arXiv subjects

Mark Embree

Publications and source records attributed to Mark Embree.

At least 19 recordsLinked to original sources

arXiv2026

Continuum Fibonacci Schr\"odinger Operators in the Strongly Coupled Regime

We study Schr\"odinger operators on the real line whose potentials are generated by the Fibonacci substitution sequence and a rule that replaces symbols by compactly supported potential pieces. We consider the case in which one of those pieces is identically zero, and study the dimension of the spectrum in the large-coupling regime. Our results include a generalization of theorems regarding explicit examples that were studied previously and a counterexample that shows that the na\"ive generalization of previously established statements is false. In particular, in the aperiodic case, the local Hausdorff dimension of the spectrum does not necessarily converge to zero uniformly on compact subsets as the coupling constant is sent to infinity.

math.SP↗

arXiv2026

Linear Systems and Eigenvalue Problems: Open Questions from a Simons Workshop

This document presents a series of open questions arising in matrix computations, i.e., the numerical solution of linear algebra problems. It is a result of working groups at the workshop Linear Systems and Eigenvalue Problems, which was organized at the Simons Institute for the Theory of Computing program on Complexity and Linear Algebra in Fall 2025. The complexity and numerical solution of linear algebra problems is a crosscutting area between theoretical computer science and numerical analysis. The value of the particular problem formulations here is that they were produced via discussions between researchers from both groups. The open questions are organized in five categories: iterative solvers for linear systems, eigenvalue computation, low-rank approximation, randomized sketching, and other areas including tensors, quantum systems, and matrix functions. (Updated to reflect the status of the open problems as of August 20, 2026.)

math.NA↗

arXiv2026

A parametric Keldysh decomposition

Contour integral algorithms seek to compute a small number of eigenvalues located within a bounded region of the complex plane. These methods can be applied to both linear and nonlinear matrix eigenvalue problems. In the latter case, the foundation of these methods comes from the Keldysh decomposition, which breaks the nonlinear matrix-valued function into two parts: a rational function whose poles match the desired eigenvalues, and a remainder term that is analytic within the target region. Under contour integration this analytic part vanishes (via Cauchy's theorem), leaving only the component containing the desired eigenvalues. We propose an extension of the Keldysh decomposition for matrix-valued functions that depend analytically on an additional parameter. We establish key properties of this parametric Keldysh decomposition, and introduce an algorithm for solving parametric nonlinear eigenvalue problems that is based upon it.

math.NA↗

arXiv2025

Interpolatory Approximations of PMU Data: Dimension Reduction and Pilot Selection

This work investigates the reduction of phasor measurement unit (PMU) data through low-rank matrix approximations. To reconstruct a PMU data matrix from fewer measurements, we propose the framework of interpolatory matrix decompositions (IDs). In contrast to methods relying on principal component analysis or singular value decomposition, IDs recover the complete data matrix using only a few of its rows (PMU datastreams) and/or a few of its columns (snapshots in time). This row-/column-based compression enables real-time monitoring of power transmission systems using measurements from a smaller subset of pilot datastreams, thereby minimizing communication bandwidth. The ID perspective gives a rigorous error bound on the quality of the data compression. We propose selecting the pilot measurements used in an ID via the discrete empirical interpolation method (DEIM), a greedy algorithm that aims to control the error bound. This bound yields a computable estimate of the reconstruction error during online operations. A violation of this estimate suggests a change in the system's operating conditions and thus serves as a tool for fault detection. Following a disturbance, DEIM can be used to localize the event source across all buses with high accuracy. Numerical tests on synthetic PMU data demonstrate DEIM's excellent performance in data compression and validate the proposed DEIM-based fault-detection and localization method.

