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In this paper, a new fuzzy Functional observer is developed for nonlinear neutral systems. Also, the existence conditions for it are studied. The delay-dependent stability of this observer is guaranteed by the combination of the solution of the Sylvester equation and the Lyapunov–Krasovskii stability approach. The parameters of the studied observer are obtained by solving linear matrix inequalities (LMIs). The performance of the approach developed in this paper is demonstrated at the end of the paper by numerical examples. [ABSTRACT FROM AUTHOR]

This paper addresses the dynamic quaternion-valued Sylvester equation (DQSE) using the quaternion real representation and the neural network method. To transform the Sylvester equation in the quaternion field into an equivalent equation in the real field, three different real representation modes for the quaternion are adopted by considering the non-commutativity of quaternion multiplication. Based on the equivalent Sylvester equation in the real field, a novel recurrent neural network model with an integral design formula is proposed to solve the DQSE. The proposed model, referred to as the fixed-time error-monitoring neural network (FTEMNN), achieves fixed-time convergence through the action of a state-of-the-art nonlinear activation function. The fixed-time convergence of the FTEMNN model is theoretically analyzed. Two examples are presented to verify the performance of the FTEMNN model with a specific focus on fixed-time convergence. Furthermore, the chattering phenomenon of the FTEMNN model is discussed, and a saturation function scheme is designed. Finally, the practical value of the FTEMNN model is demonstrated through its application to image fusion denoising. [ABSTRACT FROM AUTHOR]

For a linear matrix function f in X ∈ R m × n we consider inhomogeneous linear matrix equations f (X) = E for E ≠ 0 that have or do not have solutions. For such systems we compute optimal norm constrained solutions iteratively using the Conjugate Gradient and Lanczos’ methods in combination with the More–Sorensen optimizer. We build codes for ten linear matrix equations, of Sylvester, Lyapunov, Stein and structured types and their T-versions, that differ only in two five times repeated equation specific code lines. Numerical experiments with linear matrix equations are performed that illustrate universality and efficiency of our method for dense and small data matrices, as well as for sparse and certain structured input matrices. Specifically we show how to adapt our universal method for sparse inputs and for structured data such as encountered when fusing image data sets via a Sylvester equation algorithm to obtain an image of higher resolution. [ABSTRACT FROM AUTHOR]

Given the matrix equation A X + X B + f (X) C = D in the unknown n × m matrix X , we analyze existence and uniqueness conditions, together with computational solution strategies for f : R n × m → R being a linear or nonlinear function. We characterize different properties of the matrix equation and of its solution, depending on the considered classes of functions f. Our analysis mainly concerns small dimensional problems, though several considerations also apply to large scale matrix equations. [ABSTRACT FROM AUTHOR]

The Sylvester equation plays an important role in many branches of mathematical physics. The goal of this paper is to show that a special case of the Sylvester equation can be related to the lattice B-type Kadomtsev–Petviashvili (BKP) system via the generalized Cauchy matrix method. We use the variables given in the Sylvester equation to define the function and several scalar functions that are closely related to the lattice BKP equation. After rederiving the lattice BKP equation, we make it clear that besides its multisoliton solutions, various other types of exact solutions also exist. Furthermore, Lax pairs for the lattice BKP equation are obtained in different ways. [ABSTRACT FROM AUTHOR]

This work is devoted to solve the state and error-feedback control problems for a catalytic reverse flow reactor (CFRR), which is modeled by nonlinear partial differential equations (PDEs). These two regulation problems will be solved based on the linearized infinite-dimensional representation. The objective is to track a desired output reference under the presence of disturbances. Both the reference trajectory and the disturbance profiles are generated by a distributed parameter exosystem. First, a state feedback stabilizing regulator is designed which drives the process output to a reference trajectory. The second main aim is to develop a dynamical controller that uses the tracking error as an input. Furthermore, it has been demonstrated that the closed-loop plant is exponentially stable and the tracking error asymptotically goes to zero. The developed regulators are evaluated through numerical simulations for the case study of methane combustion. • Description of the reverse flow reactor by PDEs model with its infinite-dimensional representation. • Design of a state-feedback regulator for the CFRR model under the presence of distributed disturbances. • Design of an error-feedback regulator for the CFRR model under the presence of distributed disturbances. • Test the performances of the designed regulators through numerical simulations for methane combustion. [ABSTRACT FROM AUTHOR]

