arXiv subjects

Daniel Kressner

Publications and source records attributed to Daniel Kressner.

At least 19 recordsLinked to original sources

arXiv2026

Stochastic trace estimation for positive trace-class operators

Implicit trace estimation aims to approximate the trace of a matrix or linear operator accessible only through matrix-vector or operator-vector products. In the matrix setting, the Girard-Hutchinson estimator typically requires $\mathcal{O}(\varepsilon^{-2})$ products to achieve accuracy $\varepsilon$, while the variance-reduced Hutch++ algorithm reduces this sample complexity to $\mathcal{O}(\varepsilon^{-1})$ for positive semidefinite matrices. We develop infinite-dimensional analogues of these estimators for positive trace-class operators on separable Hilbert spaces. The idealized estimators use Gaussian random elements whose covariance is determined by the target operator, leading to unbiased operator versions of Girard-Hutchinson and Hutch++. We prove high-probability error bounds analogous to the finite-dimensional matrix results; in particular, idealized infHutch++ achieves $\mathcal{O}(\varepsilon^{-1})$ sample complexity. For practical computation, we introduce truncated implementations that restrict the random samples to finite-dimensional subspaces; for fixed sample budget, we show that truncated infHutch++ converges in distribution to its idealized counterpart as the truncation dimension tends to infinity. Numerical experiments with integral operators, density-of-states approximations, and spectral filtering for a radial Dirac operator show that these truncated estimators can achieve accuracy comparable to the ContHutch++ algorithm by Zvonek, Horning & Townsend while using lower-degree function representations and smaller internal discretizations in chebfun.

math.NA↗

arXiv2026

A counterexample to the symmetric-maximizer conjecture for Lyapunov operators

It has been conjectured that the operator norm of the Lyapunov operator induced by the Frobenius norm is always attained at a symmetric matrix. The conjecture is known to hold for all matrices of order at most five. We give an integer matrix of order seven for which the skew-symmetric restricted norm is strictly larger than the symmetric restricted norm. A rational separator and exact-arithmetic certificates establish the strict inequality without relying on floating-point computations. A direct-sum construction yields counterexamples in every order $n \geq 7$; the case $n = 6$ remains open.

math.NA↗

arXiv2026

Halving the size of skew-symmetric eigenvalue problems via the polar decomposition

This paper introduces a novel algorithm for computing eigenvalues and eigenvectors of a dense real skew-symmetric matrix $A$. Its main ingredient is the computation of a polar factor of $A$ that is both skew-symmetric and orthogonal. This polar factor is then used to transform the original problem into a Hermitian eigenvalue problem of half the size, which can be solved accurately and efficiently with standard software such as LAPACK. Numerical experiments demonstrate the stability of the method and show that its running time is competitive with existing approaches for skew-symmetric eigenvalue problems. Finally, we show that the same principle can be used to reduce an orthogonal eigenvalue problem to a unitary eigenvalue problem of half the size.

math.NA↗

arXiv2026

A mixed precision algorithm for the matrix square root

Mixed precision algorithms can significantly enhance the performance of linear algebra solvers by leveraging increasingly powerful low precision hardware while recovering working precision accuracy through, for example, iterative refinement. In this paper, we propose a novel mixed precision algorithm for computing matrix square roots. Our algorithm combines a Schur decomposition approach in low precision with iterative refinement performed through an approximate Newton method. We perform a detailed convergence analysis of the approximate Newton method. For the special case of symmetric positive definite matrices, this analysis implies that one can recover full working precision accuracy under mild conditions. Numerical experiments on x86-64 architectures indicate that our algorithm frequently reduces execution time compared with a fixed working-precision Schur algorithm.

