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Documents authored by Lin, Guohui

Document
DOI: 10.4230/LIPIcs.CPM.2023.2
Approximation Algorithms for the Longest Run Subsequence Problem

Authors: Yuichi Asahiro, Hiroshi Eto, Mingyang Gong, Jesper Jansson, Guohui Lin, Eiji Miyano, Hirotaka Ono, and Shunichi Tanaka

Published in: LIPIcs, Volume 259, 34th Annual Symposium on Combinatorial Pattern Matching (CPM 2023)

Abstract
We study the approximability of the Longest Run Subsequence problem (LRS for short). For a string S = s_1 ⋯ s_n over an alphabet Σ, a run of a symbol σ ∈ Σ in S is a maximal substring of consecutive occurrences of σ. A run subsequence S' of S is a sequence in which every symbol σ ∈ Σ occurs in at most one run. Given a string S, the goal of LRS is to find a longest run subsequence S^* of S such that the length |S^*| is maximized over all the run subsequences of S. It is known that LRS is APX-hard even if each symbol has at most two occurrences in the input string, and that LRS admits a polynomial-time k-approximation algorithm if the number of occurrences of every symbol in the input string is bounded by k. In this paper, we design a polynomial-time (k+1)/2-approximation algorithm for LRS under the k-occurrence constraint on input strings. For the case k = 2, we further improve the approximation ratio from 3/2 to 4/3.
Cite as

Yuichi Asahiro, Hiroshi Eto, Mingyang Gong, Jesper Jansson, Guohui Lin, Eiji Miyano, Hirotaka Ono, and Shunichi Tanaka. Approximation Algorithms for the Longest Run Subsequence Problem. In 34th Annual Symposium on Combinatorial Pattern Matching (CPM 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 259, pp. 2:1-2:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{asahiro_et_al:LIPIcs.CPM.2023.2,
  author =	{Asahiro, Yuichi and Eto, Hiroshi and Gong, Mingyang and Jansson, Jesper and Lin, Guohui and Miyano, Eiji and Ono, Hirotaka and Tanaka, Shunichi},
  title =	{{Approximation Algorithms for the Longest Run Subsequence Problem}},
  booktitle =	{34th Annual Symposium on Combinatorial Pattern Matching (CPM 2023)},
  pages =	{2:1--2:12},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-276-1},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{259},
  editor =	{Bulteau, Laurent and Lipt\'{a}k, Zsuzsanna},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CPM.2023.2},
  URN =		{urn:nbn:de:0030-drops-179560},
  doi =		{10.4230/LIPIcs.CPM.2023.2},
  annote =	{Keywords: Longest run subsequence problem, bounded occurrence, approximation algorithm}
}
Document
DOI: 10.4230/LIPIcs.MFCS.2022.53
Approximation Algorithms for Covering Vertices by Long Paths

Authors: Mingyang Gong, Jing Fan, Guohui Lin, and Eiji Miyano

Published in: LIPIcs, Volume 241, 47th International Symposium on Mathematical Foundations of Computer Science (MFCS 2022)

Abstract
Given a graph, the general problem to cover the maximum number of vertices by a collection of vertex-disjoint long paths seemingly escapes from the literature. A path containing at least k vertices is considered long. When k ≤ 3, the problem is polynomial time solvable; when k is the total number of vertices, the problem reduces to the Hamiltonian path problem, which is NP-complete. For a fixed k ≥ 4, the problem is NP-hard and the best known approximation algorithm for the weighted set packing problem implies a k-approximation algorithm. To the best of our knowledge, there is no approximation algorithm directly designed for the general problem; when k = 4, the problem admits a 4-approximation algorithm which was presented recently. We propose the first (0.4394 k + O(1))-approximation algorithm for the general problem and an improved 2-approximation algorithm when k = 4. Both algorithms are based on local improvement, and their performance analyses are done via amortization.
Cite as