This paper aims at finding a fixed-time solution to the Sylvester equation by using a gradient neural network (GNN). To reach this goal, a modified sign-bi-power (msbp) function is presented and applied on a linear GNN as an activation function. Accordingly, a fixed-time convergent GNN (FTC-GNN) model is developed for solving the Sylvester equation. The upper bound of the convergence time of such an FTC-GNN model can be predetermined if parameters are given regardless of the initial conditions. This point is corroborated by a detailed theoretical analysis. In addition, the convergence time is also estimated utilizing the Lyapunov stability theory. Two examples are then simulated to demonstrate the validation of the theoretical analysis, as well as the superior convergence performance of the presented FTC-GNN model as compared to the existing GNN models. [ABSTRACT FROM AUTHOR]

The split-step Padé approach is an extremely efficient tool for the integration of pseudodifferential parabolic equations, which are widely used for the modeling of acoustic wave propagation. In this study, a generalization of this method to the case of parabolic equations with unknown vector functions is presented. Such generalization requires an algorithm for efficient numerical evaluation of a function of a matrix with differential operators as its elements. After a finite-difference discretization this algorithm reduces to the solution of several Sylvester-like problems. The generalized split-step Padé method presented here is an attractive tool for solving 3D problems of sound propagation in the ocean within the framework of coupled mode parabolic equations theory. • A method is proposed for the numerical solution of the one-way counterpart of a system of coupled Helmholtz equations. • The method is based on a generalization of the split-step Padé algorithm to the case of unknown vector functions. • The method is an attractive tool for handling many practical problems where the propagation of coupled guided modes takes place. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) super-resolution is commonly used to overcome the hardware limitations of existing hyperspectral imaging systems on spatial resolution. It fuses a low-resolution (LR) HSI and a high-resolution (HR) conventional image of the same scene to obtain an HR HSI. In this work, we propose a method that integrates a physical model and deep prior information. Specifically, a novel, yet effective two-stream fusion network is designed to serve as a regularizer for the fusion problem. This fusion problem is formulated as an optimization problem whose solution can be obtained by solving a Sylvester equation. Furthermore, the regularization parameter is simultaneously estimated to automatically adjust contribution of the physical model and the learned prior to reconstruct the final HR HSI. Experimental results on both simulated and real data demonstrate the superiority of the proposed method over other state-of-the-art methods on both quantitative and qualitative comparisons. [ABSTRACT FROM AUTHOR]

In this paper, we seek connections between the Sylvester equation and Kadomtsev–Petviashvili system. By introducing Sylvester equation L M are bold, please chekc if bold neceaasry, if not, please remove all bold of equation − M K = r s T together with an evolution equation set of r and s , master function S (i , j) = s T K j C (I + M C) − 1 L i r is used to construct the Kadomtsev–Petviashvili system, including the Kadomtsev–Petviashvili equation, modified Kadomtsev–Petviashvili equation and Schwarzian Kadomtsev–Petviashvili equation. The matrix M provides τ -function by τ = | I + M C |. With the help of some recurrence relations, the reductions to the Korteweg–de Vries and Boussinesq systems are discussed. [ABSTRACT FROM AUTHOR]

We consider the numerical solution of the large scale singular Sylvester equation of the form A X − X D ⊤ = E (or A X B ⊤ − C X D ⊤ = E), where the spectra Λ (A) and Λ (D) (or, Λ (A − λ C) and Λ (D − λ B)) have a nonempty intersection. Using appropriate invariant subspaces, the singular Sylvester equation is rewritten as four Sylvester equations, three of which are nonsingular and one singular. When the invariant subspaces are small, so are three of the equations (including the singular one) which can be solved efficiently. The fourth is large but nonsingular with structures and may be solved using the projection method with Krylov subspaces or techniques involving hierarchical matrices. Some numerical examples for the subspace method are provided. [ABSTRACT FROM AUTHOR]