math.NA↗

arXiv2026

Stochastic trace estimation with tensor train random vectors

Stochastic trace estimation is a standard tool for approximating the trace of a large-scale matrix available only through matrix-vector products. However, in tensor-structured settings, unstructured Gaussian or Rademacher test vectors may be prohibitively expensive to store and compute with, while cheaper rank-one tensor-product vectors can require sample complexities that grow exponentially with the tensor order. This work studies Gaussian random tensor train vectors as a structured alternative for stochastic trace estimation. We show that, with a suitable choice of the tensor train rank, random tensor train vectors recover dimension-independent guarantees for the Girard--Hutchinson estimator. In particular, a median-of-means variant with tensor train rank $r \geq d-1$ achieves the same dependence on the accuracy $\varepsilon$ and failure probability $\delta$ as the classical estimator based on unstructured Gaussian vectors. We further prove an oblivious subspace injection result for sketches formed from independent Gaussian random tensor train vectors: tensor train rank $r\geq d-1$ and $\mathcal{O}(\varepsilon^{-2}(k+\log(1/\delta)))$ samples suffice for a $k$-dimensional target subspace. Finally, we investigate the use of such sketches within the Nystr\"{o}m++ framework. We show that the resulting estimator can achieve the desired $\mathcal{O}(\varepsilon^{-1})$ sample complexity under an additional spectral-tail condition. These results provide clarififcation on both the potential and the limitations of random tensor train vectors in stochastic trace estimation.

stat.ML↗

arXiv2026

Linear convergence of iterative contour integral-based eigensolvers for nonlinear eigenvalue problems

Solving nonlinear eigenvalue problems is an important and challenging task in scientific computing. Contour integral-based approaches are attractive for such eigenvalue problems because they reliably target all eigenvalues in a prescribed domain. However, unlike in the linear case, many traditional methods of this type, such as Beyn's method, lack an inherent iterative refinement mechanism. Consequently, achieving high accuracy requires high-quality quadrature rules for approximating the contour integral, which often leads to prohibitive computational costs. A notable exception is the so-called NLFEAST algorithm, which combines contour integral techniques with a nonlinear Rayleigh--Ritz extraction step. In this work, we propose a general framework of iterative contour integral-based methods for nonlinear eigenvalue problems that includes NLFEAST. This allows us to prove linear convergence of NLFEAST under mild assumptions and also explains why certain nonlinear eigensolvers do not combine well with iterative methods. Numerical experiments confirm our theoretical findings; in particular that NLFEAST can achieve high accuracy even with a limited number of quadrature nodes, significantly outperforming Beyn's method on challenging problems.

math.NA↗

arXiv2026

Kernel-based linear system identification using augmented Krylov subspaces

We propose a novel Krylov subspace method for estimating the finite impulse response (FIR) of a one-dimensional linear time-invariant systems. The method approximates the system's FIR using a kernel-based formulation combined with hyperparameter selection based on maximum likelihood estimation (MLE), which requires repeated evaluation of two terms: The data fit $\boldsymbol{y}^{\top} (\lambda \boldsymbol{I} + \boldsymbol{A})^{-1} \boldsymbol{y}$ and the model complexity $\log(\det (\lambda \boldsymbol{I} + \boldsymbol{A}))$, where $\boldsymbol{A}$ is a certain positive semidefinite matrix that admits fast matrix--vector products and $\lambda > 0$ is a regularization parameter. Instead of approximating these two quantities separately, we jointly approximate them using a single augmented Krylov subspace for $\boldsymbol{A}$. One major benefit of augmentation is that we obtain accelerated convergence when approximating the data fit quadratic form, through implicit preconditioning. Thanks to the shift invariance of Krylov subspaces, the extracted approximations can be used to evaluate the MLE objective for many values of $\lambda$ at little additional cost. We derive error bounds for the approximations, reflecting the benefits of augmentation demonstrated through multiple numerical experiments.

math.NA↗

arXiv2026

Lanczos with compression for symmetric eigenvalue problems

The Lanczos method with implicit restarting is one of the most popular methods for finding a few exterior eigenpairs of a large symmetric matrix $A$. Usually based on polynomial filtering, restarting is crucial to limit memory and the cost of orthogonalization. In this work, we propose a novel strategy for the same purpose, called Lanczos with compression. Unlike polynomial filtering, our approach compresses the Krylov subspace using rational approximation and, in doing so, it sacrifices the structure of the associated Krylov decomposition. Nevertheless, it remains compatible with subsequent Lanczos steps and the overall algorithm is still solely based on matrix-vector products with $A$. On the theoretical side, we show that compression introduces only a small error compared to standard (unrestarted) Lanczos and therefore has only a negligible impact on convergence. Comparable guarantees are not available for commonly used implicit restarting strategies, including the Krylov--Schur method. On the practical side, our numerical experiments demonstrate the potential of compression to outperform the Krylov--Schur method in terms of matrix-vector products.

