On Rotation Distance of Rank Bounded Trees
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Computing the rotation distance between two binary trees with n internal nodes efficiently (in poly(n) time) is a long standing open question in the study of height balancing in tree data structures. In this paper, we initiate the study of this problem bounding the rank of the trees given at the input (defined by Ehrenfeucht and Haussler (1989) in the context of decision trees). We define the rank-bounded rotation distance between two given binary trees T_1 and T_2 (with n internal nodes) of rank at most r, denoted by d_r(T_1,T_2), as the length of the shortest sequence of rotations that transforms T_1 to T_2 with the restriction that the intermediate trees must be of rank at most r. We show that the rotation distance problem reduces in polynomial time to the rank bounded rotation distance problem. This motivates the study of the problem in the combinatorial and algorithmic frontiers. Observing that trees with rank 1 coincide exactly with skew trees (binary trees where every internal node has at least one leaf as a child), we show the following results in this frontier : We present an O(n^2) time algorithm for computing d_1(T_1,T_2). That is, when the given trees are skew trees (we call this variant as skew rotation distance problem) - where the intermediate trees are restricted to be skew as well. In particular, our techniques imply that for any two skew trees d(T_1,T_2) ≤ n^2. We show the following upper bound : for any two trees T_1 and T_2 of rank at most r_1 and r_2 respectively, we have that: d_r(T_1,T_2) ≤ n^2 (1+(2n+1)(r_1+r_2-2)) where r = max{r_1,r_2}. This bound is asymptotically tight for r=1. En route our proof of the above theorems, we associate binary trees to permutations and bivariate polynomials, and prove several characterizations in the case of skew trees.
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