Let A be a unital complex semisimple Banach algebra, and M A denote its maximal ideal space. For a matrix M ∈ A n × n , M ˆ denotes the matrix obtained by taking entry-wise Gelfand transforms. For a matrix M ∈ C n × n , σ (M) ⊂ C denotes the set of eigenvalues of M. It is shown that if A ∈ A n × n and B ∈ A m × m are such that for all φ ∈ M A , σ (A ˆ (φ)) ∩ σ (B ˆ (φ)) = ∅ , then for all C ∈ A n × m , the Sylvester equation A X − X B = C has a unique solution X ∈ A n × m. As an application, Roth's removal rule is proved in the context of matrices over a Banach algebra. [ABSTRACT FROM AUTHOR]

We discuss the use of a matrix‐oriented approach for numerically solving the dense matrix equation AX + XAT + M1XN1 + ... + MℓXNℓ = F, with ℓ ≥ 1, and Mi, Ni, i = 1, ... , ℓ of low rank. The approach relies on the Sherman–Morrison–Woodbury formula formally defined in the vectorized form of the problem, but applied in the matrix setting. This allows one to solve medium size dense problems with computational costs and memory requirements dramatically lower than with a Kronecker formulation. Application problems leading to medium size equations of this form are illustrated and the performance of the matrix‐oriented method is reported. The application of the procedure as the core step in the solution of the large‐scale problem is also shown. In addition, a new explicit method for linear tensor equations is proposed, that uses the discussed matrix equation procedure as a key building block. [ABSTRACT FROM AUTHOR]

We consider the numerical solution of large scale singular (continuous‐time) Lyapunov equations of the form AX + XA⊤ + BB⊤ = 0, where A is semistable, that is, its spectrum is contained in the left half plane, with the exception of a few semisimple eigenvalues at zero. We also consider the case of a few semisimple eigenvalues on the imaginary axis. We assume that we know these few eigenvalues (zero or imaginary), and that we have or can compute the corresponding invariant subspaces. We use this information to build an appropriate newly proposed subspace on which to project the Lyapunov equations, and then compute a low‐rank approximation to the least squares solution. Selected illustrative numerical examples are provided. [ABSTRACT FROM AUTHOR]

We investigate the factorized solution of generalized stable Sylvester equations such as those arising in model reduction, image restoration, and observer design. Our algorithms, based on the matrix sign function, take advantage of the current trend to integrate high performance graphics accelerators (also known as GPUs) in computer systems. As a result, our realisations provide a valuable tool to solve large-scale problems on a variety of platforms. [ABSTRACT FROM AUTHOR]

This paper introduces a novel framework for the solution of (large-scale) Lyapunov and Sylvester equations derived from numerical integration methods. Suitable systems of ordinary differential equations are introduced. Low rank approximations of their solutions are produced by Runge-Kutta methods. Appropriate Runge-Kutta methods are identified following the idea of geometric numerical integration to preserve a geometric property, namely a low rank residual. For both types of equations we prove the equivalence of one particular instance of the resulting algorithm to the well known ADI iteration. [ABSTRACT FROM AUTHOR]

Sylvester equation is often applied to various fields, such as mathematics and control systems due to its importance. Zeroing neural network (ZNN), as a systematic design method for time-variant problems, has been proved to be effective on solving Sylvester equation in the ideal conditions. In this paper, in order to realize the predefined-time convergence of the ZNN model and modify its robustness, two new noise-tolerant ZNNs (NNTZNNs) are established by devising two novelly constructed nonlinear activation functions (AFs) to find the accurate solution of the time-variant Sylvester equation in the presence of various noises. Unlike the original ZNN models activated by known AFs, the proposed two NNTZNN models are activated by two novel AFs, therefore, possessing the excellent predefined-time convergence and strong robustness even in the presence of various noises. Besides, the detailed theoretical analyses of the predefined-time convergence and robustness ability for the NNTZNN models are given by considering different kinds of noises. Simulation comparative results further verify the excellent performance of the proposed NNTZNN models, when applied to online solution of the time-variant Sylvester equation. [ABSTRACT FROM AUTHOR]

