arXiv subjects

Michele Benzi

Publications and source records attributed to Michele Benzi.

At least 19 recordsLinked to original sources

arXiv2026

Convergence and acceleration of a nonlinear fixed-point iteration for computing the Fitness Centrality of general graphs

We establish the global convergence of the (non-homogeneous) Fitness Centrality algorithm for general graphs, deriving an explicit convergence bound for the corresponding fixed-point iteration. Furthermore, we show how the convergence can be dramatically improved by Anderson acceleration and by switching to Newton's method once a sufficiently good approximation to the fixed point has been found. The efficacy of this strategy is illustrated by numerical experiments on different types of graphs.

math.NA↗

arXiv2026

Augmented Lagrangian preconditioners for fictitious domain formulations of elliptic interface problems

We present a novel augmented Lagrangian (AL) preconditioner for the solution of linear systems arising from finite element discretizations of elliptic interface problems with jump coefficients. The method is based on the Fictitious Domain with Distributed Lagrange Multipliers formulation and it is designed to improve the convergence of the Flexible Generalized Minimal Residual (FGMRES) method in the presence of large coefficient jumps. To reduce the computational cost, we also introduce a cheaper block-triangular variant of the preconditioner. We prove eigenvalue clustering for the ideal AL preconditioner and study the limiting behavior of the spectrum for the modified variant in terms of parameters and the size of the jumps. Numerical experiments on different immersed geometries confirm mesh-independent iteration counts and robustness over large coefficient jumps, with substantial reductions in wall-clock time for the modified approach.

math.NA↗

arXiv2026

Scalable augmented Lagrangian preconditioners for fictitious domain problems

We present preconditioning techniques to solve linear systems of equations with a block two-by-two and three-by-three structure arising from finite element discretizations of the fictitious domain method with Lagrange multipliers. In particular, we propose two augmented Lagrangian-based preconditioners to accelerate the convergence of iterative solvers for such classes of linear systems. We consider two relevant examples to illustrate the performance of these preconditioners when used in conjunction with flexible GMRES: the Poisson and the Stokes fictitious domain problems. A spectral analysis is established for both exact and inexact versions of the preconditioners. We show the effectiveness of the proposed approach and the robustness of our preconditioning strategy through extensive numerical tests in both two and three dimensions.

math.NA↗

arXiv2025

Estimation of spectral gaps for sparse symmetric matrices

In this paper we propose and analyze an algorithm for identifying spectral gaps of a real symmetric matrix $A$ by simultaneously approximating the traces of spectral projectors associated with multiple different spectral slices. Our method utilizes Hutchinson's stochastic trace estimator together with the Lanczos algorithm to approximate quadratic forms involving spectral projectors. Instead of focusing on determining the gap between two particular consecutive eigenvalues of $A$, we aim to find all gaps that are wider than a specified threshold. By examining the problem from this perspective, and thoroughly analyzing both the Hutchinson and the Lanczos components of the algorithm, we obtain error bounds that allow us to determine the numbers of Hutchinson's sample vectors and Lanczos iterations needed to ensure the detection of all gaps above the target width with high probability. In particular, we conclude that the most efficient strategy is to always use a single random sample vector for Hutchinson's estimator and concentrate all computational effort in the Lanczos algorithm. Our numerical experiments demonstrate the efficiency and reliability of this approach.

math.NA↗

arXiv2025

A Lie algebra view of matrix splittings

In this paper we use some basic facts from the theory of (matrix) Lie groups and algebras to show that many of the classical matrix splittings used to construct stationary iterative methods and preconditioniers for Krylov subspace methods can be interpreted as linearizations of matrix factorizations. Moreover, we show that new matrix splittings are obtained when we specialize these splittings to some of the classical matrix groups and their Lie and Jordan algebras. As an example, we derive structured generalizations of the HSS (Hermitian and skew-Hermitian splitting) iteration, and provide sufficient conditions for their convergence.

math.NA↗

arXiv2025

Changing the ranking in eigenvector centrality of a weighted graph by small perturbations

In this article, we consider eigenvector centrality for the nodes of a graph and study the robustness (and stability) of this popular centrality measure. For a given weighted graph {\mathcal G} (both directed and undirected), we consider the associated weighted adjacency matrix A, which by definition is a non-negative matrix. The eigenvector centralities of the nodes of {\mathcal G} are the entries of the Perron eigenvector of A, which is the (positive) eigenvector associated with the eigenvalue with largest modulus. They provide a ranking of the nodes according to the corresponding centralities. An indicator of the robustness of eigenvector centrality consists in looking for a nearby perturbed graph \widetilde{\mathcal G}, with the same structure as {\mathcal G} (i.e., with the same vertices and edges), but with a weighted adjacency matrix \widetilde A such that the highest m entries (m \ge 2) of the Perron eigenvector of \widetilde A coalesce, making the ranking at the highest level ambiguous. To compute a solution to this matrix nearness problem, a nested iterative algorithm is proposed that makes use of a constrained gradient system of matrix differential equations in the inner iteration and a one-dimensional optimization of the perturbation size in the outer iteration. The proposed algorithm produces the {\em optimal} perturbation (i.e., the one with smallest Frobenius norm) of the A which causes the looked-for coalescence, which is a measure of the sensitivity of the graph. Our numerical experiments indicate that the proposed strategy outperforms more standard approaches based on algorithms for constrained optimization. The methodology is formulated in terms of graphs but applies to any nonnegative matrix, with potential applications in fields like population models, consensus dynamics, economics, etc.

