Abstract

Graph matching has received persistent attention over several decades, which can be formulated as a quadratic assignment problem (QAP). We show that a large family of functions, which we define as Separable Functions, can approximate discrete graph matching in the continuous domain asymptotically by varying the approximation controlling parameters. We also study the properties of global optimality and devise convex/concave-preserving extensions to the widely used Lawler's QAP form. Our theoretical findings show the potential for deriving new algorithms and techniques for graph matching. We deliver solvers based on two specific instances of Separable Functions, and the state-of-the-art performance of our method is verified on popular benchmarks.

Original languageEnglish (US)
Pages (from-to)853-863
Number of pages11
JournalAdvances in Neural Information Processing Systems
Volume2018-December
StatePublished - Jan 1 2018
Event32nd Conference on Neural Information Processing Systems, NeurIPS 2018 - Montreal, Canada
Duration: Dec 2 2018Dec 8 2018

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Cite this

Generalizing graph matching beyond quadratic assignment model. / Yu, Tianshu; Yan, Junchi; Wang, Yilin; Liu, Wei; Li, Baoxin.

In: Advances in Neural Information Processing Systems, Vol. 2018-December, 01.01.2018, p. 853-863.

Research output: Contribution to journalConference article

Yu, Tianshu ; Yan, Junchi ; Wang, Yilin ; Liu, Wei ; Li, Baoxin. / Generalizing graph matching beyond quadratic assignment model. In: Advances in Neural Information Processing Systems. 2018 ; Vol. 2018-December. pp. 853-863.
@article{79fa03d796734be280b803300cd509a1,
title = "Generalizing graph matching beyond quadratic assignment model",
abstract = "Graph matching has received persistent attention over several decades, which can be formulated as a quadratic assignment problem (QAP). We show that a large family of functions, which we define as Separable Functions, can approximate discrete graph matching in the continuous domain asymptotically by varying the approximation controlling parameters. We also study the properties of global optimality and devise convex/concave-preserving extensions to the widely used Lawler's QAP form. Our theoretical findings show the potential for deriving new algorithms and techniques for graph matching. We deliver solvers based on two specific instances of Separable Functions, and the state-of-the-art performance of our method is verified on popular benchmarks.",
author = "Tianshu Yu and Junchi Yan and Yilin Wang and Wei Liu and Baoxin Li",
year = "2018",
month = "1",
day = "1",
language = "English (US)",
volume = "2018-December",
pages = "853--863",
journal = "Advances in Neural Information Processing Systems",
issn = "1049-5258",

}

TY - JOUR

T1 - Generalizing graph matching beyond quadratic assignment model

AU - Yu, Tianshu

AU - Yan, Junchi

AU - Wang, Yilin

AU - Liu, Wei

AU - Li, Baoxin

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Graph matching has received persistent attention over several decades, which can be formulated as a quadratic assignment problem (QAP). We show that a large family of functions, which we define as Separable Functions, can approximate discrete graph matching in the continuous domain asymptotically by varying the approximation controlling parameters. We also study the properties of global optimality and devise convex/concave-preserving extensions to the widely used Lawler's QAP form. Our theoretical findings show the potential for deriving new algorithms and techniques for graph matching. We deliver solvers based on two specific instances of Separable Functions, and the state-of-the-art performance of our method is verified on popular benchmarks.

AB - Graph matching has received persistent attention over several decades, which can be formulated as a quadratic assignment problem (QAP). We show that a large family of functions, which we define as Separable Functions, can approximate discrete graph matching in the continuous domain asymptotically by varying the approximation controlling parameters. We also study the properties of global optimality and devise convex/concave-preserving extensions to the widely used Lawler's QAP form. Our theoretical findings show the potential for deriving new algorithms and techniques for graph matching. We deliver solvers based on two specific instances of Separable Functions, and the state-of-the-art performance of our method is verified on popular benchmarks.

UR - http://www.scopus.com/inward/record.url?scp=85064816710&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85064816710&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85064816710

VL - 2018-December

SP - 853

EP - 863

JO - Advances in Neural Information Processing Systems

JF - Advances in Neural Information Processing Systems

SN - 1049-5258

ER -