In this paper we aim to derive solutions for the SU(N) self-dual Yang–Mills (SDYM) equation with arbitrary N. A set of noncommutative relations are introduced to construct a matrix equation that can be reduced to the SDYM equation. It is shown that these relations can be generated from two different Sylvester equations, which correspond to the two Cauchy matrix schemes for the (matrix) Kadomtsev–Petviashvili hierarchy and the (matrix) Ablowitz–Kaup–Newell–Segur hierarchy, respectively. In each Cauchy matrix scheme we investigate the possible reductions that can lead to the SU (N) SDYM equation and also analyze the physical significance of some solutions, i.e. being Hermitian, positive-definite and of determinant being one. • Solutions of the SU(N) SDYM equation with arbitrary N are constructed. • The SU(N) SDYM is formulated by the Cauchy matrix scheme of the KP hierarchy. • The SU(2N) SDYM is formulated by the Cauchy matrix scheme of the AKNS hierarchy. • Some solutions of the SU(2N) SDYM obtained in the two schemes are analyzed. [ABSTRACT FROM AUTHOR]

The aim of this paper is to study some features of slice semi-regular functions SEM (Ω) on a circular domain Ω contained in the skew-symmetric algebra of quaternions H via the analysis of a family of linear operators built from left and right ⁎-multiplication on SEM (Ω) ; this class of operators includes the family of Sylvester-type operators S f , g. Our goal is achieved by a strategy based on a matrix interpretation of these operators as we show that SEM (Ω) can be seen as a 4-dimensional vector space on the field SEM R (Ω). We then study the rank of S f , g and describe its kernel and image when it is not invertible, finding meaningful differences in the cases when the rank is either 2 or 3. By using these results, we are able to characterize when the functions f and g are either equivalent under ⁎-conjugation or intertwined by means of a zero divisor, thus proving a number of statements on the behaviour of slice semi-regular functions. In this way, information about the operator obtained by linear algebra techniques give as a significant application the solution of a problem in an area of function theory which had a remarkable development in the last decade (see [16]). We also provide a complete classification of idempotents and zero divisors on product domains of H. [ABSTRACT FROM AUTHOR]

For a division ring F , the polynomials f ∈ F [ z ] can be evaluated "on the left" and "on the right" giving rise to left and right Lagrange interpolation problems. The problems containing interpolation conditions of the same type were considered in [7] where the solvability criterion was given in terms of polynomial independence of interpolation nodes. We establish the solvability criterion and describe all solutions of low degree (less than the number of interpolation conditions imposed) for the problem containing both "left" and "right" conditions. [ABSTRACT FROM AUTHOR]

Recursive blocked algorithms have proven to be highly efficient at the numerical solution of the Sylvester matrix equation and its generalizations. In this work, we show that these algorithms extend in a seamless fashion to higher-dimensional variants of generalized Sylvester matrix equations, as they arise from the discretization of PDEs with separable coefficients or the approximation of certain models in macroeconomics. By combining recursions with a mechanism for merging dimensions, an efficient algorithm is derived that outperforms existing approaches based on Sylvester solvers. [ABSTRACT FROM AUTHOR]

In this paper, we propose a space-time least-squares isogeometric method to solve parabolic evolution problems, well suited for high-degree smooth splines in the space-time domain. We focus on the linear solver and its computational efficiency: thanks to the proposed formulation and to the tensor-product construction of space-time splines, we can design a preconditioner whose application requires the solution of a Sylvester-like equation, which is performed efficiently by the fast diagonalization method. The preconditioner is robust w.r.t. spline degree and mesh size. The computational time required for its application, for a serial execution, is almost proportional to the number of degrees-of-freedom and independent of the polynomial degree. The proposed approach is also well-suited for parallelization. [ABSTRACT FROM AUTHOR]

A linear quaternionic equation in one quaternionic variable q is of the form a 1 q b 1 + a 2 q b 2 + ⋯ + a m q b m = c , where the a i , b j , c are given quaternionic coefficients. If introducing basis elements i , j , k of pure quaternions, then the quaternionic equation becomes four linear equations in four unknowns over the reals, and solving such equations is trivial. On the other hand, finding a quaternionic rational function expression of the solution that involves only the input quaternionic coefficients and their conjugates, called a basis-free solution, is non-trivial. In 1884, Sylvester initiated the study of basis-free solution to linear quaternionic equation. He considered the three-termed equation a q + q b = c , and found its solution q = (a 2 + b b ¯ + a (b + b ¯)) − 1 (a c + c b ¯) by successive left and right multiplications. In 2013, Schwartz extended the technique to the four-termed equation, and obtained the basis-free solution in explicit form. This paper solves the general problem for arbitrary number of terms in the non-degenerate case. [ABSTRACT FROM AUTHOR]

