The Grand Unification crate — all 14 executable theorems project from one spectral triple (A, H, D)
Symbolic logic unification using Most General Unifiers (MGU). Implements Meredith's condensed detachment for exploring automated proof discovery.
Workspace hack management and package/feature query
A Prolog implementation in Rust with enhanced error handling
OxiLean meta layer - Metavar-aware WHNF, unification, type class synthesis, and tactics
A modern Prolog implementation written mostly in Rust.
foras is a First-Order Reasoner which uses the principles of predicate logic to derive new facts, verify statements, and prove theorems from an existing knowledge base.
Prolog-style pattern matching and relational search for Nushell
Cargo feature unification forensics
Union-find, congruence closure, and other unification code. Based on code from rustc.
Core library for JSON schema inference using genson-rs
Manage workspace-hack packages that do feature unification inside workspaces.
Unific is a ruby unification engine. A unification engine is an essential part of a logic programming environment (the whole logic programming environment this is taken from is available as the in-development Rulog[http://github.com/jimwise/rulog] (Ruby With Logic) gem), but can also be useful on its own as a pattern matching engine which can enforce consistency across multiple matches.
Pre-processing and format-unification of various resources mainly related to Minecraft
This includes the RuboCop configuration used by codeTakt. It is for the unification and linting of coding styles.
Ember's Module Unification brought to Rails
UnificationAssertion defines +assert_unifiable+ assertion to test if given two values are unifiable.
In computer science, a disjoint-set data structure, also called a union–find data structure or merge–find set, is a data structure that keeps track of a set of elements partitioned into a number of disjoint (non-overlapping) subsets. It provides near-constant-time operations (bounded by the inverse Ackermann function) to add new sets, to merge existing sets, and to determine whether elements are in the same set. In addition to many other uses (see the Applications section), disjoint-sets play a key role in Kruskal's algorithm for finding the minimum spanning tree of a graph. A disjoint-set forest consists of a number of elements each of which stores an id, a parent pointer, and, in efficient algorithms, a value called the "rank". The parent pointers of elements are arranged to form one or more trees, each representing a set. If an element's parent pointer points to no other element, then the element is the root of a tree and is the representative member of its set. A set may consist of only a single element. However, if the element has a parent, the element is part of whatever set is identified by following the chain of parents upwards until a representative element (one without a parent) is reached at the root of the tree. Forests can be represented compactly in memory as arrays in which parents are indicated by their array index. Disjoint-set data structures model the partitioning of a set, for example to keep track of the connected components of an undirected graph. This model can then be used to determine whether two vertices belong to the same component, or whether adding an edge between them would result in a cycle. The Union–Find algorithm is used in high-performance implementations of unification. This data structure is used by the Boost Graph Library to implement its Incremental Connected Components functionality. It is also a key component in implementing Kruskal's algorithm to find the minimum spanning tree of a graph. Note that the implementation as disjoint-set forests doesn't allow the deletion of edges, even without path compression or the rank heuristic. Sharir and Agarwal report connections between the worst-case behavior of disjoint-sets and the length of Davenport–Schinzel sequences, a combinatorial structure from computational geometry.
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