Self-adjusting binary search trees
Journal of the ACM (JACM)
The pairing heap: a new form of self-adjusting heap
Algorithmica
SIAM Journal on Computing
Sequential access in splay trees takes linear time
Combinatorica
Amortized complexity of data structures
Amortized complexity of data structures
Methods for achieving fast query times in point location data structures
SODA '97 Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms
Alternatives to splay trees with O(log n) worst-case access times
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Improved Upper Bounds for Pairing Heaps
SWAT '00 Proceedings of the 7th Scandinavian Workshop on Algorithm Theory
On the Dynamic Finger Conjecture for Splay Trees Part II: The Proof
On the Dynamic Finger Conjecture for Splay Trees Part II: The Proof
On the Dynamic Finger Conjecture for Splay Trees Part I: Splay Sorting log n-Block Sequences
On the Dynamic Finger Conjecture for Splay Trees Part I: Splay Sorting log n-Block Sequences
Distribution-sensitive data structures
Distribution-sensitive data structures
Lower bounds for accessing binary search trees with rotations
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
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We present a new priority queue data structure, the queap, that executes insertion in O(1) amortized time and Extract-min in O(log(k + 2)) amortized time if there are k items that have been in the heap longer than the item to be extracted. Thus if the operations on the queap are first-in first-out, as on a queue, each operation will execute in constant time. This idea of trying to make operations on the least recently accessed items fast, which we call the queueish property, is a natural complement to the working set property of certain data structures, such as splay trees and pairing heaps, where operations on the most recently accessed data execute quickly. However, we show that the queueish property is in some sense more difficult than the working set property by demonstrating that it is impossible to create a queueish binary search tree, but that many search data structures can be made almost queueish with a O(log log n) amortized extra cost per operation.