Spectral Radius Research Articles

On Spectral Radius and Energy of a Graph with Self-Loops

The spectral radius of a square matrix is the maximum among absolute values of its eigenvalues. Suppose a square matrix is nonnegative; then, by Perron–Frobenius theory, it will be one among its eigenvalues. In this paper, Perron–Frobenius theory for adjacency matrix of graph with self-loops AGS will be explored. Specifically, it discusses the nontrivial existence of Perron–Frobenius eigenvalue and eigenvector pair in the matrix AGS−σnI, where σ denotes the number of self-loops. Also, Koolen–Moulton type bound for the energy of graph GS is explored. In addition, the existence of a graph with self-loops for every odd energy is proved.

Co-spectral radius for countable equivalence relations

Abstract We define the co-spectral radius of inclusions ${\mathcal S}\leq {\mathcal R}$ of discrete, probability- measure-preserving equivalence relations as the sampling exponent of a generating random walk on the ambient relation. The co-spectral radius is analogous to the spectral radius for random walks on $G/H$ for inclusion $H\leq G$ of groups. For the proof, we develop a more general version of the 2–3 method we used in another work on the growth of unimodular random rooted trees. We use this method to show that the walk growth exists for an arbitrary unimodular random rooted graph of bounded degree. We also investigate how the co-spectral radius behaves for hyperfinite relations, and discuss new critical exponents for percolation that can be defined using the co-spectral radius.

A study on the influence of closure model and numerical technology on the stability of two-fluid model

The influence of closure model and numerical technology on two-fluid model’s stability is studied. A stability analysis method for two-fluid model with interfacial drag term is proposed. The stability depends on the eigenvalue spectral radius of a perturbation growth rate matrix. By analyzing eigenvalue spectral radius, the effect of interfacial drag, cell size, time step and time-smoothing is studied. The main findings are: Besides interfacial drag coefficient’s magnitude, the mathematic form of drag coefficient model also has large effect. Increase of cell size, decrease of time step and time-smoothing do not necessarily lead to stability enhancement. The effectiveness of stability enhancement technology depends on relationship between spectral radius and cell size, time step and time-smoothing coefficient.

Distance signless Laplacian spectral radius for the existence of path-factors in graphs

Distance signless Laplacian spectral radius for the existence of path-factors in graphs

Asymptotic profiles of basic reproduction number for a degenerate reaction–diffusion host–pathogen model

In this paper, the basic reproduction number (BRN) is explored in terms of a degenerate reaction–diffusion host–pathogen model. The expressions of BRN and local basic reproduction number (LBRN) for the model are calculated. Some basic properties of with respect to the general diffusion rates and the asymptotic profiles of with respect to large diffusion rates and small diffusion rates are established by introducing two compact operators and . Our results reveal that is the spectral radius of a sum of the product of additive operators and strongly positive compact linear operators with spectral radii one.

Dominant eigenvalue and universal winners of digraphs

Let A be a nonnegative irreducible matrix of order n and Ai(t) be the matrix obtained by increasing its ith diagonal entry by a positive number t. An index i∈[n]={1,…,n} is called a universal winner for A, if, letting ρ(⋅) denote the spectral radius, ρ(Ai(t))≥ρ(Aj(t)) for j∈[n] and for t∈(0,∞).Let G be a strongly connected digraph with vertex set [n]. Let WG be the set of all nonnegative matrices whose underlying graph structure is G. We say a vertex u structurally dominates another vertex v in G, if ρ(Au(t))≥ρ(Av(t)) for all t∈(0,∞) and for all A∈WG. We characterize the class of digraphs G that do not have a vertex that structurally dominates all other vertices. We say two vertices u and v structurally tie if they structurally dominate each other in G. We supply an equivalent graph theoretic condition for the structural tying of two vertices in G. Let S⊆[n] be nonempty. We characterize the class of strongly connected digraphs G with vertex set [n] such that S is the set of universal winners for each A∈WG. We also characterize the strongly connected digraphs whose vertex set can be partitioned into subsets P1,P2,…,Pk such that vertices inside a part Pi structurally tie with each other and vertices of Pi structurally dominate vertices of Pj strictly (without tying) for i<j.