math.NA↗

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 novel Krylov subspace method for approximating Fr\'echet derivatives of large-scale matrix functions

We present a novel Krylov subspace method for approximating $L_f(A, E) \vc{b}$, the matrix-vector product of the Fr\'echet derivative $L_f(A, E)$ of a large-scale matrix function $f(A)$ in direction $E$, a task that arises naturally in the sensitivity analysis of quantities involving matrix functions, such as centrality measures for networks. It also arises in the context of gradient-based methods for optimization problems that feature matrix functions, e.g., when fitting an evolution equation to an observed solution trajectory. In principle, the well-known identity \[ f\left( \begin{bmatrix} A & E \\ 0 & A \end{bmatrix} \right) \begin{bmatrix} 0 \\ \vc{b} \end{bmatrix} = \begin{bmatrix} L_f(A, E) \vc{b} \\ f(A) \vc{b} \end{bmatrix}, \] allows one to directly apply any standard Krylov subspace method, such as the Arnoldi algorithm, to address this task. However, this comes with the major disadvantage that the involved block triangular matrix has unfavorable spectral properties, which impede the convergence analysis and, to a certain extent, also the observed convergence. To avoid these difficulties, we propose a novel modification of the Arnoldi algorithm that aims at better preserving the block triangular structure. In turn, this allows one to bound the convergence of the modified method by the best polynomial approximation of the derivative $f^\prime$ on the numerical range of $A$. Several numerical experiments illustrate our findings.

math.NA↗

arXiv2026

Restoring similarity in randomized Krylov methods with applications to eigenvalue problems and matrix functions

The randomized Arnoldi process has been used in large-scale scientific computing because it produces a well-conditioned basis for the Krylov subspace more quickly than the standard Arnoldi process. However, the resulting Hessenberg matrix is generally not similar to the one produced by the standard Arnoldi process, which can lead to delays or spike-like irregularities in convergence. In this paper, we introduce a modification of the randomized Arnoldi process that restores similarity with the Hessenberg matrix generated by the standard Arnoldi process. This is accomplished by enforcing orthogonality between the last Arnoldi vector and the previously generated subspace, which requires solving only one additional least-squares problem. When applied to eigenvalue problems and matrix function evaluations, the modified randomized Arnoldi process produces approximations that are identical to those obtained with the standard Arnoldi process. Numerical experiments demonstrate that our approach is as fast as the randomized Arnoldi process and as robust as the standard Arnoldi process.

math.NA↗

arXiv2025

Subspace Acceleration for Efficient Nonlinear Water Wave Simulation

Efficient simulation of nonlinear and dispersive free-surface flows governed by the incompressible Navier-Stokes equations remains a central challenge in ocean and coastal engineering. The computational bottleneck arises from solving a time-dependent discretized Poisson problem at every time step to enforce divergence free flow. This is crucial to ensure conservation of mass and requires solving long sequences of time-dependent linear systems typically using iterative methods, such as the preconditioned Krylov subspace methods. In this work, we investigate new subspace acceleration techniques for improving initial guesses to reduce the number of iterations required by iterative solvers, with a focus on nonlinear wave propagation problems. We extend the original subspace acceleration method by incorporating the complete history of previous solutions through an exponentially weighted formulation. This approach eliminates the need for repeated sketching and orthonormalization, resulting in a more efficient and scalable strategy to generate better initial guesses. Our method is implemented within a high-order finite-difference framework using a method-of-lines formulation and a low-storage Runge-Kutta time integration scheme. We demonstrate that subspace acceleration significantly reduces the number of GMRES iterations when solving the Poisson equation in nonlinear water wave simulations. Performance is evaluated on two benchmark problems: nonlinear stream function wave propagation and harmonic wave generation over a submerged bar. In both cases, the new approach achieves substantial improvements in computational efficiency without compromising accuracy. Although demonstrated using high-order finite difference methods, the technique is discretization independent and broadly applicable to incompressible free-surface flow solvers.

