Learning to sparsify travelling salesman problem instances

DOI10.1007/978-3-030-78230-6_26OpenAlexW3174346336MaRDI QIDQ2117244

James Fitzpatrick, Deepak Ajwani, Paula Carroll

Publication date: 21 March 2022

Full work available at URL: https://arxiv.org/abs/2104.09345

zbMATH Keywords

linear programminginteger programmingtravelling salesman problemmachine learninggraph sparsification

Mathematics Subject Classification ID

Combinatorial optimization (90C27) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Uses Software

• NetworkX
• TSPLIB
• SCIP
• LKH
• Scikit
• PySCIPOpt

Cites Work

• Unnamed Item
• Unnamed Item
• Mixed integer programming: A historical perspective with Xpress-MP
• A method to compute the sparse graphs for traveling salesman problem based on frequency quadrilaterals
• Certification of an optimal TSP tour through 85,900 cities
• ``Neural computation of decisions in optimization problems
• An effective implementation of the Lin-Kernighan traveling salesman heuristic
• Generalization of machine learning for problem reduction: a case study on travelling salesman problems
• PySCIPOpt: Mathematical Programming in Python with the SCIP Optimization Suite
• Edge Elimination in TSP Instances
• Graph spanners
• TSPLIB—A Traveling Salesman Problem Library
• Exploiting Erraticism in Search
• A General Framework for Graph Sparsification
• Solution of a Large-Scale Traveling-Salesman Problem

This page was built for publication: Learning to sparsify travelling salesman problem instances