The 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. Despite extensive work and some notable progress, including, for example, the Tango Trees (Demaine et al., FOCS 2004), that give the best currently known $O(log log n)$-competitive algorithm, the conjecture remains widely open. One of the main hurdles towards settling the conjecture is that we currently do not have approximation algorithms achieving better than an $O(log log n)$-approximation, even in the offline setting. All known non-trivial algorithms for BSTs so far rely on comparing the algorithms cost with the so-called Wilbers first 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 two-fold. First, we show that the gap between the WB-1 bound and the optimal solution value can be as large as $Omega(log log n/ log log log n)$; in fact, the gap holds even for several stronger variants of the bound. Second, we provide a simple algorithm, that, given an integer $D>0$, obtains an $O(D)$-approximation in time $expleft(Oleft (n^{1/2^{Omega(D)}}log nright )right )$. In particular, this gives a constant-factor approximation sub-exponential time algorithm. 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 {em Guillotine Bound}, that is stronger than WB, 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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