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Admissible heuristic

As explained in Advanced Algorithms and Data Structures

An admissible heuristic is a heuristic that never overestimates the cost of reaching a goal from a given state.

An admissible heuristic never overestimates the cost of reaching a goal from a state. In A* search, this optimism helps prioritize promising routes without claiming that a remaining path is cheaper than it can actually be. section 14.5.1

Use the condition with these related checks:

  • Pair admissibility with consistency when applying A* to general graphs: admissibility avoids overestimating a complete path, while consistency avoids overestimating an individual edge. section 14.5.1
  • For trees, admissibility alone is sufficient because a node has only one path from the root to any goal through it. section 14.5.1
  • Use straight-line distance to a delivery address as an optimistic guide: it favors paths moving toward the goal and can reduce visited vertices. section 14.5.1

How Advanced Algorithms and Data Structures covers it

How deep it goes
Defines optimism, contrasts consistency, and notes tree sufficiency without developing a formal proof.
Examples
Uses delivery routing, straight-line distance, and an inflated vertex estimate that produces a non-optimal A* path.
What it assumes
Assumes familiarity with A* search, graph paths, vertices, goals, and heuristic functions.
What you can do after
Apply it via A* by checking that a heuristic does not overestimate and, for graphs, is also consistent.
How it connects
Connects admissibility to A* search, consistency, heuristics, graph paths, and shortest-route problems.

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This explainer was generated by Manning's knowledge graph from the text of Advanced Algorithms and Data Structures. The section links point to the parts of the book it draws on.