Linked from

The 16 pages that link to Backtracking, each with the reason it gives.

• Dynamic programmingCompared with: Backtracking explores alternatives, while dynamic programming merges repeated states.

• Constraint satisfaction problemRelated: It explores assignments incrementally and retreats when a constraint is violated.

• Depth-first searchRelated: DFS backtracks when the current vertex has no unvisited neighbors.

• Greedy algorithmCompared with: Backtracking revisits decisions that a greedy algorithm permanently commits to.

• RecursionRelated: Recursive calls represent successive choices and return when a branch fails.

• Logic programmingRelated: It lets an interpreter explore alternative rule applications after a failed proof.

• Divide-and-conquer algorithmCompared with: It branches over candidate choices, unlike decompositions that combine solved subproblems.

• Hamiltonian cycleRelated: Hamiltonian-cycle search can extend a path vertex by vertex and prune dead ends.

• Hamiltonian pathRelated: It explores vertex sequences while pruning choices that cannot form a full path.

• SudokuRelated: It solves puzzles when logical eliminations alone do not determine the next move.

• Mathematical puzzleRelated: It handles puzzles whose choices create many constrained possibilities.

• Graph isomorphism problemRelated: Exact solvers extend partial vertex mappings and prune inconsistent branches.

• Hamiltonian path problemRelated: It explores vertex sequences and prunes those that cannot extend to a spanning path.

• NonogramRelated: Difficult puzzles can require testing a candidate and undoing it after contradiction.

• Mahjong solitaireRelated: Solvers use it to test move sequences and abandon branches that strand unmatched tiles.

• Peg solitaireRelated: Solvers use it to test jump sequences and abandon dead ends.