Theory of Computing
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Title : Pinning Down the Strong Wilber-1 Bound for Binary Search Trees
Authors : Parinya Chalermsook, Julia Chuzhoy, and Thatchaphol Saranurak
Volume : 19
Number : 8
Pages : 1-71
URL : https://theoryofcomputing.org/articles/v019a008
Abstract
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Dynamic Optimality Conjecture, postulating the existence of an
$O(1)$-competitive online algorithm for binary search trees (BSTs), is
among the most fundamental open problems in dynamic data structures.
The conjecture remains wide open, despite extensive work and some
notable progress, including, for example, the $O(\log\log n)$-competitive
Tango Trees, which is the best currently known competitive ratio.
One of the main hurdles towards settling the conjecture is that we
currently do not have polynomial-time approximation algorithms
achieving better than an $O(\log \log n)$-approximation, even in the
offline setting. All known non-trivial algorithms for BSTs rely on
comparing the algorithm's cost with the so-called Wilber-1 bound (WB-1).
Therefore, establishing the worst-case relationship between this bound
and the optimal solution cost appears crucial for further progress, and
it is an interesting open question in its own right.
Our contribution is twofold. First, we show that the gap between WB-1
and the optimal solution value can be as large as $\Omega(\log \log n/
\log \log \log n)$; in fact, we show that the gap holds even for
several stronger variants of the bound.* Second, we show, given an
integer $D> 0$, a $D$-approximation algorithm that runs in time
$\exp\left(O\left (n^{1/2^{\Omega(D)}}\log n\right )\right )$. In
particular, this yields a constant-factor approximation algorithm with
subexponential running time.** Moreover, we obtain a simpler and
cleaner efficient $O(\log \log n)$-approximation algorithm that
can be used in an online setting. Finally, we suggest a new bound,
that we call the _Guillotine Bound_, that is stronger than
WB-1, while maintaining its algorithm-friendly nature, that we hope
will lead to better algorithms. All our results use the geometric
interpretation of the problem, leading to cleaner and simpler
analysis.
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* A recent independent paper by Lecomte and Weinstein (ESA'20)
shows an even stronger, $\Omega(\log\log n)$, separation.
** The term "subexponential time" in this paper refers to
the running time $2^{o(n)}$.
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An extended abstract of this paper appeared in the
Proceedings of the 23rd Internat. Conf. on Approximation
Algorithms and Combinatorial Optimization (APPROXâ€™20).