Computational Geometry
Average-case analysis is a method used to determine the expected performance or complexity of an algorithm under typical conditions, rather than the worst-case or best-case scenarios. It helps in understanding how an algorithm behaves when it is run on random inputs or on inputs that are representative of real-world usage. This form of analysis provides a more realistic measure of efficiency, especially in fields like computational geometry where algorithms often process spatial data.
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