Pattern-avoiding access in binary search trees
Parinya Chalermsook, Mayank Goswami, Laszlo Kozma, Kurt Mehlhorn,, Thatchaphol Saranurak

TL;DR
This paper investigates the complexity of access sequences in binary search trees, specifically analyzing the Greedy algorithm's performance on pattern-avoiding sequences and providing new bounds related to the traversal conjecture.
Contribution
The paper introduces the first upper bounds for Greedy on pattern-avoiding sequences, linking sequence complexity to pattern avoidance and revealing an input-revealing property.
Findings
Greedy is nearly linear for preorder traversal sequences.
With linear preprocessing, Greedy achieves linear time on such sequences.
The results connect sequence complexity to pattern avoidance, providing new bounds for online BST algorithms.
Abstract
The dynamic optimality conjecture is perhaps the most fundamental open question about binary search trees (BST). It postulates the existence of an asymptotically optimal online BST, i.e. one that is constant factor competitive with any BST on any input access sequence. The two main candidates for dynamic optimality in the literature are splay trees [Sleator and Tarjan, 1985], and Greedy [Lucas, 1988; Munro, 2000; Demaine et al. 2009] [..] Dynamic optimality is trivial for almost all sequences: the optimum access cost of most length-n sequences is Theta(n log n), achievable by any balanced BST. Thus, the obvious missing step towards the conjecture is an understanding of the "easy" access sequences. [..] The difficulty of proving dynamic optimality is witnessed by highly restricted special cases that remain unresolved; one prominent example is the traversal conjecture [Sleator and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAlgorithms and Data Compression · Optimization and Search Problems · Advanced Graph Theory Research
