Linear Probing: Analysis

If there are no collisions, insert/remove and search performance is $\Omicron(1)$; this is the best-case scenario.

The worst-case scenario is when the probing sequence goes over every occupied position. This scenario leads to $\Omicron(n)$ performance where $n$ is the number of items stored in the hash table.

Given an open-address hash table with load factor $\alpha$, the expected number of probes in an unsuccessful search (or for inserting an element) is at most:

$$ \frac{1}{1 - \alpha} $$

The statement above can be proved by assuming the hash table employs a uniform hashing function. The proof is beyond the scope of this class. Interested reader is referred to the CLRS: Theorem $11.6$ and Corollary $11.7$.

The take-home message: since $\alpha < 1$ in open-address hash tables, the average (expected) performance is constant time under the assumption of usinform hashing.