Lazy Search Trees

TL;DR

The paper introduces lazy search trees, a versatile data structure that efficiently supports order-based and dynamic operations, bridging the gap between binary search trees and priority queues with performance guarantees based on query distribution.

Contribution

It presents a new comparison-based data structure with adaptive performance bounds, supporting a wide range of operations and improving insertion times based on query patterns.

Findings

Supports order-based operations with adaptive performance

Reduces insertion time from Θ(log n) to near constant in certain cases

Operates efficiently on arrays with minimal pointers

Abstract

We introduce the lazy search tree data structure. The lazy search tree is a comparison-based data structure on the pointer machine that supports order-based operations such as rank, select, membership, predecessor, successor, minimum, and maximum while providing dynamic operations insert, delete, change-key, split, and merge. We analyze the performance of our data structure based on a partition of current elements into a set of gaps $\{\Delta_i\}$ based on rank. A query falls into a particular gap and splits the gap into two new gaps at a rank $r$ associated with the query operation. If we define $B = \sum_i |\Delta_i| \log_2(n/|\Delta_i|)$, our performance over a sequence of $n$ insertions and $q$ distinct queries is $O(B + \min(n \log \log n, n \log q))$. We show $B$ is a lower bound. Effectively, we reduce the insertion time of binary search trees from $\Theta(\log n)$ to $O(\min(\log(n/|\Delta_i|) + \log \log |\Delta_i|, \; \log q))$, where $\Delta_i$ is the gap in which the inserted element falls. Over a sequence of $n$ insertions and $q$ queries, a time bound of $O(n \log q + q \log n)$ holds; better bounds are possible when queries are non-uniformly distributed. As an extreme case of non-uniformity, if all queries are for the minimum element, the lazy search tree performs as a priority queue with $O(\log \log n)$ time insert and decrease-key operations. The same data structure supports queries for any rank, interpolating between binary search trees and efficient priority queues. Lazy search trees can be implemented to operate mostly on arrays, requiring only $O(\min(q, n))$ pointers. Via direct reduction, our data structure also supports the efficient access theorems of the splay tree, providing a powerful data structure for non-uniform element access, both when the number of accesses is small and large.