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It is particularly useful for efficiently implementing backtracking algorithms, such as Knuth's Algorithm X for the exact cover problem. [1] Algorithm X is a recursive , nondeterministic , depth-first , backtracking algorithm that finds all solutions to the exact cover problem.
The following program is an implementation of Donald Knuth's informal description of the solution on Page 31, Section 7.2.2 Backtrack Programming from The Art of Computer Programming, Volume 4B into the Python programming language. [28]
Goal-directed programming languages such as Icon, Planner and Prolog, which use backtracking internally to generate answers. The DPLL algorithm for solving the Boolean satisfiability problem. The following is an example where backtracking is used for the constraint satisfaction problem:
Some hobbyists have developed computer programs that will solve Sudoku puzzles using a backtracking algorithm, which is a type of brute force search. [3] Backtracking is a depth-first search (in contrast to a breadth-first search), because it will completely explore one branch to a possible solution before moving to another branch.
The most used techniques are variants of backtracking, constraint propagation, and local search. These techniques are also often combined, as in the VLNS method, and current research involves other technologies such as linear programming. [14] Backtracking is a recursive algorithm. It maintains a partial assignment of the variables.
In backtracking algorithms, look ahead is the generic term for a subprocedure that attempts to foresee the effects of choosing a branching variable to evaluate one of its values. The two main aims of look-ahead are to choose a variable to evaluate next and to choose the order of values to assign to it.
The DPLL algorithm enhances over the backtracking algorithm by the eager use of the following rules at each step: Unit propagation If a clause is a unit clause , i.e. it contains only a single unassigned literal, this clause can only be satisfied by assigning the necessary value to make this literal true.
When further backtracking or backjumping from the node, the variable of the node is removed from this set, and the set is sent to the node that is the destination of backtracking or backjumping. This algorithm works because the set maintained in a node collects all variables that are relevant to prove unsatisfiability in the leaves that are ...