clpz
HiGHS
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clpz
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Logic programming is overrated, at least for logic puzzles (2013)
As pointed out in the comments in the article, these kinds of logic puzzles are easier to solve using constraint programming than "regular" logic programming.
For example, see the solution to the Zebra Puzzle here: https://www.metalevel.at/prolog/puzzles which uses CLPZ[^1].
[^1]: https://github.com/triska/clpz
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Markus Triska Interview on Prolog
Scryer has the strings-as-lists-but-implemented-efficiently thing, possibly more strict ISO Prolog compatible syntax, and it may ship with a more advanced constraint library (I'm not clear on the relationship between SWI's clpfd and Scryer clpz).
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is it possible to have a reversable operation
None of these are full-fledged programming languages, however. They're limited to problems that lie in the polynomial hierarchy (A class which contains P and NP). Logic programming is generally only used to solve hard problems for which no good algorithm is known. Prolog also sort of fits this niche and it has a bunch of solvers integrated into it. Notably CLPFD which uses https://github.com/triska/clpz for constraint logic programming. Rosette (https://docs.racket-lang.org/rosette-guide/index.html) is another solver-based language. Except it uses lisp syntax (it's embedded in the Racket language). It uses Z3 as a solver (linked above for SMT theories)
- Ask HN: Do you use an optimization solver? Which one? Why? Do you like it?
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What is the difference between constraint solving and constraints programming?
Constraint programming I guess is when one uses a prolog library such as: https://github.com/triska/clpz
HiGHS
- Algorithms - Researchers Approach New Speed Limit for Seminal Problem
- HiGHS: High performance open source MILP and QP solver
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Ask HN: Do you use an optimization solver? Which one? Why? Do you like it?
I've been using CBC via python-mip (https://github.com/coin-or/python-mip). It's great because it's got a super clean interface (milp variables/expressions/constraints), the code is quite accessible, and it's low overhead which makes it good for solving many very small problems.
Community sentiment seems to be beginning to shift toward favouring the HiGHS solver (https://github.com/ERGO-Code/HiGHS) over CBC. Something I'm keeping a close eye on.
nextmv seems to pitch itself as a generic solving ("decision automation") platform or something (unclear). But it seems that the only fleshed out product offering is for vehicle routing, based on the docs. Are there plans to offer, for instance, a solver binary that can be used to solve generic problems?
Also all the github repos under https://github.com/nextmv-io are private, so links from docs are 404.
What are some alternatives?
prolog-checkers - A Player vs AI game of checkers implemented in Prolog
or-tools - Google's Operations Research tools:
SSI - A Prolog Compiler written in Prolog.
OptaPlanner - Java Constraint Solver to solve vehicle routing, employee rostering, task assignment, maintenance scheduling, conference scheduling and other planning problems.
kanren - An extensible, lightweight relational/logic programming DSL written in pure Python
osqp - The Operator Splitting QP Solver
csips - A pure-python integer programming solver
exact
optaplanner-quickstarts - Mirror of https://github.com/apache/incubator-kie-optaplanner-quickstarts
SciPy - SciPy library main repository