We consider the solution of systems of linear matrix equations in two or three unknown matrices. For dense problems we derive algorithms that determine the numerical solution by only involving matrices of the same size as those in the original problem, thus requiring low computational resources. For large and structured systems we show how the problem properties can be exploited to design effective algorithms with low memory and operation requirements. Numerical experiments illustrate the performance of the new methods. [ABSTRACT FROM AUTHOR]

This work is about a balanced truncation type order reduction method which is developed for stable and unstable large-scale continuous-time systems. In this method, a quantitative measure criterion for choosing the dominant eigenvalues helps in determining the steady-state and transient information of the dynamical system. These dominant eigenvalues are used to form a new substructure matrix that retains the dominant modes (or may desirable mode) of the original system. Retaining the dominant eigenvalues in the reduced mode assures stability and results in greater accuracy as the retained eigenvalues provides a physical link to the real system. In the quest to preserve the dominant eigenvalue of the real system, the proposed technique uses Sylvester equation for system transformation. Having obtained transformed model, the reduced model has been achieved by truncating the non-dominant eigenvalues using the singular perturbation approximation method. The efficiency and accuracy of the proposed method has been demonstrated by the benchmark test systems which were from the state-of-the-art models. [ABSTRACT FROM AUTHOR]

In this paper we apply the results obtained in [3] to establish some outcomes of the study of the behaviour of a class of linear operators, which include the Sylvester ones, acting on slice semi-regular functions. We first present a detailed study of the kernel of the linear operator ℒf,g (when not trivial), showing that it has dimension 2 if exactly one between f and g is a zero divisor, and it has dimension 3 if both f and g are zero divisors. Afterwards, we deepen the analysis of the behaviour of the -product, giving a complete classification of the cases when the functions fv, gv and fv gv are linearly dependent and obtaining, as a by-product, a necessary and sufficient condition on the functions f and g in order their *-product is slice-preserving. At last, we give an Embry-type result which classifies the functions f and g such that for any function h commuting with f + g and f * g, we have that h commutes with f and g, too. [ABSTRACT FROM AUTHOR]

Projection methods based on Krylov subspaces are an effective tool for solving low-rank Sylvester matrix equations in the large scale cases. The initial problem is projected onto a sequence of nested subspaces. At each step of the process, a reduced Sylvester equation, whose coefficient matrices are restrictions of the original coefficient matrices to the projection subspaces, are solved by a direct method. In this paper, after recalling some important properties about the extended block Arnoldi process, we propose an alternative approach that enables us to determine approximate solutions by solving projected and reduced Sylvester equations whose coefficient matrices are restrictions of the inverse of the original matrices. Some numerical experiments are given in order to illustrate the performance of our approach. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new pansharpening method called detail-based deep Laplacian pansharpening (DDLPS) to improve the spatial resolution of hyperspectral imagery. This method includes three main components: upsampling, detail injection, and optimization. In particular, a deep Laplacian pyramid super-resolution network (LapSRN) improves the resolution of each band. Then, a guided image filter and a gain matrix are used to combine the spatial and spectral details with an optimization problem, which is formed to adaptively select an injection coefficient. The DDLPS method is compared with 11 state-of-the-art or traditional pansharpening approaches. The experimental results demonstrate the superiority of the DDLPS method in terms of both quantitative indices and visual appearance. In addition, the training of LapSRN is based on the data sets of traditional RGB images, which overcomes the practical difficulty of insufficient training samples for pansharpening. [ABSTRACT FROM AUTHOR]

