eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Leibniz International Proceedings in Informatics
1868-8969
2020-09-16
19:1
19:11
10.4230/LIPIcs.FUN.2021.19
article
Train Tracks with Gaps
Kuszmaul, William
1
Massachusetts Institute of Technology, CSAIL, Cambrige, MA, USA
We identify a tradeoff curve between the number of wheels on a train car, and the amount of track that must be installed in order to ensure that the train car is supported by the track at all times. The goal is to build an elevated track that covers some large distance 𝓁, but that consists primarily of gaps, so that the total amount of feet of train track that is actually installed is only a small fraction of 𝓁. In order so that the train track can support the train at all points, the requirement is that as the train drives across the track, at least one set of wheels from the rear quarter and at least one set of wheels from the front quarter of the train must be touching the track at all times.
We show that, if a train car has n sets of wheels evenly spaced apart in its rear and n sets of wheels evenly spaced apart in its front, then it is possible to build a train track that supports the train car but uses only Θ(𝓁 / n) feet of track. We then consider what happens if the wheels on the train car are not evenly spaced (and may even be configured adversarially). We show that for any configuration of the train car, with n wheels in each of the front and rear quarters of the car, it is possible to build a track that supports the car for distance 𝓁 and uses only O((𝓁 log n)/n) feet of track. Additionally, we show that there exist configurations of the train car for which this tradeoff curve is asymptotically optimal. Both the upper and lower bounds are achieved via applications of the probabilistic method.
The algorithms and lower bounds in this paper provide simple illustrative examples of many of the core techniques in probabilistic combinatorics and randomized algorithms. These include the probabilistic method with alterations, the use of McDiarmid’s inequality within the probabilistic method, the algorithmic Lovász Local Lemma, the min-hash technique, and the method of conditional probabilities.
https://drops.dagstuhl.de/storage/00lipics/lipics-vol157-fun2021/LIPIcs.FUN.2021.19/LIPIcs.FUN.2021.19.pdf
probabilistic method
algorithms
trains
Lovász Local Lemma
McDiarmid’s Inequality