Abstract

The quadratic shortest path problem (QSPP) is the problem of finding a path with prespecified start vertex s and end vertex t in a digraph such that the sum of weights of arcs and the sum of interaction costs over all pairs of arcs on the path is minimized. We first consider a variant of the QSPP known as the adjacent QSPP. It was recently proven that the adjacent QSPP on cyclic digraphs cannot be approximated unless hbox {P}=hbox {NP}. Here, we give a simple proof for the same result. We also show that if the quadratic cost matrix is a symmetric weak sum matrix and all s–t paths have the same length, then an optimal solution for the QSPP can be obtained by solving the corresponding instance of the shortest path problem. Similarly, it is shown that the QSPP with a symmetric product cost matrix is solvable in polynomial time. Further, we provide sufficient and necessary conditions for a QSPP instance on a complete symmetric digraph with four vertices to be linearizable. We also characterize linearizable QSPP instances on complete symmetric digraphs with more than four vertices. Finally, we derive an algorithm that examines whether a QSPP instance on the directed grid graph G_{pq} (p,qge 2) is linearizable. The complexity of this algorithm is {mathcal {O}(p^{3}q^{2}+p^{2}q^{3})}.

Highlights

  • The shortest path problem (SPP) is the problem of finding a path between two vertices in a directed graph such that the total weight of the arcs on the path is minimized

  • We show that the algorithm fails for the adjacent quadratic shortest path problem (QSPP) considered on directed cyclic graphs, while it performs well on directed acyclic graphs (DAGs)

  • We study the complexity and special cases of the quadratic shortest path problem

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Summary

Introduction

The shortest path problem (SPP) is the problem of finding a path between two vertices in a directed graph such that the total weight of the arcs on the path is minimized. The quadratic shortest path problem (QSPP) is the problem of finding a path between two vertices in a directed graph such that the total weight of the arcs and the sum of interaction costs over all pairs of arcs on the path is minimized. The SPP is a well-studied combinatorial optimization problem, that can be solved in polynomial time if there do not exist negative cycles in the considered graph. Buchheim and Traversi (2015) and Rostami et al (2016) present several approaches to solve the general QSPP. Buchheim and Traversi (2015) consider separable underestimators that can be exploited for solving binary quadratic programming problems, including the QSPP. In this paper we do not investigate computational aspects for solving the QSPP in general

Main results and outline
Problem formulation
Complexity results for the general and adjacent QSPP
The general QSPP
The adjacent QSPP restricted to DAGs
The general adjacent QSPPs
Polynomially solvable cases of the QSPP
Special cost matrices
The QSPP on complete digraphs
The QSPP on directed grid graphs
Conclusion
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