eess.SY↗

arXiv2025

Regularizing Ill-Posed Inverse Problems: Deblurring Barcodes

This manuscript is designed to introduce students in applied mathematics and data science to the concept of regularization for ill-posed inverse problems. Construct a mathematical model that describes how an image gets blurred. Convert a calculus problem into a linear algebra problem by discretization. Inverting the blurring process should sharpen up an image; this requires the solution of a system of linear algebraic equations. Solving this linear system of equations turns out to be delicate, as deblurring is an example of an ill-posed inverse problem. To address this challenge, recast the system as a regularized least squares problem (also known as ridge regression).

math.NA↗

arXiv2025

Polynomial Approximation to the Inverse of a Large Matrix

The inverse of a large matrix can often be accurately approximated by a polynomial of degree significantly lower than the order of the matrix. The iteration polynomial generated by a run of the GMRES algorithm is a good candidate, and its approximation to the inverse often seems to track the accuracy of the GMRES iteration. We investigate the quality of this approximation through theory and experiment, noting the practical need to add copies of some polynomial terms to improve stability. To mitigate storage and orthogonalization costs, other approaches have appeal, such as polynomial preconditioned GMRES and deflation of problematic eigenvalues. Applications of such polynomial approximations include solving systems of linear equations with multiple right-hand sides (where the solutions to subsequent problems come simply by multiplying the polynomial against the new right-hand sides) and variance reduction in multilevel Monte Carlo methods.

math.NA↗

arXiv2024

Computing Spectral Size: Rigorous Algorithms and the Limits of Computation

Many structures in mathematical physics and dynamics exhibit intricate fractal geometry. Such behavior appears prominently in quantum mechanics and materials science through spectra of aperiodic and quasicrystalline operators, where questions of ``size'' (Lebesgue measure, fractal dimension, spectral gaps, etc.) are central. Yet the lack of rigorous computational tools for analyzing these quantities limits both theory and application. Na\"ive truncation often fails, and there is no overarching framework to explain what can, and cannot, be computed. We develop a unified program for the rigorous computation of spectral size for bounded self-adjoint operators, based on local spectral exclusions and adaptive covers. This constructive framework yields algorithmically optimal methods (under natural computational assumptions) that bridge spectral theory with computation to address problems previously deemed intractable. Their complexity is classified within the Solvability Complexity Index (SCI) hierarchy, extending Smale's program on the limits of computation. Sharp computational lower bounds are established through impossibility results for limit-periodic Schr\"odinger operators constructed from adversarial potentials. The methods enable state-of-the-art rigorous computations for one- and two-dimensional aperiodic systems, and pinpoint problems where numerics can feed directly into computer-assisted proofs. Beyond spectral analysis, they apply broadly to computing measures of size for general closed sets, opening new directions in the computational study of complex geometric structures.

math.SP↗

arXiv2023

Extending Elman's Bound for GMRES

If the numerical range of a matrix is contained in the right half of the complex plane, the GMRES algorithm for solving linear systems will reduce the norm of the residual at every iteration. In his Ph.D. dissertation, Howard Elman derived a bound that guarantees convergence. When the numerical range contains the origin, GMRES need not make progress at every step and Elman's bound does not apply, even if all the eigenvalues are located in the right half-plane. However by solving a Lyapunov equation, one can construct an inner product in which the numerical range is contained in the right half-plane. One can then bound GMRES (run in the standard Euclidean norm) by applying Elman's bound in this new inner product, at the cost of a multiplicative constant that characterizes the distortion caused by the change of inner product. Using Lyapunov inverse iteration, one can build a family of such inner products, trading off the location of the numerical range with the size of constant. This approach complements techniques that Greenbaum and colleagues have recently proposed for excising the origin from the numerical range to gain greater insight into the convergence of GMRES for nonnormal matrices.

math.NA↗

arXiv2022

Gap Labels for Zeros of the Partition Function of the 1D Ising Model via the Schwartzman Homomorphism

