Model order determination using the Hankel matrix of impulse responses

This letter studies identification problems of model orders using the Hankel matrix of impulse responses of a system and presents two order identification methods: one is based on the singularities or ratios of the Hankel matrix determinants and the other is based on the singular value decomposition of the Hankel matrix. A numerical example verifies the proposed methods.     

Fast algorithms for designing variable FIR notch filters

The paper presents fast algorithms for designing finite impulse response (FIR) notch filters. The aim is to design a digital FIR notch filter so that the magnitude of the filter has a deep notch at a specified frequency, and as the notch frequency changes, the filter coefficients should be able to track the notch fast in real time. The filter design problem is first converted into a convex optimization problem in the autocorrelation domain. The frequency response of the autocorrelation of the filter impulse response is compared with the desired filter response and the integral square error is minimized with respect to the unknown autocorrelation coefficients. Spectral factorization is used to calculate the coefficients of the filter. In the optimization process, the computational advantage is obtained by exploiting the structure of the Hessian matrix which consists of a Toeplitz plus a Hankel matrix. Two methods have been used for solving the Toeplitz‐plus‐Hankel system of equations. In the first method, the computational time is reduced by using Block–Levinson's recursion for solving the Toeplitz‐plus‐Hankel system of matrices. In the second method, the conjugate gradient method with different preconditioners is used to solve the system. Comparative studies demonstrate the computational advantages of the latter. Both these algorithms have been used to obtain the autocorrelation coefficients of notch filters with different orders. The original filter coefficients are found by spectral factorization and each of these filters have been tested for filtering synthetic as well as real‐life signals. Copyright © 2007 John Wiley & Sons, Ltd.     

On the identifiability of measurement error in the bifurcating autoregressive model

Huggins and Staudte (1994) considered a mixed linear model for the analysis of cell lineage data and in models for the covariance structure which involved measurement error, it was not immediately clear that the parameters involved were identifiable. Whilst a numerical examination of the Hessian matrix at the estimated parameter values gave some reassurance, this was not theoretically satisfying. Here a matrix formulation of the robust estimating functions of Huggins (1993a, b) as applied in Huggins and Staudte (1994), which include the maximum likelihood estimating functions under the assumption of multivariate normality as a special case, is given along with a direct proof linking identifiability expressed in terms of the estimating functions with the information matrix or its analogue in more general settings. The resulting conditions on the estimating functions may then be checked globally using computer algebra, suggesting a method for establishing identifiability in mixed linear models in general.     

On identifiability for multidimensional armax model

This paper gives a definition of identifiability for multidimensional linear input-output systems and presents a necessary and sufficient condition for its satisfaction. For a class of identifiable systems it is also shown that the unknown coefficients of the system can consistently be estimated by a recursive algorithm.This project is supported by the National Natural Science Foundation of China and the TWAS RG MP 898-117.     

Factorization of moving-average spectral densities by state-space representations and stacking

To factorize a spectral density matrix of a vector moving average process, we propose a state space representation. Although this state space is not necessarily of minimal dimension, its associated system matrices are simple and most matrix multiplications involved are nothing but index shifting. This greatly reduces the complexity of computation. Moreover, in this article we stack every q consecutive observations of the original process MA(q) and generate a vector MA(1) process. We consider a similar state space representation for the stacked process. Consequently, the solution hinges on a surprisingly compact discrete algebraic Riccati equation (DARE), which involves only one Toeplitz and one Hankel block matrix composed of autocovariance functions. One solution to this equation is given by the so-called iterative projection algorithm. Each iteration of the stacked version is equivalent to q iterations of the unstacked one. We show that the convergence behavior of the iterative projection algorithm is characterized by the decreasing rate of the partial correlation coefficients for the stacked process. In fact, the calculation of the partial correlation coefficients via the Whittle algorithm, which takes a very simple form in this case, offers another solution to the problem. To achieve computational efficiency, we apply the general Newton procedure given by Lancaster and Rodman to the DARE and obtain an algorithm of quadratic convergence rate. One immediate application of the new algorithms is polynomial stabilization. We also discuss various issues such as check of positivity and numerical implementation.     

The stable computation of formal orthogonal polynomials

For many applications — such as the look-ahead variants of the Lanczos algorithm — a sequence of formal (block-)orthogonal polynomials is required. Usually, one generates such a sequence by taking suitable polynomial combinations of a pair of basis polynomials. These basis polynomials are determined by a look-ahead generalization of the classical three term recurrence, where the polynomial coefficients are obtained by solving a small system of linear equations. In finite precision arithmetic, the numerical orthogonality of the polynomials depends on a good choice of the size of the small systems; this size is usually controlled by a heuristic argument such as the condition number of the small matrix of coefficients. However, quite often it happens that orthogonality gets lost.We present a new variant of the Cabay-Meleshko algorithm for numerically computing pairs of basis polynomials, where the numerical orthogonality is explicitly monitored with the help of stability parameters. A corresponding error analysis is given. Our stability parameter is shown to reflect the condition number of the underlying Hankel matrix of moments. This enables us to prove the weak and strong stability of our method, provided that the corresponding Hankel matrix is well-conditioned.This work was partially supported by the HCM project ROLLS, under contract CHRX-CT93-0416.     

