability problems like this one.
I'm not familiar with CP-SAT, but TTBOMK all SAT solvers use a type of backtracking search underneath called DPLL. Modern ones are highly tuned in terms of which variable they choose to branch on next, and in what order to try its possible values; this can have an enormous impact on runtime. They probably use several tricks on top of that; the big one that I'm aware is conflict-driven clause learning, where the solver adds new constraints that it discovers as it goes along (e.g., it might be able to determine that x and y always have the same value in every solution), which can shrink the search space a lot.
- The first thing that came to my mind was: Tetris with periodic boundary conditions.by analog31
- CP-SAT is still a glorified and optimized recursive backtracking search. There's nothing better in the literature. They call it DPLL.
It just evaluates the data and constraints to search the more obvious paths first, and cut illegal paths earlier. And some integer tricks, and a cache of learned constraints. Which the Russians found, when trying to solve Chess efficiently. IBM/Ken Thompson just threw hardware at it, while the Russians made the algorithmic advances. Just as now with LLM's. The US folks just throw hardware at it, whilst the Chinese optimize their algos.
by rurban - CP here being Constraint Problem, or CSP (Constraint Satisfaction Problem).
https://en.wikipedia.org/wiki/Constraint_satisfaction_proble...
by emil-lp - Reinventing the wheel is not always to be avoided. Finding an interesting wheel and trying to build your own equivalent can be an excellent way to improve your skills as a programmer [1].
"Feed it to SAT solver" is a super useful technique every seasoned programmer should have in their toolbelt.
If you want to improve your programming skill, I suggest implementing your own version of these two interesting, satisfying algorithms:
- DPLL, the classic general-purpose SAT solving algorithm [3].
- Algorithm X (aka Dancing Links), Knuth's super elegant search algorithm for solving exact cover problems [4] [5].
You're not going to build something that beats CaDiCaL [6] in an afternoon, but writing your own implementation of these algorithms will teach you a lot, and a reasonable time investment will get you to a pretty satisfying endpoint of having a practical solver for your favorite application (Sudoku, polyomino packing, etc.)
[1] Reinventing wheels is not a bad way to spend your days at a retreat focusing on one's personal growth as a programmer. "I think I'll learn a lot and get some good practice at programming and algorithmic thinking" is an easy bar to meet.
In a professional context, telling your company or client they should commit scarce, expensive engineering resources (e.g. your time) to creating and maintaining their own wheel design requires justification [2].
[2] "Don't reinvent wheels" is good general advice for junior developers. Senior developers know that sometimes there are specific situations where this general advice doesn't apply. For example, if no existing wheels meet your application's requirements, or if you have ideas for proprietary features that give you a competitive advantage. Even in a professional setting, reinventing the wheel isn't always a bad idea; you just need to meet a much higher bar to justify the commitment of resources.
[3] https://en.wikipedia.org/wiki/DPLL_algorithm
[4] https://arxiv.org/abs/cs/0011047
by csense - Ok, in this specific case, a puzzle with what 10 pieces, a recursive backtracker will be just as fast and more importantly, be far easier to reason about and implement.
If this was a thousand piece puzzle, I would still venture recursive backtracker with good heuristics will beat CP-SAT, even in the sudoku case some good heuristics with backtracking beats CP-SAT. Not sure why Claude immediately jumped to using CP-SAT.
by sashank_1509 - Back in the day BYTE magazine had a puzzle contest - solve the 8-queens problem and find all the solutions.
As a kid, it seemed obvious to me that a good model was the digits 1 through 8, representing the row that a queen would be in. Doing a recursive backtracker on an ordering of the digits and testing each pair for diagonal (digit i minus digit j was equal to +/- i-j) and my BASIC program spit out the solutions in twenty minutes.
I didnt enter the contest because I figured everybody would do that. Turns out when the editor published the 'best' solutions, they all, every single one, modeled the board as an 8 by 8 array and ran around following diagonals iteratively. And took hours to complete.
Anyway, choosing a good solution model is most of the problem solved.
by JoeAltmaier