Inspired by the 1995 paper of Baake--Grimm--Pisani, we aim to explain the empirical observation that the distribution of Lee--Yang zeros corresponding to a one-dimensional Ising model appears to follow the gap labelling theorem. This follows by combining two main ingredients: first, the relation between the transfer matrix formalism for 1D Ising model and an ostensibly unrelated matrix formalism generating the Szeg\H{o} recursion for orthogonal polynomials on the unit circle, and second, the gap labelling theorem for CMV matrices.

math.SP↗

arXiv2022

Analysis of GMRES for Low-Rank and Small-Norm Perturbations of the Identity Matrix

In many applications, linear systems arise where the coefficient matrix takes the special form ${\bf I} + {\bf K} + {\bf E}$, where ${\bf I}$ is the identity matrix of dimension $n$, ${\rm rank}({\bf K}) = p \ll n$, and $\|{\bf E}\| \leq \epsilon < 1$. GMRES convergence rates for linear systems with coefficient matrices of the forms ${\bf I} + {\bf K}$ and ${\bf I} + {\bf E}$ are guaranteed by well-known theory, but only relatively weak convergence bounds specific to matrices of the form ${\bf I} + {\bf K} + {\bf E}$ currently exist. In this paper, we explore the convergence properties of linear systems with such coefficient matrices by considering the pseudospectrum of ${\bf I} + {\bf K}$. We derive a bound for the GMRES residual in terms of $\epsilon$ when approximately solving the linear system $({\bf I} + {\bf K} + {\bf E}){\bf x} = {\bf b}$ and identify the eigenvalues of ${\bf I} + {\bf K}$ that are sensitive to perturbation. In particular, while a clustered spectrum away from the origin is often a good indicator of fast GMRES convergence, that convergence may be slow when some of those eigenvalues are ill-conditioned. We show there can be at most $2p$ eigenvalues of ${\bf I} + {\bf K}$ that are sensitive to small perturbations. We present numerical results when using GMRES to solve a sequence of linear systems of the form $({\bf I} + {\bf K}_j + {\bf E}_j){\bf x}_j = {\bf b}_j$ that arise from the application of Broyden's method to solve a nonlinear partial differential equation.

math.NA↗

arXiv2022

Discontinuities of the Integrated Density of States for Laplacians Associated with Penrose and Ammann-Beenker Tilings

Aperiodic substitution tilings provide popular models for quasicrystals, materials exhibiting aperiodic order. We study the graph Laplacian associated with four tilings from the mutual local derivability class of the Penrose tiling, as well as the Ammann--Beenker tiling. In each case we exhibit locally-supported eigenfunctions, which necessarily cause jump discontinuities in the integrated density of states for these models. By bounding the multiplicities of these locally-supported modes, in several cases we provide concrete lower bounds on this jump. These results suggest a host of questions about spectral properties of the Laplacian on aperiodic tilings, which we collect at the end of the paper.

math.SP↗

arXiv2022

How Descriptive are GMRES Convergence Bounds?

GMRES is a popular Krylov subspace method for solving linear systems of equations involving a general non-Hermitian coefficient matrix. The conventional bounds on GMRES convergence involve polynomial approximation problems in the complex plane. Three popular approaches pose this approximation problem on the spectrum, the field of values, or pseudospectra of the coefficient matrix. We analyze and compare these bounds, illustrating with six examples the success and failure of each. When the matrix departs from normality due only to a low-dimensional invariant subspace, we discuss how these bounds can be adapted to exploit this structure. Since the Arnoldi process that underpins GMRES provides approximations to the pseudospectra, one can estimate the GMRES convergence bounds as an iteration proceeds.

math.NA↗

arXiv2020

Contour Integral Methods for Nonlinear Eigenvalue Problems: A Systems Theoretic Approach