Almost sure and mean square convergence of iterative learning control system with state feedback under random packet dropouts

This paper investigates the almost sure and mean square convergence of a state feedback-based iterative learning control scheme for a class of discrete-time multi-input–multi-output linear systems with a direct feedback term in the presence of random data dropouts. Firstly, the realisability of the control system is considered. It is shown that the realisability of the control system is only determined by the direct feedback matrix, which has nothing to do with the input–output coupling matrix, and the direct feedback matrix to be full-row rank is enough to guarantee the system realisable. Secondly, the convergence of output tracking errors is analysed in both the almost sure and mean square senses, respectively. By the contraction mapping method combined with matrix decomposition and transformation techniques, it is strictly proved that the output sequence can converge to any given desired trajectory in the sense of almost sure and mean square, respectively, if and only if the general spectral radius condition is satisfied. Finally, an example is given to show the correctness of the theoretical analysis.

A probabilistic framework for describing a class of switching signals in continuous-time

We present an interpretation of switching signals with certain average dwell time in terms of Poisson process, which allows to build a probabilistic framework to accommodate all the switching signals of this kind. As a result, we can learn more knowledge about such signals. In particular, such a switching signal and the solution of the corresponding switching system jointly constitute a Markov process. And the Markov property will facilitate the asymptotic behaviour analysis of the switching dynamics. As a byproduct, we present an upper bound on the joint spectral radius of a family of Hurwitz or anti-Hurwitz matrices, which turns out to be quite sharp as compared with the existing results.

A CMOS-compatible oscillation-based VO2 Ising machine solver

Phase-encoded oscillating neural networks offer compelling advantages over metal-oxide-semiconductor-based technology for tackling complex optimization problems, with promising potential for ultralow power consumption and exceptionally rapid computational performance. In this work, we investigate the ability of these networks to solve optimization problems belonging to the nondeterministic polynomial time complexity class using nanoscale vanadium-dioxide-based oscillators integrated onto a Silicon platform. Specifically, we demonstrate how the dynamic behavior of coupled vanadium dioxide devices can effectively solve combinatorial optimization problems, including Graph Coloring, Max-cut, and Max-3SAT problems. The electrical mappings of these problems are derived from the equivalent Ising Hamiltonian formulation to design circuits with up to nine crossbar vanadium dioxide oscillators. Using sub-harmonic injection locking techniques, we binarize the solution space provided by the oscillators and demonstrate that graphs with high connection density (η > 0.4) converge more easily towards the optimal solution due to the small spectral radius of the problem’s equivalent adjacency matrix. Our findings indicate that these systems achieve stability within 25 oscillation cycles and exhibit power efficiency and potential for scaling that surpasses available commercial options and other technologies under study. These results pave the way for accelerated parallel computing enabled by large-scale networks of interconnected oscillators.

Upper Bounds for the Spectral Radius of a PF Matrix

Upper Bounds for the Spectral Radius of a PF Matrix

The signless Laplacian spectral radius of graphs without intersecting odd cycles

Let $F_{a_1,\dots,a_k}$ be a graph consisting of $k$ cycles of odd length $2a_1+1,\dots, 2a_k+1,$ respectively, which intersect in exactly one common vertex, where $k\geq1$ and $a_1\ge a_2\ge \cdots\ge a_k\ge 1$. In this paper, we present a sharp upper bound for the signless Laplacian spectral radius of all $F_{a_1,\dots,a_k}$-free graphs and characterize all extremal graphs which attain the bound. The stability methods and structure of graphs associated with the eigenvalue are adapted for the proof.

