Key-independent optimality

Key-independent optimality is a property of some binary search tree data structures in computer science proposed by John Iacono.^[1] Suppose that key-value pairs are stored in a data structure, and that the keys have no relation to their paired values. A data structure has **key-independent optimality** if, when randomly assigning the keys, the expected performance of the data structure is within a constant factor of the optimal data structure. Key-independent optimality is related to dynamic optimality.

Definitions

There are many binary search tree algorithms that can look up a sequence of $m$ keys $X = x_1, x_2, \cdots, x_m$ , where each $x_i$ is a number between $1$ and $n$ . For each sequence $X$ , let $\textit{OPT}(X)$ be the fastest binary search tree algorithm that looks up the elements in $X$ in order. Let $b$ be one of the $n!$ possible permutation of the sequence $1, 2, \cdots, n$ , chosen at random, where $b(i)$ is the $i$ th entry of $b$ . Let $b(X) = b(x_1), b(x_2), \cdots ,b(x_m)$ . Iacono defined, for a sequence $X$ , that $\textit{KIOPT}(X) = E[\textit{OPT}(b(X))]$ .

A data structure has key-independent optimality if it can lookup the elements in $X$ in time $O(\textit{KIOPT}(X))$ .

Relationship with other bounds

Key-independent optimality has been proved to be asymptotically equivalent to the working set theorem. Splay trees are known to have key-independent optimality.

References

↑ "John Iacono. Key independent optimality. Algorithmica, 42(1):3-10, 2005.".