Mingyang Gong, Jing Fan, Guohui Lin, and Eiji Miyano. Approximation Algorithms for Covering Vertices by Long Paths. In 47th International Symposium on Mathematical Foundations of Computer Science (MFCS 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 241, pp. 53:1-53:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{gong_et_al:LIPIcs.MFCS.2022.53,
  author =	{Gong, Mingyang and Fan, Jing and Lin, Guohui and Miyano, Eiji},
  title =	{{Approximation Algorithms for Covering Vertices by Long Paths}},
  booktitle =	{47th International Symposium on Mathematical Foundations of Computer Science (MFCS 2022)},
  pages =	{53:1--53:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-256-3},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{241},
  editor =	{Szeider, Stefan and Ganian, Robert and Silva, Alexandra},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.MFCS.2022.53},
  URN =		{urn:nbn:de:0030-drops-168517},
  doi =		{10.4230/LIPIcs.MFCS.2022.53},
  annote =	{Keywords: Path cover, k-path, local improvement, amortized analysis, approximation algorithm}
}
Document
DOI: 10.4230/LIPIcs.CPM.2022.15
Polynomial-Time Equivalences and Refined Algorithms for Longest Common Subsequence Variants

Authors: Yuichi Asahiro, Jesper Jansson, Guohui Lin, Eiji Miyano, Hirotaka Ono, and Tadatoshi Utashima

Published in: LIPIcs, Volume 223, 33rd Annual Symposium on Combinatorial Pattern Matching (CPM 2022)

Abstract
The problem of computing the longest common subsequence of two sequences (LCS for short) is a classical and fundamental problem in computer science. In this paper, we study four variants of LCS: the Repetition-Bounded Longest Common Subsequence problem (RBLCS) [Yuichi Asahiro et al., 2020], the Multiset-Restricted Common Subsequence problem (MRCS) [Radu Stefan Mincu and Alexandru Popa, 2018], the Two-Side-Filled Longest Common Subsequence problem (2FLCS), and the One-Side-Filled Longest Common Subsequence problem (1FLCS) [Mauro Castelli et al., 2017; Mauro Castelli et al., 2019]. Although the original LCS can be solved in polynomial time, all these four variants are known to be NP-hard. Recently, an exact, O(1.44225ⁿ)-time, dynamic programming (DP)-based algorithm for RBLCS was proposed [Yuichi Asahiro et al., 2020], where the two input sequences have lengths n and poly(n). We first establish that each of MRCS, 1FLCS, and 2FLCS is polynomially equivalent to RBLCS. Then, we design a refined DP-based algorithm for RBLCS that runs in O(1.41422ⁿ) time, which implies that MRCS, 1FLCS, and 2FLCS can also be solved in O(1.41422ⁿ) time. Finally, we give a polynomial-time 2-approximation algorithm for 2FLCS.
Cite as

Yuichi Asahiro, Jesper Jansson, Guohui Lin, Eiji Miyano, Hirotaka Ono, and Tadatoshi Utashima. Polynomial-Time Equivalences and Refined Algorithms for Longest Common Subsequence Variants. In 33rd Annual Symposium on Combinatorial Pattern Matching (CPM 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 223, pp. 15:1-15:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{asahiro_et_al:LIPIcs.CPM.2022.15,
  author =	{Asahiro, Yuichi and Jansson, Jesper and Lin, Guohui and Miyano, Eiji and Ono, Hirotaka and Utashima, Tadatoshi},
  title =	{{Polynomial-Time Equivalences and Refined Algorithms for Longest Common Subsequence Variants}},
  booktitle =	{33rd Annual Symposium on Combinatorial Pattern Matching (CPM 2022)},
  pages =	{15:1--15:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-234-1},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{223},
  editor =	{Bannai, Hideo and Holub, Jan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CPM.2022.15},
  URN =		{urn:nbn:de:0030-drops-161424},
  doi =		{10.4230/LIPIcs.CPM.2022.15},
  annote =	{Keywords: Repetition-bounded longest common subsequence problem, multiset restricted longest common subsequence problem, one-side-filled longest common subsequence problem, two-side-filled longest common subsequence problem, exact algorithms, and approximation algorithms}
}
Document
DOI: 10.4230/LIPIcs.ISAAC.2017.66
A (1.4 + epsilon)-Approximation Algorithm for the 2-Max-Duo Problem

Authors: Yao Xu, Yong Chen, Guohui Lin, Tian Liu, Taibo Luo, and Peng Zhang

Published in: LIPIcs, Volume 92, 28th International Symposium on Algorithms and Computation (ISAAC 2017)

