Strengthened Lazy Heaps: Surpassing the Lower Bounds for Binary Heaps

TL;DR

This paper introduces a novel in-place heap data structure that achieves optimal comparison bounds and surpasses traditional lower bounds for binary heaps by strengthening heap properties and buffering insertions.

Contribution

It presents a new in-place heap with constant-time minimum and insert operations and an efficient extract-min, surpassing known lower bounds for binary heaps.

Findings

Minimum and insert operations take O(1) worst-case time.

Extract-min operation takes O(log n) worst-case time with at most log n + O(1) comparisons.

The data structure surpasses known lower bounds for binary heaps.

Abstract

Let $n$ denote the number of elements currently in a data structure. An in-place heap is stored in the first $n$ locations of an array, uses $O(1)$ extra space, and supports the operations: minimum, insert, and extract-min. We introduce an in-place heap, for which minimum and insert take $O(1)$ worst-case time, and extract-min takes $O(\lg{} n)$ worst-case time and involves at most $\lg{} n + O(1)$ element comparisons. The achieved bounds are optimal to within additive constant terms for the number of element comparisons. In particular, these bounds for both insert and extract-min -and the time bound for insert- surpass the corresponding lower bounds known for binary heaps, though our data structure is similar. In a binary heap, when viewed as a nearly complete binary tree, every node other than the root obeys the heap property, i.e. the element at a node is not smaller than that at its parent. To surpass the lower bound for extract-min, we reinforce a stronger property at the bottom levels of the heap that the element at any right child is not smaller than that at its left sibling. To surpass the lower bound for insert, we buffer insertions and allow $O(\lg^2{} n)$ nodes to violate heap order in relation to their parents.