Graph search

A graph search explores vertices through incident edges while recording visited vertices. On a finite graph, depth-first or breadth-first exploration terminates and decides reachability; recording predecessors also gives a path. Restricting to vertices reachable from a start and able to reach an accepting vertex identifies the productive part of a deterministic finite automaton.

Maintain a frontier of partial states in a priority queue and expand the state with the best current priority. Correctness of a particular stopping rule depends on what the priority bounds. If every descendant has smaller priority than its ancestor, the frontier maximum also bounds the priority of every unseen descendant.

Best-first search prioritizing accumulated path cost, equivalently A-star search with zero heuristic function. With finite branching factor and positive lower-bounded step costs, tree search is complete for finite solutions and returns a least-cost goal if the goal test is applied on removal from the search frontier. Goal testing at generation can instead return an expensive edge before a cheaper multi-edge route is explored.

Best-first search using only a heuristic function estimating remaining goal cost, without adding accumulated path cost. A comparator supplying the same total ordering of estimates suffices to choose expansions even if numeric estimates are unavailable. In general it need not be a complete search algorithm or an optimal search algorithm; particular stronger information or finite-graph policies can supply additional guarantees.

In a finite reachable search problem with strictly positive step costs and a reachable goal, greedy best-first search using a perfect heuristic selects one parent–child chain until it reaches a goal state. At a selected nonterminal state $s$, an optimal first transition has $h^{\ast }(s^{\prime })=h^{\ast }(s)-c(s,s^{\prime })<h^{\ast }(s)$. Every older search frontier entry has estimate at least $h^{\ast }(s)$, so the next selected entry must be a newly generated child with smaller estimate. Repeating proves the chain property. In an infinite finitely branching problem, a positive uniform lower bound on costs also bounds the chain length by the initial remaining cost divided by that lower bound. Zero-cost ties can break the chain property; the lemma does not establish least accumulated path cost.

Best-first search prioritizing the estimated total solution cost $f=g+h$, with $g$ the cost already incurred and $h$ a heuristic function for the remaining cost. With nonnegative estimates, goal estimates zero, finite branching factor and step costs bounded below by a positive constant, the tree search version using an admissible heuristic returns a minimum-cost goal when it removes one for expansion. An optimal solution path always has a frontier node with $f\le C^{\ast }$; a more costly goal cannot be removed before it. The stated finiteness and lower-cost assumptions ensure finitely many earlier nodes when a finite solution exists. Repeated-state handling in graph search needs reopening when a better path is found, unless a consistent heuristic justifies finalizing expanded states.

Suppose $0\le h(n)\le h^{\ast }(n)\le u(n)$ in a search problem, with $h^{\ast }=\infty$ at dead ends. Every finite $g(n)+u(n)$ bounds some reachable solution cost from above, so their running minimum $B$ is at least the global optimum $C^{\ast }$. Every node of an optimal path has $g+h\le C^{\ast }\le B$, and therefore survives the displayed strict pruning rule. Ordinary A-star search can continue to a selected goal without receiving a path witnessing $u$.

Equality pruning is unsafe without a stored incumbent solution: when $B=C^{\ast }$, it could delete every optimal continuation. A numerical upper bound does not by itself provide a plan to return. This distinguishes an upper-bound certificate from a constructed solution in branch and bound.