Abstract
The maximum duo-preservation string mapping (Max-Duo) problem is the complement of the well studied minimum common string partition (MCSP) problem, both of which have applications in many fields including text compression and bioinformatics. k-Max-Duo is the restricted version of Max-Duo, where every letter of the alphabet occurs at most k times in each of the strings, which is readily reduced into the well known maximum independent set (MIS) problem on a graph of maximum degree \Delta \le 6(k-1). In particular, 2-Max-Duo can then be approximated arbitrarily close to 1.8 using the state-of-the-art approximation algorithm for the MIS problem. 2-Max-Duo was proved APX-hard and very recently a (1.6 + \epsilon)-approximation was claimed, for any \epsilon > 0. In this paper, we present a vertex-degree reduction technique, based on which, we show that 2-Max-Duo can be approximated arbitrarily close to 1.4.
Cite as

Yao Xu, Yong Chen, Guohui Lin, Tian Liu, Taibo Luo, and Peng Zhang. A (1.4 + epsilon)-Approximation Algorithm for the 2-Max-Duo Problem. In 28th International Symposium on Algorithms and Computation (ISAAC 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 92, pp. 66:1-66:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{xu_et_al:LIPIcs.ISAAC.2017.66,
  author =	{Xu, Yao and Chen, Yong and Lin, Guohui and Liu, Tian and Luo, Taibo and Zhang, Peng},
  title =	{{A (1.4 + epsilon)-Approximation Algorithm for the 2-Max-Duo Problem}},
  booktitle =	{28th International Symposium on Algorithms and Computation (ISAAC 2017)},
  pages =	{66:1--66:12},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-054-5},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{92},
  editor =	{Okamoto, Yoshio and Tokuyama, Takeshi},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ISAAC.2017.66},
  URN =		{urn:nbn:de:0030-drops-82120},
  doi =		{10.4230/LIPIcs.ISAAC.2017.66},
  annote =	{Keywords: Approximation algorithm, duo-preservation string mapping, string partition, independent set}
}
Document
DOI: 10.4230/LIPIcs.ISAAC.2016.55
Single Machine Scheduling with Job-Dependent Machine Deterioration

Authors: Wenchang Luo, Yao Xu, Weitian Tong, and Guohui Lin

Published in: LIPIcs, Volume 64, 27th International Symposium on Algorithms and Computation (ISAAC 2016)

Abstract
We consider the single machine scheduling problem with job-dependent machine deterioration. In the problem, we are given a single machine with an initial non-negative maintenance level, and a set of jobs each with a non-preemptive processing time and a machine deterioration. Such a machine deterioration quantifies the decrement in the machine maintenance level after processing the job. To avoid machine breakdown, one should guarantee a non-negative maintenance level at any time point; and whenever necessary, a maintenance activity must be allocated for restoring the machine maintenance level. The goal of the problem is to schedule the jobs and the maintenance activities such that the total completion time of jobs is minimized. There are two variants of maintenance activities: in the partial maintenance case each activity can be allocated to increase the machine maintenance level to any level not exceeding the maximum; in the full maintenance case every activity must be allocated to increase the machine maintenance level to the maximum. In a recent work, the problem in the full maintenance case has been proven NP-hard; several special cases of the problem in the partial maintenance case were shown solvable in polynomial time, but the complexity of the general problem is left open. In this paper we first prove that the problem in the partial maintenance case is NP-hard, thus settling the open problem; we then design a 2-approximation algorithm.
Cite as

Wenchang Luo, Yao Xu, Weitian Tong, and Guohui Lin. Single Machine Scheduling with Job-Dependent Machine Deterioration. In 27th International Symposium on Algorithms and Computation (ISAAC 2016). Leibniz International Proceedings in Informatics (LIPIcs), Volume 64, pp. 55:1-55:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

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@InProceedings{luo_et_al:LIPIcs.ISAAC.2016.55,
  author =	{Luo, Wenchang and Xu, Yao and Tong, Weitian and Lin, Guohui},
  title =	{{Single Machine Scheduling with Job-Dependent Machine Deterioration}},
  booktitle =	{27th International Symposium on Algorithms and Computation (ISAAC 2016)},
  pages =	{55:1--55:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-026-2},
  ISSN =	{1868-8969},
  year =	{2016},
  volume =	{64},
  editor =	{Hong, Seok-Hee},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ISAAC.2016.55},
  URN =		{urn:nbn:de:0030-drops-68236},
  doi =		{10.4230/LIPIcs.ISAAC.2016.55},
  annote =	{Keywords: Scheduling, machine deterioration, maintenance, NP-hard, approxima- tion algorithm}
}
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