We present efficient algorithms for constructing a shortest path between two states in the Tower of Hanoi graph, and for computing the length of the shortest path. The key element is a finite-state machine which decides, after examining on the average only 63/38 of the largest discs, whether the largest disc will be moved once or twice. This solves a problem raised by Andreas Hinz, and results in a better understanding of how the shortest path is determined. Our algorithm for computing the length of the shortest path is typically about twice as fast as the existing algorithm. We also use our results to give a new derivation of the average distance 466/885 between two random points on the Sierpinski gasket of unit side.
We modify the rules of the classical Tower of Hanoi puzzle in a quite natural way to get the Fibonacci sequence involved in the optimal algorithm of resolution, and show some nice properties of such a variant. In particular, we deduce from this Tower of Hanoi-Fibonacci a Gray-like code on the set of binary words without the factor 11, which has some properties intersting for itself and from which an iterative algorithm for the Tower of Hanoi-Fibonacci is obtained. Such an algorithm involves the Fibonacci substitution. Eventually, we briefly extend the study to some natural generalizations.
Maximum matchings and minimum dominating sets in Apollonian networks and extended Tower of Hanoi graphs
阿波罗网络显示出显着的幂律和小世界性质，在大多数现实的网络系统中观察到的。它们的对偶图是推广的Tower of Hanoi图，它是由Tower of Hanoi图通过添加一个特殊的顶点连接到它的所有三个极端顶点而得到的。本文研究了Apollonian网络及其对偶图中的最大匹配和最小控制集，这两个问题在复杂网络的结构可控性等领域有着广泛的应用。对于这两个网络，我们确定它们的匹配数，控制数，最大匹配数，以及最小控制集的数量。
A parallel variant of the Tower of Hanoi Puzzle is described herein. Within this parallel context, two theorems on minimal walks in the state space of configurations, along with their constructive proofs, are provided. These proofs are used to describe a {\sl denoising method}: a method for identifying and eliminating sub-optimal transfers within an arbitrary, valid sequence of disk configurations (as per the rules of the Puzzle). We discuss potential applications of this method to hierarchical reinforcement learning.
Some of the algorithms for solving the Tower of Hanoi puzzle can be applied "with eyes closed" or "without memory". Here we survey the solution for the classical Tower of Hanoi that uses finite automata, as well as some variations on the original puzzle. In passing, we obtain a new result on morphisms generating the classical and the lazy Tower of Hanoi.
We consider the relationship between the Laplacians on two sequences of planar graphs, one from the theory of self-similar groups and one from analysis on fractals. By establishing a spectral decimation map between these sequences we give an elementary calculation of the spectrum of the former, which was first computed by Grigorchuk and Šunić. Our method also gives a full description of the eigenfunctions.