Let A = {Aij}i,j∈I, where I is an index set, be a doubly indexed family of matrices, where Aij is ni × nj. For each i∈I, let Vi be an ni-dimensional vector space. We say A is reducible in the coupled sense if there exist subspaces, Ui ⊆ Vi, with Ui ≠{0} for at least one i∈I, and Ui ≠ Vi for at least one i, such that Aij(Uj) ⊆ Ui for all i, j. Let B = {Bij}i,j∈I also be a doubly indexed family of matrices, where Bij is mi times mj. For each i∈I, let Xi be a matrix of size ni times mi. Suppose Aij} Xj = Xi Bij for all i, j. We prove versions of Schur's lemma for A, mathcal B satisfying coupled irreducibility conditions. We also consider a refinement of Schur's lemma for sets of normal matrices and prove corresponding versions for A, mathcal B satisfying coupled normality and coupled irreducibility conditions. [ABSTRACT FROM AUTHOR]

We consider the discretization of time-space diffusion equations with fractional derivatives in space and either one-dimensional (1D) or 2D spatial domains. The use of an implicit Euler scheme in time and finite differences or finite elements in space leads to a sequence of dense large scale linear systems describing the behavior of the solution over a time interval. We prove that the coefficient matrices arising in the 1D context are rank structured and can be efficiently represented using hierarchical formats (H-matrices, HODLR). Quantitative estimates for the rank of the off-diagonal blocks of these matrices are presented. We analyze the use of HODLR arithmetic for solving the 1D case and we compare this strategy with existing methods that exploit the Toeplitz-like structure to precondition the GMRES iteration. The numerical tests demonstrate the convenience of the HODLR format when at least a reasonably low number of time steps is needed. Finally, we explain how these properties can be leveraged to design fast solvers for problems with 2D spatial domains that can be reformulated as matrix equations. The experiments show that the approach based on the use of rank-structured arithmetic is particularly effective and outperforms current state of the art techniques. [ABSTRACT FROM AUTHOR]

Perturbation analysis has been primarily considered to be one of the main issues in many fields. The Schur decomposition can factor a square matrix as the product of a unitary matrix and an upper triangular matrix, which contains eigenvalues of the square matrix. In view of the importance of T-eigenvalue problems, this paper discusses a tensor Schur decomposition (T-Schur), the T-Schur decomposition = U ∗ T ∗ U ∗ , which is based on T-product multiplication of third-order tensors. We present the normwise and componentwise perturbation analysis for the unitary tensor U , the upper triangular tensor T and T-eigenvalues of the tensor A. We explore some applications of the T-Schur decomposition and perform the T-Schur form to solve the tensor pole assignment. We give an algorithm to solve the tensor based T-Sylvester equation and present its perturbation bound and the backward error. The T-Sylvester equation can also be used to estimate the condition of the T-sign function. We apply the T-Schur decomposition to facial recognation and compare it with other types of tensor decompositions. [ABSTRACT FROM AUTHOR]

Abstract In this study, based on the MHSS (Modified Hermitian and skew-Hermitian splitting) method, we will present a generalized MHSS approach for solving large sparse Sylvester equation with non-Hermitian and complex symmetric positive definite/semi-definite matrices. The new method (GMHSS) is a four-parameter iteration procedure where the iterative sequence is unconditionally convergent to the unique solution of the Sylvester equation. Then to improve the GMHSS method, the inexact version of the GMHSS iterative method (IGMHSS) will be described and will be analyzed. Also, by using a new idea, we try to minimize the upper bound of the spectral radius of iteration matrix. Two test problems are given to illustrate the efficiency of the new approach. [ABSTRACT FROM AUTHOR]

In this paper, the output regulation problem is addressed for a class of linear hyperbolic infinite-dimensional systems with spatially varying coefficients modelling a large class of convection-dominated transport reaction systems. In particular, distributed parameter systems with bounded input and unbounded output operators are considered. First, we demonstrate a general conclusion about the exponential stability of the considered system by relating the stability to the solution of an associated differential equation. Based on the assumption that the hyperbolic system satisfies the exponential stability conditions, the main manuscript contribution is the development of two novel finite-dimensional regulators, output and error feedback regulators, such that the controlled output of the plant tracks a reference signal generated by a known signal process (exosystem). In order to guarantee the feasibility of the proposed regulators, the solvability of the corresponding Sylvester equations is discussed and the solvability conditions are provided. Finally, simulations of output regulation of an axial dispersion reactor and a relevant numerical example illustrate the main results and performance of the proposed regulators synthesis. [ABSTRACT FROM AUTHOR]

