4.6 Judging a search strategy
You can assess any search algorithm on completeness, optimality, time and space, and read a complexity bound like O(b^d) for what it means in practice.
Before:01. Mathematics for Machine Learning02. Python — Basics to Advanced
Four questions decide whether a search algorithm is any use: will it find a solution, will it find the best one, how long will it take, how much memory will it need. Everything after this is scored on the same four, which is what makes the algorithms comparable at all. The b, d and m notation — branching factor, solution depth, maximum depth — is the shared vocabulary. The insight worth keeping is that an algorithm can be complete and still useless: guaranteed to finish, long after anyone stopped caring.
Work through these
Define completeness, optimality, time complexity and space complexity
Whether an algorithm always finds a solution if one exists, whether it finds the cheapest, and what it costs in time and in memory. Every search method in this unit gets judged on these four.
Use the b, d and m notation correctly
The standard notation for how many actions are available, how deep the shallowest solution is, and how deep the space goes. Using them correctly is what makes the complexity results readable.
Read a bound like O(b^d) and say what it means at a real problem size
A bound that grows exponentially means the algorithm is fine on small problems and impossible on real ones. Converting a bound into a rough count of states is what makes that concrete.
Explain how an algorithm can be complete and still useless
An algorithm can be guaranteed to find a solution eventually and still be useless, because eventually may be longer than the universe has. Completeness alone is not a recommendation.
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