math.NA↗

arXiv2025

An augmented Lagrangian preconditioner for the control of the Navier--Stokes equations

We address the solution of the distributed control problem for the steady, incompressible Navier--Stokes equations. We propose an inexact Newton linearization of the optimality conditions. Upon discretization by a finite element scheme, we obtain a sequence of large symmetric linear systems of saddle-point type. We use an augmented Lagrangian-based block triangular preconditioner in combination with the flexible GMRES method at each Newton step. The preconditioner is applied inexactly via a suitable multigrid solver. Numerical experiments indicate that the resulting method appears to be fairly robust with respect to viscosity, mesh size, and the choice of regularization parameter when applied to 2D problems.

math.NA↗

arXiv2025

Modeling advection on distance-weighted directed networks

In this paper we propose a model for describing advection dynamics on distance-weighted directed graphs. To this end we establish a set of key properties, or axioms, that a discrete advection operator should satisfy, and prove that there exists an essentially unique operator satisfying all such properties. Both infinite and finite networks are considered, as well as possible variants and extensions. We illustrate the proposed model through examples, both analytical and numerical, and we describe an application to the simulation of a traffic network.

cs.SI↗

arXiv2025

Spectra and pseudospectra in the evaluation of material stability in phase field schemes

We consider the dynamics of bodies with "active" microstructure described by vector-valued phase fields. For waves with time-varying amplitude, the associated evolution equation involves a matrix that can be non-normal, depending on the constitutive choices adopted for the microstructural actions associated with the considered phase field. The occurrence of non-normality requires to look at the pseudospectrum of the considered matrix, namely the set of all possible eigenvalues of matrices in a $\varepsilon$-neighborhood of the matrix itself, because the eigenvalues of non-normal matrices can be very sensitive to small perturbations and therefore the spectral analysis alone would not be sufficient to distinguish with certainty between table and unstable behavior. We develop the relevant analyses in the case of quasicrystals for which the values of some constitutive parameters are not known or are uncertain from an experimental point of view, a circumstance suggesting parametric analyses. We find circumstances in which the pseudospectra obtained by means of the so-called structured perturbations predict instability when, instead, the spectral analysis indicates stability.

math-ph↗

arXiv2024

Accelerating iterative solvers via a two-dimensional minimum residual technique

This paper deals with speeding up the convergence of a class of two-step iterative methods for solving linear systems of equations. To implement the acceleration technique, the residual norm associated with computed approximations for each sub-iterate is minimized over a certain two-dimensional subspace. Convergence properties of the proposed method are studied in detail. The approach is further developed to solve (regularized) normal equations arising from the discretization of ill-posed problems. The results of numerical experiments are reported to illustrate the performance of exact and inexact variants of the method on several test problems from different application areas.

math.NA↗

arXiv2024

Efficient preconditioners for solving dynamical optimal transport via interior point methods

In this paper we address the numerical solution of the quadratic optimal transport problem in its dynamical form, the so-called Benamou-Brenier formulation. When solved using interior point methods, the main computational bottleneck is the solution of large saddle point linear systems arising from the associated Newton-Raphson scheme. The main purpose of this paper is to design efficient preconditioners to solve these linear systems via iterative methods. Among the proposed preconditioners, we introduce one based on the partial commutation of the operators that compose the dual Schur complement of these saddle point linear systems, which we refer as $\boldsymbol{B}\boldsymbol{B}$-preconditioner. A series of numerical tests show that the $\boldsymbol{B}\boldsymbol{B}$-preconditioner is the most efficient among those presented, despite a performance deterioration in the last steps of the interior point method. It is in fact the only one having a CPU-time that scales only slightly worse than linearly with respect to the number of unknowns used to discretize the problem.

math.NA↗

arXiv2023

An augmented Lagrangian-based preconditioning technique for a class of block three-by-three linear systems

We propose an augmented Lagrangian-based preconditioner to accelerate the convergence of Krylov subspace methods applied to linear systems of equations with a block three-by-three structure such as those arising from mixed finite element discretizations of the coupled Stokes-Darcy flow problem. We analyze the spectrum of the preconditioned matrix and we show how the new preconditioner can be efficiently applied. Numerical experiments are reported to illustrate the effectiveness of the preconditioner in conjunction with flexible GMRES for solving linear systems of equations arising from a 3D test problem.