Robust-to-Dynamics Optimization

A robust-to-dynamics optimization (RDO) problem is an optimization problem specified by two pieces of input: (i) a mathematical program (an objective function [Formula: see text] and a feasible set [Formula: see text]) and (ii) a dynamical system (a map [Formula: see text]). Its goal is to minimize f over the set [Formula: see text] of initial conditions that forever remain in [Formula: see text] under g. The focus of this paper is on the case where the mathematical program is a linear program and where the dynamical system is either a known linear map or an uncertain linear map that can change over time. In both cases, we study a converging sequence of polyhedral outer approximations and (lifted) spectrahedral inner approximations to [Formula: see text]. Our inner approximations are optimized with respect to the objective function f, and their semidefinite characterization—which has a semidefinite constraint of fixed size—is obtained by applying polar duality to convex sets that are invariant under (multiple) linear maps. We characterize three barriers that can stop convergence of the outer approximations to [Formula: see text] from being finite. We prove that once these barriers are removed, our inner and outer approximating procedures find an optimal solution and a certificate of optimality for the RDO problem in a finite number of steps. Moreover, in the case where the dynamics are linear, we show that this phenomenon occurs in a number of steps that can be computed in time polynomial in the bit size of the input data. Our analysis also leads to a polynomial-time algorithm for RDO instances where the spectral radius of the linear map is bounded above by any constant less than one. Finally, in our concluding section, we propose a broader research agenda for studying optimization problems with dynamical systems constraints, of which RDO is a special case. Funding: O. Günlük was partially supported by the Office of Naval Research [Grant N00014-21-1-2575]. This work was partially funded by the Alfred P. Sloan Foundation, the Air Force Office of Scientific Research, Defense Advanced Research Projects Agency [Young Faculty Award], the National Science Foundation [Faculty Early Career Development Program Award], and Google [Faculty Award].

Convergence Study of 2D Transport Method for Multigroup k-Eigenvalue Problems with High-Order Scattering Source Using Fourier Analysis

The convergence behavior of a two-dimensional (2D) transport method has been derived by Fourier analysis for single-group problems with isotropic sources. However, in real calculation, to pursue precision, a high-order scattering source is a common option, and its influence on convergence performance is worth investigating. No theoretical convergence study of a 2D transport method for multigroup problems with high-order scattering sources was previously performed, but it is important work that would complement existing studies. This study presents a Fourier analysis for solving multigroup problems with high-order scattering. First, the influences of the number of inner iterations for the multigroup isotropic scattering problem are analyzed. It is found that with an increase of the number of inner iterations, the spectral radius decreases and finally reaches an asymptotic value. When the scattering ratio is increased, more inner iterations are required to reach the asymptotic value. Then, the influences of high-order scattering are analyzed. The Fourier analysis results show that for high-order scattering source problems, the influence of the number of inner iterations is different from the isotropic scattering case. The influences of first-order scattering and second-order scattering are not the same. With an increase of first-order scattering, the spectral radius first decreases in the small optical thickness region and then increases in the large optical thickness region, which may lead to the divergence of iterations. If second-order scattering is not too large, an increase of second-order scattering decreases the spectral radius for all optical thickness regions. First-order scattering and second-order scattering that are too large may result in an unpredictable slope of the spectral radius for optical thicknesses between 10−1 and 1.

The Graft Transformation and Their Application on the Spectral Radius of Block Graphs

Let [Formula: see text] be a connected graph and [Formula: see text] be the adjacency matrix of [Formula: see text]. Suppose that [Formula: see text] are the eigenvalues of [Formula: see text]. In this paper, we first give a graft transformation on the spectral radius of graphs and then as their application, we determine the extremal graphs with maximum and minimum spectral radii among all clique trees. Furthermore, we also determine the unique graph with maximum spectral radius among all block graphs by using different methods.

On the spectral radius properties of a key matrix in periodic impulse control

In this work, we consider the periodic impulse control of a system modeled as a set of linear differential equations. We define a matrix that governs the qualitative behavior of the controlled system. This matrix depends on the period and effects of the control interventions. We investigate properties of the spectral radius of this matrix and in particular, how it depends on the period of the interventions. Our main result is on the convexity of the spectral radius with respect to this period. We discuss implications of this convexity on establishing an optimal and maximum period for effective control. Finally, we provide an example motivated from a real-life scenario.