Linear matrix equations, such as the Sylvester and Lyapunov equations, play an important role in various applications, including the stability analysis and dimensionality reduction of linear dynamical control systems and the solution of partial differential equations. In this work, we present and analyze a new algorithm, based on tensorized Krylov subspaces, for quickly updating the solution of such a matrix equation when its coeffcients undergo low-rank changes. We demonstrate how our algorithm can be utilized to accelerate the Newton method for solving continuous-time algebraic Riccati equations. Our algorithm also forms the basis of a new divide-and-conquer approach for linear matrix equations with coeffcients that feature hierarchical low-rank structure, such as hierarchically off-diagonal low-rank structures, hierarchically semiseparable, and banded matrices. Numerical experiments demonstrate the advantages of divide-and-conquer over existing approaches, in terms of computational time and memory consumption. [ABSTRACT FROM AUTHOR]

We consider the problem of efficiently solving Sylvester and Lyapunov equations of medium and large scale, in case of rank-structured data, i.e., when the coefficient matrices and the right-hand side have low-rank off-diagonal blocks. This comprises problems with banded data, recently studied in [A. Haber and M. Verhaegen, Automática J. IFAC, 73 (2016), pp. 256--268; D. Palitta and V. Simoncini, Numerical Methods for Large-Scale Lyapunov Equations with Symmetric Banded Data, preprint, arxiv, 1711.04187, 2017], which often arise in the discretization of elliptic PDEs. We show that, under suitable assumptions, the quasiseparable structure is guaranteed to be numerically present in the solution, and explicit novel estimates of the numerical rank of the off-diagonal blocks are provided. Efficient solution schemes that rely on the technology of hierarchical matrices are described, and several numerical experiments confirm the applicability and efficiency of the approaches. We develop a MATLAB toolbox that allows easy replication of the experiments and a ready-to-use interface for the solvers. The performances of the different approaches are compared, and we show that the new methods described are efficient on several classes of relevant problems. [ABSTRACT FROM AUTHOR]

In this paper, a class of variable-order fractional convection diffusion equations have been solved with assistance of the second kind Chebyshev wavelets operational matrix. The operational matrix of variable-order fractional derivative is derived for the second kind Chebyshev wavelets. By implementing the second kind Chebyshev wavelets functions and also the associated operational matrix, the considered equations will be reduced to the corresponding Sylvester equation, which can be solved by some appropriate iterative solvers. Also, the convergence analysis of the proposed numerical method to the exact solutions and error estimation are given. A variety of numerical examples are considered to show the efficiency and accuracy of the presented technique. [ABSTRACT FROM AUTHOR]

We investigate the time domain model order reduction (MOR) framework using general orthogonal polynomials by Jiang and Chen [1] and extend their idea by exploiting the structure of the corresponding linear system of equations. Identifying an equivalent Sylvester equation, we show a connection to a rational Krylov subspace, and thus to moment matching. This theoretical link between the MOR techniques is illustrated by three numerical examples. For linear time-invariant systems, the link also motivates that the time-domain approach can be at best as accurate as moment matching, since the expansion points are fixed by the choice of the polynomial basis, while in moment matching they can be adapted to the system. [ABSTRACT FROM AUTHOR]

This short note provides an explicit description of the Fréchet derivatives of the principal square root matrix function at any order. We present an original formulation that allows to compute sequentially the Fréchet derivatives of the matrix square root at any order starting from the first order derivative. A Taylor expansion at any order with an integral remainder term is also provided, yielding the first result of this type for this class of matrix function. [ABSTRACT FROM AUTHOR]

Dual number matrix decompositions have played an important role in fields such as kinematics and computer graphics in recent years. In this paper, we present a QR decomposition algorithm for dual number matrices. When dealing with large-scale problems, we present the thin QR decomposition of dual number matrices, along with its algorithm with column pivoting. In numerical experiments, we discuss the suitability of different QR algorithms when confronted with various large-scale dual matrices, providing their respective domains of applicability. Finally, we employ the QR decomposition of dual matrices to compute the DMPGI, attaining results of higher precision. [ABSTRACT FROM AUTHOR]