Integrable operators and the squares of Hankel operators

Integrable operators arise in random matrix theory, where they describe the asymptotic eigenvalue distribution of large self-adjoint random matrices from the generalized unitary ensembles. This paper gives sufficient conditions for an integrable operator to be the square of a Hankel operator, and applies the condition to the Airy, associated Laguerre, modified Bessel and Whittaker functions.     

Carathéodory-Toeplitz and Nehari problems for matrix valued almost periodic functions

In this paper the positive and strictly contractive extension problems for almost periodic matrix functions are treated. We present necessary and sufficient conditions for the existence of extensions in terms of Toeplitz and Hankel operators on Besicovitch spaces and Lebesgue spaces. Furthermore, when a solution exists a special extension (the band extension) is constructed which enjoys a maximum entropy property. A linear fractional parameterization of the set of all extensions is also provided. The techniques used in the proofs include factorizations of matrix valued almost periodic functions and a general algebraic scheme called the band method.

     

Numerical Solution of Systems of Loaded Ordinary Differential Equations with Multipoint Conditions

A system of loaded ordinary differential equations with multipoint conditions is considered. The problem under study is reduced to an equivalent boundary value problem for a system of ordinary differential equations with parameters. A system of linear algebraic equations for the parameters is constructed using the matrices of the loaded terms and the multipoint condition. The conditions for the unique solvability and well-posedness of the original problem are established in terms of the matrix made up of the coefficients of the system of linear algebraic equations. The coefficients and the righthand side of the constructed system are determined by solving Cauchy problems for linear ordinary differential equations. The solutions of the system are found in terms of the values of the desired function at the initial points of subintervals. The parametrization method is numerically implemented using the fourth-order accurate Runge–Kutta method as applied to the Cauchy problems for ordinary differential equations. The performance of the constructed numerical algorithms is illustrated by examples.     

Exact Fourier expansion in cylindrical coordinates for the three-dimensional Helmholtz Green function

A new method is presented for Fourier decomposition of the Helmholtz Green function in cylindrical coordinates, which is equivalent to obtaining the solution of the Helmholtz equation for a general ring source. The Fourier coefficients of the Green function are split into their half advanced + half retarded and half advanced–half retarded components, and closed form solutions for these components are then obtained in terms of a Horn function and a Kampé de Fériet function respectively. Series solutions for the Fourier coefficients are given in terms of associated Legendre functions, Bessel and Hankel functions and a hypergeometric function. These series are derived either from the closed form 2-dimensional hypergeometric solutions or from an integral representation, or from both. A simple closed form far-field solution for the general Fourier coefficient is derived from the Hankel series. Numerical calculations comparing different methods of calculating the Fourier coefficients are presented. Fourth order ordinary differential equations for the Fourier coefficients are also given and discussed briefly.     

An exponential matrix method for solving systems of linear differential equations

This paper presents an exponential matrix method for the solutions of systems of high‐order linear differential equations with variable coefficients. The problem is considered with the mixed conditions. On the basis of the method, the matrix forms of exponential functions and their derivatives are constructed, and then by substituting the collocation points into the matrix forms, the fundamental matrix equation is formed. This matrix equation corresponds to a system of linear algebraic equations. By solving this system, the unknown coefficients are determined and thus the approximate solutions are obtained. Also, an error estimation based on the residual functions is presented for the method. The approximate solutions are improved by using this error estimation. To demonstrate the efficiency of the method, some numerical examples are given and the comparisons are made with the results of other methods. Copyright © 2012 John Wiley & Sons, Ltd.     

The local parametric identifiability of a linear stationary system from a single observation of its solution

A special case of a system of linear stationary differential equations depending on a parameter is considered. Necessary and sufficient conditions for the local parametric identifiability of such a system from a single observation of its solution are obtained. Relations between the conditions obtained and the behavior of the spectrum of the matrix of the system are analyzed.     

Exact Fourier expansion in cylindrical coordinates for the three-dimensional Helmholtz Green function

A new method is presented for Fourier decomposition of the Helmholtz Green function in cylindrical coordinates, which is equivalent to obtaining the solution of the Helmholtz equation for a general ring source. The Fourier coefficients of the Green function are split into their half advanced + half retarded and half advanced–half retarded components, and closed form solutions for these components are then obtained in terms of a Horn function and a Kampé de Fériet function respectively. Series solutions for the Fourier coefficients are given in terms of associated Legendre functions, Bessel and Hankel functions and a hypergeometric function. These series are derived either from the closed form 2-dimensional hypergeometric solutions or from an integral representation, or from both. A simple closed form far-field solution for the general Fourier coefficient is derived from the Hankel series. Numerical calculations comparing different methods of calculating the Fourier coefficients are presented. Fourth order ordinary differential equations for the Fourier coefficients are also given and discussed briefly.     