Contour integral methods for nonlinear eigenvalue problems seek to compute a subset of the spectrum in a bounded region of the complex plane. We briefly survey this class of algorithms, establishing a relationship to system realization techniques in control theory. This connection motivates a new general framework for contour integral methods (for linear and nonlinear eigenvalue problems), building on recent developments in multi-point rational interpolation of dynamical systems. These new techniques, which replace the usual Hankel matrices with Loewner matrix pencils, incorporate general interpolation schemes and permit ready recovery of eigenvectors. Because the main computations (the solution of linear systems associated with contour integration) are identical for these Loewner methods and the traditional Hankel approach, a variety of new eigenvalue approximations can be explored with modest additional work. Numerical examples illustrate the potential of this approach. We also discuss how the concept of filter functions can be employed in this new framework, and show how contour methods enable a data-driven modal truncation method for model reduction.

math.NA↗

arXiv2020

On the balanced truncation error bound and sign parameters from arrowhead realizations

Balanced truncation and singular perturbation approximation for linear dynamical systems yield reduced-order models that satisfy a well-known error bound involving the Hankel singular values. We show that this bound holds with equality for single-input, single-output systems, if the sign parameters corresponding to the truncated Hankel singular values are all equal. These signs are determined by a generalized state-space symmetry property of the corresponding linear model. For a special class of systems having arrowhead realizations, the signs can be determined directly from the off-diagonal entries of the corresponding arrowhead matrix. We describe how such arrowhead systems arise naturally in certain applications of network modeling, and illustrate these results with a power system model that motivated this study.

eess.SY↗

arXiv2020

Spectral characterization of magic angles in twisted bilayer graphene

Twisted bilayer graphene (TBG) has been experimentally observed to exhibit almost flat bands when the twisting occurs at certain magic angles. In this letter, we report new results on the continuum model of twisted bilayer graphene and its electronic band structure. Under we show that in the approximation of vanishing AA-coupling, the magic angles (at which there exist entirely flat bands) are given as the eigenvalues of a non-hermitian operator, and that all bands start squeezing exponentially fast as the angle $\theta$ tends to $0$. In particular, as the interaction potential changes, the dynamics of magic angles involves the non-physical complex eigenvalues. Using our new spectral characterization, we show that the equidistant scaling of inverse magic angles, is special for the choice of tunnelling potentials in the continuum model, and is not protected by symmetries. While we also show that the protection of zero-energy states holds in the continuum model as long as particle-hole symmetry is preserved, we observe that the existence of flat bands and the exponential squeezing are special properties of the chiral model.

cond-mat.str-el↗

arXiv2020

Mathematics of magic angles in a model of twisted bilayer graphene

We provide a mathematical account of the recent Physical Reviews Letter by Tarnopolsky--Kruchkov--Vishwanath. The new contributions are a spectral characterization of magic angles, its accurate numerical implementation and an exponential estimate on the squeezing of all bands as the angle decreases. Pseudospectral phenomena due to the non-hermitian nature of operators appearing in the model play a crucial role in our analysis.

math-ph↗

arXiv2019

Pseudospectra of Loewner Matrix Pencils

Loewner matrix pencils play a central role in the system realization theory of Mayo and Antoulas, an important development in data-driven modeling. The eigenvalues of these pencils reveal system poles. How robust are the poles recovered via Loewner realization? With several simple examples, we show how pseudospectra of Loewner pencils can be used to investigate the influence of interpolation point location and partitioning on pole stability, the transient behavior of the realized system, and the effect of noisy measurement data. We include an algorithm to efficiently compute such pseudospectra by exploiting Loewner structure.

math.NA↗

arXiv2018

Polynomial Preconditioned Arnoldi

Polynomial preconditioning can improve the convergence of the Arnoldi method for computing eigenvalues. Such preconditioning significantly reduces the cost of orthogonalization; for difficult problems, it can also reduce the number of matrix-vector products. Parallel computations can particularly benefit from the reduction of communication-intensive operations. The GMRES algorithm provides a simple and effective way of generating the preconditioning polynomial. For some problems high degree polynomials are especially effective, but they can lead to stability problems that must be mitigated. A two-level "double polynomial preconditioning" strategy provides an effective way to generate high-degree preconditioners.

math.NA↗