physics.flu-dyn↗

arXiv2025

Interpolatory Dynamical Low-Rank Approximation: Theoretical Foundations and Algorithms

Dynamical low-rank approximation (DLRA) is a widely used paradigm for solving large-scale matrix differential equations, as they arise, for example, from the discretization of time-dependent partial differential equations on tensorized domains. Through orthogonally projecting the dynamics onto the tangent space of a low-dimensional manifold, DLRA achieves a significant reduction of the storage required to represent the solution. However, the need for evaluating the velocity field can make it challenging to attain a corresponding reduction of computational cost in the presence of nonlinearities. In this work, we address this challenge by replacing orthogonal tangent space projections with oblique, data-sparse projections selected by a discrete empirical interpolation method (DEIM). At the continuous-time level, this leads to DLRA-DEIM, a well-posed differential inclusion (in the Filippov sense) that captures the discontinuities induced by changes in the indices selected by DEIM. We establish an existence result, exactness property and error bound for DLRA-DEIM that match existing results for DLRA. For the particular case of QDEIM, a popular variant of DEIM, we provide an explicit convex-polytope characterization of the differential inclusion. Building on DLRA-DEIM, we propose a new class of projected integrators, called PRK-DEIM, that combines explicit Runge--Kutta methods with DEIM-based projections. We analyze the convergence order of PRK-DEIM and show that it matches the accuracy of previously proposed projected Runge-Kutta methods, while being significantly cheaper. Extensions to exponential Runge--Kutta methods and low-order tensor differential equations demonstrate the versatility of our framework.

math.NA↗

arXiv2025

RJD-BASE: Multi-Modal Spectral Clustering via Randomized Joint Diagonalization

We revisit the problem of spectral clustering in multimodal settings, where each data modality is encoded as a graph Laplacian. While classical approaches--including joint diagonalization, spectral co-regularization, and multiview clustering--attempt to align embeddings across modalities, they often rely on costly iterative refinement and may fail to directly target the spectral subspace relevant for clustering. In this work, we introduce two key innovations. First, we bring the power of randomization to this setting by sampling random convex combinations of Laplacians as a simple and scalable alternative to explicit eigenspace alignment. Second, we propose a principled selection rule based on Bottom-$k$ Aggregated Spectral Energy (BASE)--a $k$-dimensional extension of the directional smoothness objective from recent minimax formulations--which we uniquely apply as a selection mechanism rather than an optimization target. The result is Randomized Joint Diagonalization with BASE Selection (RJD-BASE), a method that is easily implementable, computationally efficient, aligned with the clustering objective, and grounded in decades of progress in standard eigensolvers. Through experiments on synthetic and real-world datasets, we show that RJD-BASE reliably selects high-quality embeddings, outperforming classical multimodal clustering methods at low computational cost.

math.NA↗

arXiv2025

Accelerated Solvers for Neutral Particle Dynamics in Plasma Simulation

The simulation of turbulence in the boundary region of a tokamak is crucial for understanding and optimizing the performance of fusion reactors. In this work, the use of low-rank linear algebra techniques is shown to enhance the efficiency of boundary simulations, specifically by accelerating the solution of a kinetic model for the neutral particles. Solving the kinetic model deterministically using the method of characteristics requires the solution of integral equations, which typically result in dense linear systems upon discretization. We employ hierarchical matrix approximations to significantly reduce the computational cost of assembling and solving the linear systems, leading to substantial savings in both time and memory. The hierarchical matrix method is implemented and tested within the GBS simulation code for boundary simulations, achieving over 90\% reduction in computation time and memory, and enabling simulations with unprecedented spatial resolution for neutral particles.

