An improved approximation algorithm for the subpath planning problem and its generalization

Hanna Sumita, Yuma Yonebayashi, Naonori Kakimura, Ken Ichi Kawarabayashi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper focuses on a generalization of the traveling salesman problem (TSP), called the subpath planning problem (SPP). Given 2n vertices and n independent edges on a metric space, we aim to find a shortest tour that contains all the edges. SPP is one of the fundamental problems in both artificial intelligence and robotics. Our main result is to design a 1.5-approximation algorithm that runs in polynomial time, improving the currently best approximation algorithm. The idea is direct use of techniques developed for TSP. In addition, we propose a generalization of SPP called the subgroup planning problem (SGPP). In this problem, we are given a set of disjoint groups of vertices, and we aim to find a shortest tour such that all the vertices in each group are traversed sequentially. We propose a 3-approximation algorithm for SGPP. We also conduct numerical experiments. Compared with previous algorithms, our algorithms improve the solution quality by more than 10% for large instances with more than 10,000 vertices.

Original languageEnglish
Title of host publication26th International Joint Conference on Artificial Intelligence, IJCAI 2017
PublisherInternational Joint Conferences on Artificial Intelligence
Pages4412-4418
Number of pages7
ISBN (Electronic)9780999241103
Publication statusPublished - 2017
Event26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia
Duration: 2017 Aug 192017 Aug 25

Other

Other26th International Joint Conference on Artificial Intelligence, IJCAI 2017
CountryAustralia
CityMelbourne
Period17/8/1917/8/25

Fingerprint

Approximation algorithms
Planning
Traveling salesman problem
Artificial intelligence
Robotics
Polynomials
Experiments

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Sumita, H., Yonebayashi, Y., Kakimura, N., & Kawarabayashi, K. I. (2017). An improved approximation algorithm for the subpath planning problem and its generalization. In 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 (pp. 4412-4418). International Joint Conferences on Artificial Intelligence.

An improved approximation algorithm for the subpath planning problem and its generalization. / Sumita, Hanna; Yonebayashi, Yuma; Kakimura, Naonori; Kawarabayashi, Ken Ichi.

26th International Joint Conference on Artificial Intelligence, IJCAI 2017. International Joint Conferences on Artificial Intelligence, 2017. p. 4412-4418.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Sumita, H, Yonebayashi, Y, Kakimura, N & Kawarabayashi, KI 2017, An improved approximation algorithm for the subpath planning problem and its generalization. in 26th International Joint Conference on Artificial Intelligence, IJCAI 2017. International Joint Conferences on Artificial Intelligence, pp. 4412-4418, 26th International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, 17/8/19.
Sumita H, Yonebayashi Y, Kakimura N, Kawarabayashi KI. An improved approximation algorithm for the subpath planning problem and its generalization. In 26th International Joint Conference on Artificial Intelligence, IJCAI 2017. International Joint Conferences on Artificial Intelligence. 2017. p. 4412-4418
Sumita, Hanna ; Yonebayashi, Yuma ; Kakimura, Naonori ; Kawarabayashi, Ken Ichi. / An improved approximation algorithm for the subpath planning problem and its generalization. 26th International Joint Conference on Artificial Intelligence, IJCAI 2017. International Joint Conferences on Artificial Intelligence, 2017. pp. 4412-4418
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