The Index Weighted Hermitian Adjacency Matrices for Mixed Graphs

Based on the Hermitian adjacency matrices of second kind introduced by Mohar [1] and weighted adjacency matrices introduced in [2], we define a kind of index weighted Hermitian adjacency matrices of mixed graphs. In this paper we characterize the structure of mixed graphs which are cospectral to their underlying graphs, then we determine a upper bound on the spectral radius of mixed graphs with maximum degree Δ , and characterize the corresponding extremal graphs.

Learning stability of partially observed switched linear systems

This paper deals with learning stability of partially observed switched linear systems under arbitrary switching. Such systems are widely used to describe cyber–physical systems which arise by combining physical systems with digital components. In many real-world applications, the internal states cannot be observed directly. It is thus more realistic to conduct system analysis using the outputs of the system. Stability is one of the most frequent requirement for safety and robustness of cyber–physical systems. Existing methods for analyzing stability of switched linear systems often require the knowledge of the parameters and/or all the states of the underlying system. In this paper, we propose an algorithm for deciding stability of switched linear systems under arbitrary switching based purely on observed output data. The proposed algorithm essentially relies on an output-based Lyapunov stability framework and returns an estimate of the joint spectral radius (JSR). We also prove a probably approximately correct error bound on the quality of the estimate of the JSR from the perspective of statistical learning theory.

Hermitian skew Laplacian matrix of oriented graphs

Let [Formula: see text] be a simple graph of order [Formula: see text] having [Formula: see text] edges and let [Formula: see text] be an orientation of [Formula: see text]. The hermitian skew Laplacian matrix [Formula: see text] of [Formula: see text] is defined as [Formula: see text], where [Formula: see text] is the imaginary unit, [Formula: see text] is the diagonal matrix of oriented degrees [Formula: see text] and [Formula: see text] is the skew matrix. The spectral radius of the matrix [Formula: see text] is the hermitian skew Laplacian spectral radius and the sum of the absolute values of eigenvalues of [Formula: see text] is the hermitian skew Laplacian energy of [Formula: see text]. In this paper we study the hermitian spectral radius and hermitian skew Laplacian energy of [Formula: see text]. We obtain some new bounds for the hermitian skew Laplacian spectral radius and characterize the extremal oriented graphs attaining these bounds. Further, we obtain some bounds for the hermitian skew Laplacian energy and locate the extremal oriented graphs.

On the spectral radius of graphs with given maximum degree and girth

We prove upper bounds for the spectral radius ρ(G) of an n-vertex graph with given maximum degree and girth at least 2ℓ+1. This extends the previous result of [9] regarding graphs with girth at least five. When ℓ=3 or |V(G)| is relatively small compared with the maximum degree, our upper bounds are sharp. In addition, for a tree T, we provide an upper bound for the spectral radius of an n-vertex T-free graph with given maximum degree. This bound is also sharp for a certain class of trees.

Newman-Watts-Strogatz topology in deep echo state networks for speech emotion recognition

Cortical neural connectivity exhibits a small-world (SW) network topology. However, its role in neural information processing remains unclear. In this study, we investigate the learning performance of a Deep Echo State Network (DeepESN) that incorporates the Newman-Watts-Strogatz (NWS) graph topology as a reservoir. The short path length of the SW topology allows input information to reach output nodes faster, and the clustered arrangement of this topology can expand the range of the echo state property. Additionally, in this paper, we seek to identify the best set of these key factors, such as the spectral radius of the reservoir’s weight matrix and the hyperparameters neighbors' and probabilities. We evaluate the NWS-DeepESNs on four Speech Emotion Recognition (SER) tasks, which are RAVDESS, Emo-db, SAVEE, and TESS databases. By examining the NWS-DeepESN’s performance on SER tasks, we aim to determine the role of the NWS graph topology in enhancing neural information processing. The obtained results exhibited significant improvements in recognition accuracies (weighted and unweighted) with (76.45%, 75.79%), (87.89%, 87.14%), (99%, 98.62%), and (63%, 62.24%), respectively, for RAVDESS, Emo-db, SAVEE, and TESS databases. The proposed approach improves emotion recognition capabilities and yields more accurate results compared to existing state-of-the-art approaches.