We provide an amended version of Corollaries 7 and 9 in [De Terán, Iannazzo, Poloni, Robol, “Solvability and uniqueness criteria for generalized Sylvester-type equations”]. These results characterize the unique solvability of the matrix equation A X B + C X ⋆ D = E (where the coefficients need not be square) in terms of an equivalent condition on the spectrum of certain matrix pencils of the same size as one of its coefficients. [ABSTRACT FROM AUTHOR]

In the numerical treatment of large-scale Sylvester and Lyapunov equations, projection methods require solving a reduced problem to check convergence. As the approximation space expands, this solution takes an increasing portion of the overall computational effort. When data are symmetric, we show that the Frobenius norm of the residual matrix can be computed at significantly lower cost than with available methods, without explicitly solving the reduced problem. For certain classes of problems, the new residual norm expression combined with a memory-reducing device make classical Krylov strategies competitive with respect to more recent projection methods. Numerical experiments illustrate the effectiveness of the new implementation for standard and extended Krylov subspace methods. [ABSTRACT FROM AUTHOR]

The block inverse iteration and block Newton’s methods proposed by the authors of this paper for computing invariant pairs of regular linear matrix pencils are generalized to the case of regular nonlinear matrix pencils. Numerical properties of the proposed methods are demonstrated with a typical quadratic eigenproblem. [ABSTRACT FROM AUTHOR]

This paper contributes a numerical method for solving a class of fractional convection diffusion equations with time-space variable coefficients. By implementing Legendre polynomials and also the associated operational matrix, the considered equations will be reduced to the corresponding systems of algebraic equations, which can be solved by computer programming. Also, the error analysis of the suggested method to the exact solutions is provided. Finally, numerical examples are provided to show the efficiency of the presented method. [ABSTRACT FROM AUTHOR]

We consider the numerical solution of a c-stable linear equation in the tensor product space [ABSTRACT FROM AUTHOR]

In this paper, a numerical method based on Legendre polynomials is proposed for solving the variable order time fractional diffusion equation. We adopt the Coimbra variable order time fractional operator, which can be viewed as a Caputo-type definition. Operational matrix of differentiation is also introduced. Combining this matrix with the properties of Legendre polynomials, we transform the initial problem into a Sylvester equation. Numerical example is provided to demonstrate the validity and applicability of the technique. Moreover, comparing the methodology with the known method shows that our approach is more efficient and more convenient. [ABSTRACT FROM AUTHOR]

We propose a preconditioned squared Smith algorithm to solve large scale continuous-time Sylvester matrix equations. We firstly construct a preconditioner by using the alternating directional implicit (ADI) iteration, and transform the original equation to an equivalent non-symmetric Stein matrix equation. Then we apply the squared Smith algorithm to generate the low-rank approximation form with a Krylov subspace. Numerical experiments show that the algorithm has better iteration efficiency and convergence accuracy in comparison with the Jacobi iteration method. [ABSTRACT FROM AUTHOR]

We consider the numerical solution of the projected nonsymmetric algebraic Riccati equations or their associated Sylvester equations via Newton’s method, arising in the refinement of estimates of invariant (or deflating subspaces) for a large and sparse real matrix A (or pencil A − λ B ). The engine of the method is the inversion of the matrix P 2 P 2 ⊤ A − γ I n or P l 2 P l 2 ⊤ ( A − γ B ) , for some orthonormal P 2 or P l 2 from R n × ( n − m ) , making use of the structures in A or A − λ B and the Sherman–Morrison–Woodbury formula. Our algorithms are efficient, under appropriate assumptions, as shown in our error analysis and illustrated by numerical examples. [ABSTRACT FROM AUTHOR]

In this paper, we generalize an existing technique which is relevant to a pole placement problem and state feedback control design with constraints on control inputs. The existing technique concerns only a restrictive class of systems for which many assumptions should be satisfied. The generalization aims then at avoiding those assumptions. The advantages of generalizing this technique are to determine linear feedback controllers that asymptotically stabilize a wide class of systems which are subject to input constraints without violating these constraints, to derive a simple formula for the state feedback and to assign the whole spectrum when stable poles are not suitably located for performances. Moreover, there will be no need to assume a lack of overlap between open and closed-loop spectra. Illustrative examples are presented to point out these advantages. [ABSTRACT FROM AUTHOR]