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Spectral Methods in Complex Systems

Statistical Mechanics 2025-09-10 v2 Machine Learning Mathematical Physics math.MP

Abstract

These notes offer a unified introduction to spectral methods for the study of complex systems. They are intended as an operative manual rather than a theorem-proof textbook: the emphasis is on tools, identities, and perspectives that can be readily applied across disciplines. Beginning with a compendium of matrix identities and inversion techniques, the text develops the connections between spectra, dynamics, and structure in finite-dimensional systems. Applications range from dynamical stability and random walks on networks to input-output economics, PageRank, epidemic spreading, memristive circuits, synchronization phenomena, and financial stability. Throughout, the guiding principle is that eigenvalues, eigenvectors, and resolvent operators provide a common language linking problems in physics, mathematics, computer science, and beyond. The presentation is informal, accessible to advanced undergraduates, yet broad enough to serve as a reference for researchers interested in spectral approaches to complex systems.

Keywords

Cite

@article{arxiv.2509.05793,
  title  = {Spectral Methods in Complex Systems},
  author = {Francesco Caravelli},
  journal= {arXiv preprint arXiv:2509.05793},
  year   = {2025}
}

Comments

Expanded and cleaned notes. Based on lectures given at the online school on spectral methods in complex systems (2019); 467 pages. Comments welcome