Polynomial-space exact algorithm for TSP in degree-5 graphs

2015 ◽  
Author(s):  
N.M. Yunos ◽  
A. Shurbevski ◽  
H. Nagamochi
Keyword(s):  
Exact Algorithm ◽  
Polynomial Space

scholarly journals An Improved-time Polynomial-space Exact Algorithm for TSP in Degree-5 Graphs

2017 ◽  
Vol 25 (0) ◽  
pp. 639-654
Author(s):  
Norhazwani Md Yunos ◽  
Aleksandar Shurbevski ◽  
Hiroshi Nagamochi
Keyword(s):  
Exact Algorithm ◽  
Polynomial Space

scholarly journals New Algorithms for Mixed Dominating Set

2021 ◽  
Vol vol. 23 no. 1 (Discrete Algorithms) ◽  
Author(s):  
Louis Dublois ◽  
Michael Lampis ◽  
Vangelis Th. Paschos
Keyword(s):  
Lower Bound ◽  
Dominating Set ◽  
Exact Algorithm ◽  
Polynomial Space ◽  
Parameterized Algorithms ◽  
Exponential Time ◽  
Open Questions ◽  
Fpt Algorithm ◽  
Exponential Space ◽  
New Algorithms

A mixed dominating set is a collection of vertices and edges that dominates all vertices and edges of a graph. We study the complexity of exact and parameterized algorithms for \textsc{Mixed Dominating Set}, resolving some open questions. In particular, we settle the problem's complexity parameterized by treewidth and pathwidth by giving an algorithm running in time $O^*(5^{tw})$ (improving the current best $O^*(6^{tw})$), as well as a lower bound showing that our algorithm cannot be improved under the Strong Exponential Time Hypothesis (SETH), even if parameterized by pathwidth (improving a lower bound of $O^*((2 - \varepsilon)^{pw})$). Furthermore, by using a simple but so far overlooked observation on the structure of minimal solutions, we obtain branching algorithms which improve both the best known FPT algorithm for this problem, from $O^*(4.172^k)$ to $O^*(3.510^k)$, and the best known exponential-time exact algorithm, from $O^*(2^n)$ and exponential space, to $O^*(1.912^n)$ and polynomial space. Comment: This paper has been accepted to IPEC 2020

Exact algorithm for generating optimal homogenous strip T-shape layouts

2013 ◽  
Vol 32 (9) ◽  
pp. 2634-2637
Author(s):  
Jun JI ◽  
Yi-ping LU ◽  
Jian-zhong ZHA ◽  
Yao-dong CUI
Keyword(s):  
Exact Algorithm

A Combinatorial Problem Which Is Complete in Polynomial Space

1976 ◽  
Vol 23 (4) ◽  
pp. 710-719 ◽  
Author(s):  
S. Even ◽  
R. E. Tarjan
Keyword(s):  
Combinatorial Problem ◽  
Polynomial Space

scholarly journals An exact algorithm for subgraph homeomorphism

2009 ◽  
Vol 7 (4) ◽  
pp. 464-468 ◽  
Author(s):  
Andrzej Lingas ◽  
Martin Wahlen
Keyword(s):  
Exact Algorithm

scholarly journals A Comparison of Genetic Representations and Initialisation Methods for the Multi-objective Shortest Path Problem on Multigraphs

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Lilla Beke ◽  
Michal Weiszer ◽  
Jun Chen
Keyword(s):  
Shortest Path ◽  
Time Budget ◽  
Exact Algorithm ◽  
Simple Graph ◽  
Shortest Path Problem ◽  
Future Application ◽  
Multi Objective ◽  
Trade Offs ◽  
Conflicting Objectives ◽  
Problem Instances

AbstractThis paper compares different solution approaches for the multi-objective shortest path problem (MSPP) on multigraphs. Multigraphs as a modelling tool are able to capture different available trade-offs between objectives for a given section of a route. For this reason, they are increasingly popular in modelling transportation problems with multiple conflicting objectives (e.g., travel time and fuel consumption), such as time-dependent vehicle routing, multi-modal transportation planning, energy-efficient driving, and airport operations. The multigraph MSPP is more complex than the NP-hard simple graph MSPP. Therefore, approximate solution methods are often needed to find a good approximation of the true Pareto front in a given time budget. Evolutionary algorithms have been successfully applied for the simple graph MSPP. However, there has been limited investigation of their applications to the multigraph MSPP. Here, we extend the most popular genetic representations to the multigraph case and compare the achieved solution qualities. Two heuristic initialisation methods are also considered to improve the convergence properties of the algorithms. The comparison is based on a diverse set of problem instances, including both bi-objective and triple objective problems. We found that the metaheuristic approach with heuristic initialisation provides good solutions in shorter running times compared to an exact algorithm. The representations were all found to be competitive. The results are encouraging for future application to the time-constrained multigraph MSPP.

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