activity
20162026
most citedCan stable and accurate neural networks be computed? -- On the barriers of deep learning and Smale's 18th problem

130 citations · 345 across the 38 of their papers we have counts for

collaborators

47 papers

math.NA2026

Computing Spectral Properties of Differential Operators on Surfaces

Gustav Conradie, Matthew J. Colbrook, Daniel Fortunato

Computing spectra of differential operators on surfaces is crucial in many applications, ranging from medical image analysis to nanoscale quantum structures. However, discretizatio…

math.CO2026

Crown graphs maximise the representation number of bipartite graphs

Matthew J. Colbrook, Catherine Drysdale

The representation number of a graph is the least positive integer for which its vertices can be arranged in a word, each occurring times, so that two distinct letters alte…

math.AP2026

A counterexample to Kenig's conjecture for the Laplace double-layer operator

Matthew J. Colbrook, Siavash Sadeghi

We disprove Kenig's 1994 spectral-radius conjecture for the double-layer operator in every dimension . For every sufficiently close to , we construct a bounded…

math.NA2026

A Complete Resolution of Forsythe's Conjecture for Restarted Conjugate Gradients

Matthew J. Colbrook, George Stepaniants, Alex Townsend

Forsythe's conjecture, published in 1968, asserts that for each restart length , every exact-arithmetic restarted conjugate-gradient iteration on a real symmetric positive defin…

math.SP2026

Finiteness and exponential growth of full graph -spectra

Matthew J. Colbrook

We prove that the full spectrum of the graph -Laplacian is finite for every finite graph and every real . This resolves the problem of finiteness of the full graph spectrum…

math.AP2026

A sharp isoperimetric inequality for the Neumann--Poincaré operator in every dimension

Matthew J. Colbrook, Siavash Sadeghi

Let , , be a bounded connected domain with boundary of class , where . For the adjoint Neumann--Poincaré operator , no…

math.AP2026

A computer-assisted counterexample to the planar Berenstein conjecture

Matthew J. Colbrook, Siavash Sadeghi, George Stepaniants

Recent work of Colbrook and Stepaniants produced the first counterexamples to the planar Pompeiu and Schiffer conjectures and introduced the conformal fixed-disc, disk-polynomial,…

math.NA2026

A Proof of the Forsythe Conjecture for the Two-Step Restarted Conjugate Gradient Method

Matthew J. Colbrook, George Stepaniants, Alex Townsend

Forsythe conjectured in 1968 that the normalized residual directions of restarted conjugate gradients approach a two-cycle. We prove the conjecture for restart length two: unless t…

math.AP2026

A computer-assisted counterexample to the planar Pompeiu and Schiffer conjectures

Matthew J. Colbrook, George Stepaniants

The planar Pompeiu problem, originating in 1929, and the associated Schiffer conjecture are long-standing rigidity questions linking rigid-motion integral transforms and Fourier ze…

math.NA2026

No Spectral Invisibility for Dissipative Barrier Truncations in Any Dimension

Matthew J. Colbrook, Marco Marletta

The dissipative barrier method suppresses spectral pollution, but whether it can itself conceal genuine spectral points has remained open. Known as the graveyard problem in computa…

math.NA2026

Nyström Error Beyond -Matrices: A Minimal Diagonally Dominant Obstruction

Matthew J. Colbrook

We study the nuclear-norm error of a column-selected Nyström approximation to , where is symmetric diagonally dominant and . Our central question is whether…

math.NA2026

Ten Digits on a Train: AI-Assisted Verification of Two Eigenvalue Problems

Matthew J. Colbrook

Accurate numerical eigenvalues are often difficult to certify, especially in singular or non-normal settings. This article reports a human--AI collaboration on two such computation…

quant-ph2025★ 1 cited

Computation and Verification of Spectra for Non-Hermitian Systems

Catherine Drysdale, Matthew Colbrook, Michael T. M. Woodley

We establish a connection between quantum mechanics and computation, revealing fundamental limitations for algorithms computing spectra, especially in non-Hermitian settings. Intro…

math.NA2025

An Introductory Guide to Koopman Learning

Matthew J. Colbrook, Zlatko Drmač, Andrew Horning

Koopman operators provide a linear framework for data-driven analyses of nonlinear dynamical systems, but their infinite-dimensional nature presents major computational challenges.…

math.DS2025

Avoiding spectral pollution for transfer operators using residuals

April Herwig, Matthew J. Colbrook, Oliver Junge +2

Koopman operator theory enables linear analysis of nonlinear dynamical systems by lifting their evolution to infinite-dimensional function spaces. However, finite-dimensional appro…

math.NA2025

Convergent Methods for Koopman Operators on Reproducing Kernel Hilbert Spaces

Nicolas Boullé, Matthew J. Colbrook, Gustav Conradie

Data-driven spectral analysis of Koopman operators is a powerful tool for understanding numerous real-world dynamical systems, from neuronal activity to variations in sea surface t…

cs.LG2025

Deep greedy unfolding: Sorting out argsorting in greedy sparse recovery algorithms

Sina Mohammad-Taheri, Matthew J. Colbrook, Simone Brugiapaglia

Gradient-based learning imposes (deep) neural networks to be differentiable at all steps. This includes model-based architectures constructed by unrolling iterations of an iterativ…

math.NA2025

Universal Methods for Nonlinear Spectral Problems

Matthew J. Colbrook, Catherine Drysdale

Nonlinear spectral problems arise across a range of fields, including mechanical vibrations, fluid-solid interactions, and photonic crystals. Discretizing infinite-dimensional nonl…

math.NA2024

Computing Generalized Eigenfunctions in Rigged Hilbert Spaces

Matthew J. Colbrook, Andrew Horning, Tianyiwa Xie

We introduce a simple, general, and convergent scheme to compute generalized eigenfunctions of self-adjoint operators with continuous spectra on rigged Hilbert spaces. Our approach…

cond-mat.stat-mech2024★ 1 cited

Bridging the Gap between Koopmanism and Response Theory: Using Natural Variability to Predict Forced Response

Niccolò Zagli, Matthew Colbrook, Valerio Lucarini +2

The fluctuation-dissipation theorem is a cornerstone result in statistical mechanics that can be used to translate the statistics of the free natural variability of a system into i…

math.SP2024★ 1 cited

A Novel Use of Pseudospectra in Mathematical Biology: Understanding HPA Axis Sensitivity

Catherine Drysdale, Matthew J. Colbrook

The Hypothalamic-Pituitary-Adrenal (HPA) axis is a major neuroendocrine system, and its dysregulation is implicated in various diseases. This system also presents interesting mathe…

math.SP2024

Computing Spectral Size: Rigorous Algorithms and the Limits of Computation

Matthew J. Colbrook, Mark Embree, Jake Fillman

Many structures in mathematical physics and dynamics exhibit intricate fractal geometry. Such behavior appears prominently in quantum mechanics and materials science through spectr…

math.DS2024

Adversarial dynamical systems characterize when data-driven learning succeeds or fails

Matthew J. Colbrook, Igor Mezić, Alexei Stepanenko

Many systems resist analytical modeling, making data-driven inference of dynamics important. Yet data-driven methods can fail to converge or generalize, leaving open a central ques…

math.DS2024

Multiplicative Dynamic Mode Decomposition

Nicolas Boullé, Matthew J. Colbrook

Koopman operators are infinite-dimensional operators that linearize nonlinear dynamical systems, facilitating the study of their spectral properties and enabling the prediction of…