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Related papers: A Tight Lower Bound for Decrease-Key in the Pure H…

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A lower bound is presented which shows that a class of heap algorithms in the pointer model with only heap pointers must spend Omega(log log n / log log log n) amortized time on the decrease-key operation (given O(log n) amortized-time…

Data Structures and Algorithms · Computer Science 2013-07-17 John Iacono

Since the invention of the pairing heap by Fredman, Sedgewick, Sleator, and Tarjan, it has been an open question whether this or any other simple "self-adjusting" heap supports decrease-key operations in $O(\log\log n)$ time, where $n$ is…

Data Structures and Algorithms · Computer Science 2025-02-13 Corwin Sinnamon , Robert E. Tarjan

Improving the structure and analysis in \cite{elm0}, we give a variation of the pairing heaps that has amortized zero cost per meld (compared to an $O(\log \log{n})$ in \cite{elm0}) and the same amortized bounds for all other operations.…

Data Structures and Algorithms · Computer Science 2009-04-09 Amr Elmasry

The pairing heap is a simple "self-adjusting" implementation of a heap (priority queue). Inserting an item into a pairing heap or decreasing the key of an item takes O(1) time worst-case, as does melding two heaps. But deleting an item of…

Data Structures and Algorithms · Computer Science 2022-08-26 Corwin Sinnamon , Robert Tarjan

We give a priority queue that achieves the same amortized bounds as Fibonacci heaps. Namely, find-min requires O(1) worst-case time, insert, meld and decrease-key require O(1) amortized time, and delete-min requires $O(\log n)$ amortized…

Data Structures and Algorithms · Computer Science 2010-02-11 Amr Elmasry

The smooth heap and the closely related slim heap are recently invented self-adjusting implementations of the heap (priority queue) data structure. We analyze the efficiency of these data structures. We obtain the following amortized bounds…

Data Structures and Algorithms · Computer Science 2021-11-08 Corwin Sinnamon , Robert E. Tarjan

Pairing heaps are shown to have constant amortized time Insert and Meld, thus showing that pairing heaps have the same amortized runtimes as Fibonacci heaps for all operations but Decrease-key.

Data Structures and Algorithms · Computer Science 2014-01-23 John Iacono

A Fibonacci heap is a deterministic data structure implementing a priority queue with optimal amortized operation costs. An unfortunate aspect of Fibonacci heaps is that they must maintain a "mark bit" which serves only to ensure efficiency…

Data Structures and Algorithms · Computer Science 2015-02-19 Jerry Li , John Peebles

We are concentrating on reducing overhead of heaps based on comparisons with optimal worstcase behaviour. The paper is inspired by Strict Fibonacci Heaps [1], where G. S. Brodal, G. Lagogiannis, and R. E. Tarjan implemented the heap with…

Data Structures and Algorithms · Computer Science 2019-11-27 Vladan Majerech

The pairing heap is a classical heap data structure introduced in 1986 by Fredman, Sedgewick, Sleator, and Tarjan. It is remarkable both for its simplicity and for its excellent performance in practice. The "magic" of pairing heaps lies in…

Data Structures and Algorithms · Computer Science 2018-06-22 Dani Dorfman , Haim Kaplan , László Kozma , Uri Zwick

The smooth heap is a recently introduced self-adjusting heap [Kozma, Saranurak, 2018] similar to the pairing heap [Fredman, Sedgewick, Sleator, Tarjan, 1986]. The smooth heap was obtained as a heap-counterpart of Greedy BST, a binary search…

Data Structures and Algorithms · Computer Science 2021-07-13 Maria Hartmann , László Kozma , Corwin Sinnamon , Robert E. Tarjan

We revisit multipass pairing heaps and path-balanced binary search trees (BSTs), two classical algorithms for data structure maintenance. The pairing heap is a simple and efficient "self-adjusting" heap, introduced in 1986 by Fredman,…

Data Structures and Algorithms · Computer Science 2018-06-25 Dani Dorfman , Haim Kaplan , László Kozma , Seth Pettie , Uri Zwick

A heap is a dynamic data structure that stores a set of labeled values under the following operations: pop returns the minimum value of the heap, Push($x_i$) pushes a new value $x_i$ onto the heap, and DecreaseKey($i$, $v$) decreases the…

Data Structures and Algorithms · Computer Science 2026-04-28 Ivor van der Hoog , John Iacono , Eva Rotenberg , Daniel Rutschmann

Chazelle [JACM00] introduced the soft heap as a building block for efficient minimum spanning tree algorithms, and recently Kaplan et al. [SOSA2019] showed how soft heaps can be applied to achieve simpler algorithms for various selection…

Data Structures and Algorithms · Computer Science 2020-08-13 Gerth Stølting Brodal

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…

Data Structures and Algorithms · Computer Science 2014-07-15 Stefan Edelkamp , Jyrki Katajainen , Amr Elmasry

One of the biggest open problems in external memory data structures is the priority queue problem with DecreaseKey operations. If only Insert and ExtractMin operations need to be supported, one can design a comparison-based priority queue…

Data Structures and Algorithms · Computer Science 2016-11-04 Kasper Eenberg , Kasper Green Larsen , Huacheng Yu

We introduce a new family of priority-queue data structures: partition-based simple heaps. The structures consist of $O(\log n)$ doubly-linked lists; order is enforced among data in different lists, but the individual lists are unordered.…

Data Structures and Algorithms · Computer Science 2026-03-03 Gerth Stølting Brodal , John Iacono , Casper Moldrup Rysgaard , Sebastian Wild

In the paper "Fast Fibonacci heaps with worst case extensions", we have described heaps with both Meld-DecreaseKey and DecreaseKey interfaces, allowing operations with guaranteed worst-case asymptotically optimal times. The paper was…

Data Structures and Algorithms · Computer Science 2020-11-20 Vladan Majerech

We show the $O(\log n)$ time extract minimum function of efficient priority queues can be generalized to the extraction of the $k$ smallest elements in $O(k \log(n/k))$ time (we define $\log(x)$ as $\max(\log_2(x), 1)$.), which we prove…

Data Structures and Algorithms · Computer Science 2022-01-11 Bryce Sandlund , Lingyi Zhang

In the particular case we have insertions/deletions at the tail of a given set S of $n$ one-dimensional elements, we present a simpler and more concrete algorithm than that presented in [Anderson, 2007] achieving the same (but also…

Data Structures and Algorithms · Computer Science 2008-12-18 Spyros Sioutas
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