physics.plasm-ph↗

arXiv2025

On the randomized SVD in infinite dimensions

Randomized methods, such as the randomized SVD (singular value decomposition) and Nystr\"om approximation, are an effective way to compute low-rank approximations of large matrices. Motivated by applications to operator learning, Boull\'e and Townsend (FoCM, 2023) recently proposed an infinite-dimensional extension of the randomized SVD for a Hilbert-Schmidt operator $A$ that invokes randomness through a Gaussian process with a covariance operator $K$. While the non-isotropy introduced by $K$ allows one to incorporate prior information on $A$, an unfortunate choice may lead to unfavorable performance and large constants in the error bounds. In this work, we introduce a novel infinite-dimensional extension of the randomized SVD that does not require such a choice and enjoys error bounds that match those for the finite-dimensional case. Our extension implicitly uses isotropic random vectors, reflecting a choice commonly made in the finite-dimensional case. In fact, the theoretical results of this work show how the usual randomized SVD applied to a discretization of $A$ approaches our infinite-dimensional extension as the discretization gets refined, both in terms of error bounds and the Wasserstein distance. We also present and analyze a novel extension of the Nystr\"om approximation for self-adjoint positive semi-definite trace class operators.

math.NA↗

arXiv2025

Lanczos with compression for symmetric matrix Lyapunov equations

This work considers large-scale Lyapunov matrix equations of the form $AX + XA = \boldsymbol{c}\boldsymbol{c}^T$, where $A$ is a symmetric positive definite matrix and $\boldsymbol{c}$ is a vector. Motivated by the need to solve such equations in a wide range of applications, various numerical methods have been developed to compute low-rank approximations of the solution matrix $X$. In this work, we focus on the Lanczos method, which has the distinct advantage of requiring only matrix-vector products with $A$, making it broadly applicable. However, the Lanczos method may suffer from slow convergence when $A$ is ill-conditioned, leading to excessive memory requirements for storing the Krylov subspace basis generated by the algorithm. To address this issue, we propose a novel compression strategy for the Krylov subspace basis that significantly reduces memory usage without hindering convergence. This is supported by both numerical experiments and a convergence analysis. Our analysis also accounts for the loss of orthogonality due to round-off errors in the Lanczos process.

math.NA↗

arXiv2025

Stochastic trace estimation for parameter-dependent matrices applied to spectral density approximation

Stochastic trace estimation is a well-established tool for approximating the trace of a large symmetric matrix $\boldsymbol{B}$. Several applications involve a matrix that depends continuously on a parameter $t \in [a,b]$, and require trace estimates of $\boldsymbol{B}(t)$ for many values of $t$. This is, for example, the case when approximating the spectral density of a matrix. Approximating the trace separately for each matrix $\boldsymbol{B}(t_1), \dots, \boldsymbol{B}(t_m)$ clearly incurs redundancies and a cost that scales linearly with $m$. To address this issue, we propose and analyze modifications for three stochastic trace estimators, the Girard-Hutchinson, Nystr\"om, and Nystr\"om++ estimators. Our modification uses fixed randomization across different values of $t$, that is, every matrix $\boldsymbol{B}(t_1), \dots, \boldsymbol{B}(t_m)$ is multiplied with the same set of random vectors. When combined with Chebyshev approximation in $t$, the use of such constant random matrices allows one to reuse matrix-vector products across different values of $t$, leading to significant cost reduction. Our analysis shows that the loss of stochastic independence across different $t$ does not lead to deterioration. In particular, we show that $\mathcal{O}(\varepsilon^{-1})$ random matrix-vector products suffice to ensure an error of $\varepsilon > 0$ for Nystr\"om++, independent of low-rank properties of $\boldsymbol{B}(t)$. We discuss in detail how the combination of Nystr\"om++ with Chebyshev approximation applies to spectral density estimation and provide an analysis of the resulting method. This improves various aspects of an existing stochastic estimator for spectral density estimation. Several numerical experiments from electronic structure interaction and neural network optimization validate our findings.

math.NA↗