Differential Equations and Variational Methods on Graphs : With Applications to Machine Learning and Image Analysis (Cambridge Monographs on Applied and Computational Mathematics)

van Gennip, Yves/ Budd, Jeremy

Cambridge University Press（2026/02発売）

• 製本 Hardcover:ハードカバー版／ページ数 389 p.
• 言語 ENG
• 商品コード 9781009556682

Full Description

The burgeoning field of differential equations on graphs has experienced significant growth in the past decade, propelled by the use of variational methods in imaging and by its applications in machine learning. This text provides a detailed overview of the subject, serving as a reference for researchers and as an introduction for graduate students wishing to get up to speed. The authors look through the lens of variational calculus and differential equations, with a particular focus on graph-Laplacian-based models and the graph Ginzburg-Landau functional. They explore the diverse applications, numerical challenges, and theoretical foundations of these models. A meticulously curated bibliography comprising approximately 800 references helps to contextualise this work within the broader academic landscape. While primarily a review, this text also incorporates some original research, extending or refining existing results and methods.

Contents

1. Introduction; 2. Setup; 3. Important models on graphs; 4. Applications; 5. Implementation; 6. Connections between Allen-Cahn, MBO, and MCF; 7. Discrete-to-continuum convergence; 8. Connections with other fields and open questions; Appendix A. Γ-convergence; Appendix B. Steady states of two mass-conserving fidelity-forced diffusion equations; References; Index.