Structured mixed and componentwise condition numbers of some structured matrices

Structured matrices, such as Cauchy, Vandermonde, Toeplitz, Hankel, and circulant matrices, are considered in this paper. We apply a Kronecker product-based technique to deduce the structured mixed and componentwise condition numbers for the matrix inversion and for the corresponding linear systems.     

Extending the notions of companion and infinite companion to matrix polynomials

The notion of infinite companion matrix is extended to the case of matrix polynomials (including polynomials with singular leading coefficient). For row reduced polynomials a finite companion is introduced as the compression of the shift matrix. The methods are based on ideas of dilation theory. Connections with systems theory are indicated. Applications to the problem of linearization of matrix polynomials, solution of systems of difference and differential equations and new factorization formulae for infinite block Hankel matrices having finite rank are shown. As a consequence, any system of linear difference or differential equations with constant coefficients can be transformed into a first order system of dimension n = deg det D.     

具有特殊协方差结构的 SURE 模型中参数估计的若干结果

本文讨论具有特殊协方差结构似乎不相关回归方程（SURE）模型中参数的估计问题．除非另有说明,损失函数将取为二次损失和矩阵损失．本文证明了回归系数的线性可估函数的最小二乘估计是极小极大的且在矩阵损失函数下是可容许的;还分别在仿射交换群和平移群下导出了存在回归系数的线性可估函数的一致最小风险同变（UMRE）估计的充要条件,并证明了在仿射交换和二次损失下不存在协方差阵和方差的UMRE估计．     

A method of explicit factorization of matrix functions and applications

A method of explicit factorization of matrix functions of second order is proposed. The method consists of reduction of this problem to two scalar barrier problems and a finite system of linear equations. Applications to various classes of singular integral equations and equations with Toeplitz and Hankel matrices are given.     

Randomized preprocessing versus pivoting

It is known that without pivoting Gaussian elimination can run significantly faster (particularly for matrices that have structures of Toeplitz or Hankel types), but becomes numerically unsafe. The known remedies take their toll, e.g., symmetrization squares the condition number of the input matrix. Can we fix the problem without such a punishment? Taking this challenge we combine randomized preconditioning techniques with iterative refinement and prove that this combination is expected to make pivoting-free Gaussian elimination numerically safe while keeping it fast. For matrices having structures of Toeplitz or Hankel types transition to Gaussian elimination with no pivoting decreases arithmetic time bound from cubic to nearly linear, and our tests show dramatic decrease of the CPU time as well.     

Impulsively-controlled systems and reverse dwell time: A linear programming approach

We present a receding horizon algorithm that converges to the exact solution in polynomial time for a class of optimal impulse control problems with uniformly distributed impulse instants and governed by so-called reverse dwell time conditions. The cost has two separate terms, one depending on time and the second monotonically decreasing on the state norm. The obtained results have both theoretical and practical relevance. From a theoretical perspective we prove certain geometrical properties of the discrete set of feasible solutions. From a practical standpoint, such properties reduce the computational burden and speed up the search for the optimum thus making the algorithm suitable for the on-line implementation in real-time problems. Our approach consists in approximating the optimal impulse control problem via a binary linear programming problem with a totally unimodular constraint matrix. Hence, solving the binary linear programming problem is equivalent to solving its linear relaxation. Then, given the feasible solution from the linear relaxation, we find the optimal solution via receding horizon and local search. Numerical illustrations of a queueing system are performed.     

Solving knapsack sharing problems with general tradeoff functions

A knapsack sharing problem is a maximin or minimax mathematical programming problem with one or more knapsack constraints (an inequality constraint with all non-negative coefficients). All knapsack sharing algorithms to date have assumed that the objective function is composed of single variable functions called tradeoff functions which are strictly increasing continuous functions. This paper develops optimality conditions and algorithms for knapsack sharing problems with any type of continuous tradeoff function including multiple-valued and staircase functions which can be increasing, decreasing, unimodal, bimodal, or even multi-modal. To do this, optimality conditions are developed for a special type of multiple-valued, nondecreasing tradeoff function called an ascending function. The optimal solution to any knapsack sharing problem can then be found by solving an equivalent problem where all the tradeoff functions have been transformed to ascending functions. Polynomial algorithms are developed for piecewise linear tradeoff functions with a fixed number of breakpoints. The techniques needed to construct efficient algorithms for any type of tradeoff function are discussed.