The Average Solution of a TSP Instance in a Graph

ABSTRACT We define the average ‐TSP distance of a graph as the average length of a shortest closed walk visiting vertices, that is, the expected length of the solution for a random TSP instance with uniformly random chosen vertices. We prove relations with the average ‐Steiner distance and characterize the cases where equality occurs. We also give sharp bounds for given the order of the graph.

Authors

Institutions

Publication Details

Journal
Journal of Graph Theory
Published
2025-02-16
DOI
https://doi.org/10.1002/jgt.23232
Citations
1
Primary Topic
Advanced Graph Theory Research
Type
article
Field-Weighted Citation Impact
1.50

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Average Solution of a TSP Instance in a Graph

Stijn Cambie
1 citations
Journal of Graph Theory
Advanced Graph Theory Research
1.50
article

The Average Solution of a TSP Instance in a Graph

Stijn Cambie
article en
1 citations

Abstract

ABSTRACT We define the average ‐TSP distance of a graph as the average length of a shortest closed walk visiting vertices, that is, the expected length of the solution for a random TSP instance with uniformly random chosen vertices. We prove relations with the average ‐Steiner distance and characterize the cases where equality occurs. We also give sharp bounds for given the order of the graph.

Journal of Graph TheoryVol. 109(3)
KU Leuven (BE)
National Science Foundation, Fonds Wetenschappelijk Onderzoek, Institute for Basic Science
Openalex Percentile: Top 19%
Advanced Graph Theory Research
1.50
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.