math.NA↗

arXiv2023

Computation of the von Neumann entropy of large matrices via trace estimators and rational Krylov methods

We consider the problem of approximating the von Neumann entropy of a large, sparse, symmetric positive semidefinite matrix $A$, defined as $\operatorname{tr}(f(A))$ where $f(x)=-x\log x$. After establishing some useful properties of this matrix function, we consider the use of both polynomial and rational Krylov subspace algorithms within two types of approximations methods, namely, randomized trace estimators and probing techniques based on graph colorings. We develop error bounds and heuristics which are employed in the implementation of the algorithms. Numerical experiments on density matrices of different types of networks illustrate the performance of the methods.

math.NA↗

arXiv2022

Solving linear systems of the form $(A + γUU^T)\, {\bf x} = {\bf b}$ by preconditioned iterative methods

We consider the iterative solution of large linear systems of equations in which the coefficient matrix is the sum of two terms, a sparse matrix $A$ and a possibly dense, rank deficient matrix of the form $γUU^T$, where $γ> 0$ is a parameter which in some applications may be taken to be 1. The matrix $A$ itself can be singular, but we assume that the symmetric part of $A$ is positive semidefinite and that $A+γUU^T$ is nonsingular. Linear systems of this form arise frequently in fields like optimization, fluid mechanics, computational statistics, and others. We investigate preconditioning strategies based on an alternating splitting approach combined with the use of the Sherman-Morrison-Woodbury matrix identity. The potential of the proposed approach is demonstrated by means of numerical experiments on linear systems from different application areas.

math.NA↗

arXiv2022

Structural analysis of water networks

Liquid water, besides being fundamental for life on Earth, has long fascinated scientists due to several anomalies. Different hypotheses have been put forward to explain these peculiarities. The most accredited one foresees the presence in the supercooled region of two phases at different densities: the low-density liquid phase and the high-density liquid phase. In our previous work [Faccio et al., J. Mol. Liq. 355 (2022): 118922], we showed that it is possible to identify these two forms in water networks through a computational approach based on molecular dynamics simulation and on the calculation of the total communicability of the associated graph, in which the nodes correspond to water molecules and the edges represent the connections (interactions) between molecules. In this paper, we present a more in-depth investigation of the application of graph-theory based approaches to the analysis of the structure of water networks. In particular, we investigate different connectivity and centrality measures and we report on the use of a variety of global metrics aimed at giving a topological and geometrical characterization of liquid water.

math.NA↗

arXiv2022

Solving cubic matrix equations arising in conservative dynamics

In this paper we consider the spatial semi-discretization of conservative PDEs. Such finite dimensional approximations of infinite dimensional dynamical systems can be described as flows in suitable matrix spaces, which in turn leads to the need to solve polynomial matrix equations, a classical and important topic both in theoretical and in applied mathematics. Solving numerically these equations is challenging due to the presence of several conservation laws which our finite models incorporate and which must be retained while integrating the equations of motion. In the last thirty years, the theory of geometric integration has provided a variety of techniques to tackle this problem. These numerical methods require solving both direct and inverse problems in matrix spaces. We present three algorithms to solve a cubic matrix equation arising in the geometric integration of isospectral flows. This type of ODEs includes finite models of ideal hydrodynamics, plasma dynamics, and spin particles, which we use as test problems for our algorithms.

math.NA↗

arXiv2021

New results and open problems on subgraph centrality

Subgraph centrality, introduced by Estrada and Rodr\'iguez-Vel\'azquez in [12], has become a widely used centrality measure in the analysis of networks, with applications in biology, neuroscience, economics and many other fields. It is also worthy of study from a strictly mathematical point of view, in view of its connections to topics in spectral graph theory, number theory, analytic matrix functions, and combinatorics. In this paper we present some new results and a list of open questions about subgraph centrality and other node centrality measures based on graph walks.

math.CO↗

arXiv2021

Low, high and very-high density forms of liquid water revealed by a medium-range order descriptor

We present in this paper a computational approach based on molecular dynamics simulations and graph theory to characterize the structure of liquid water considering not only the local structural arrangement within the first (or second) hydration shell, but also the medium- to long-range order. In particular, a new order parameter borrowed from the graph-theory framework, i.e. the node total communicability (NTC ), is introduced to analyze the dynamic network of water molecules in the liquid phase. This order parameter is able not only to accurately report on the different high-density-liquid (HDL) and low-density-liquid (LDL) water phases postulated in the liquid-liquid phase transition hypothesis, but also to unveil the presence of very high density liquid (VHDL) clusters, both under pressure and at ambient conditions. To the best of our knowledge, VHDL water patches under moderate pressures were not observed before.

cond-mat.soft↗