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Aspvall, Plass & Tarjan (1979) found a simpler linear time procedure for solving 2-satisfiability instances, based on the notion of strongly connected components from graph theory. [4] Two vertices in a directed graph are said to be strongly connected to each other if there is a directed path from one to the other and vice versa.
In computer science and formal methods, a SAT solver is a computer program which aims to solve the Boolean satisfiability problem.On input a formula over Boolean variables, such as "(x or y) and (x or not y)", a SAT solver outputs whether the formula is satisfiable, meaning that there are possible values of x and y which make the formula true, or unsatisfiable, meaning that there are no such ...
For example, x 1 is a positive literal, ¬x 2 is a negative literal, and x 1 ∨ ¬x 2 is a clause. The formula ( x 1 ∨ ¬ x 2 ) ∧ (¬ x 1 ∨ x 2 ∨ x 3 ) ∧ ¬ x 1 is in conjunctive normal form; its first and third clauses are Horn clauses, but its second clause is not.
Domain-specific constraints may come to the constraint store both from the body of a clauses and from equating a literal with a clause head: for example, if the interpreter rewrites the literal A(X+2) with a clause whose fresh variant head is A(Y/2), the constraint X+2=Y/2 is added to the constraint store. If a variable appears in a real or ...
The basic backtracking algorithm runs by choosing a literal, assigning a truth value to it, simplifying the formula and then recursively checking if the simplified formula is satisfiable; if this is the case, the original formula is satisfiable; otherwise, the same recursive check is done assuming the opposite truth value.
A unification problem is a finite set E={ l 1 ≐ r 1, ..., l n ≐ r n} of equations to solve, where l i, r i are in the set of terms or expressions.Depending on which expressions or terms are allowed to occur in an equation set or unification problem, and which expressions are considered equal, several frameworks of unification are distinguished.
In computer science, a literal is a textual representation (notation) of a value as it is written in source code. [1] [2] Almost all programming languages have notations for atomic values such as integers, floating-point numbers, and strings, and usually for Booleans and characters; some also have notations for elements of enumerated types and compound values such as arrays, records, and objects.
The SciPy scientific library, for instance, uses HiGHS as its LP solver [13] from release 1.6.0 [14] and the HiGHS MIP solver for discrete optimization from release 1.9.0. [15] As well as offering an interface to HiGHS, the JuMP modelling language for Julia [16] also describes the specific use of HiGHS in its user documentation. [17]