At some point it may be switched out with an alternate SAT implementation, but I suspect SAT in general is very much destined to stay an integral part of conda. From my experience many instances appearing in practice are easy to solve even if there very large. The problem with these instances is that it takes to much space to write done the SAT-formual. So you do not even get to the point where you would run the solver.
To understand what this means you need a clear notion of Complexity classes.
Here is a short rundown: P is the class of all problems which can be solved in polynomial time i. NP is the class of all problem for which a solution can be verified in polynomial time. This means veryfing a given solution is fast, but finding one is usually slow most often exponential time.
Then there is the set of NP-Complete problems. This set contains all problem which are so generic, you can solve these Problems instead of another one from NP this is called reducing a problem onto another. This means you can transform a problem from one domain into another NP-Complete problem, have it derive an answer and transform the answer back.
Often however it can be proven that a problem is NP-Complete, but the transformations are unclear for another given problem. So, yes, SAT can be used for all these problems you are mentioning. Often however this is not feasible. Where it is feasible is, when no other fast algorithm is known, such as the puzzle you mention.
In this case you do not have to develop an algorithm for the puzzle, but can use any of the highly optimized SAT-Solvers out there and you will end up with a reasonable fast algorithm for your puzzle.
Traversing a tree structure is so simple for example, that any transformation from and to SAT will most likely be much more complex than just writing the traversal directly.My SAT solver fuzzing setup August 25, Development, SAT, Tools bug, fuzzing, library msoos Since the SAT solver fuzzing paper by Brummayer, Lonsing and Biere, SAT solver fuzzing has been quite the thing in the SAT community.
SAT Game - try solving a Boolean satisfiability problem yourself SAT problem format. A SAT problem is often described in the DIMACS-CNF format: an input file in which each line represents a single disjunction.
For example, a file with the two lines 1 -5 4 0 -1 5 3 4 0 represents the formula "(x 1 ∨ ¬x 5 ∨ x 4) ∧ (¬x 1 ∨ x 5 ∨ x 3 ∨ x 4)". An Efﬁcient and Trustworthy Theory Solver for Bit-vectors in Satisﬁability Modulo Theories by Liana Hadarean A dissertation submitted in partial fulﬁllment of the requirements for the degree of the internals of a SAT solver as well as for being a great ofﬁce-mate.
As SAT-algorithms become more and more complex, there is little chance of writing a SAT-solver that is free of bugs.
So it is of great importance to be able to verify the information returned by a SAT-solver. 2clseq-A DPLL Solver Using Extensive Binary Clause Reasoning 2clseq is a DPLL SAT solver that utilizes extensive reasoning with the binary clause subtheory at every node of the search tree.
Since this reasoning is performed dynamically many more opportunities arise as we descend the search tree. Fast Exact Solvers for Chromatic Number. Ask Question.
You might want to try to use a SAT solver or a Max-SAT solver. I expect that they will work better than a reduction to an integer program, since I think colorability is closer to satsfiability. You also need clauses to ensure that each edge is proper. Then you just do a binary.