An iterative algorithm for a Cauchy problem in eddy-current modelling

We consider a linear steady-state eddy-current problem for a magnetic field in a bounded domain. The boundary consists of two parts: reachable with prescribed Cauchy data and unreachable with no data on it. We design an iterative (Landweber type) algorithm for solution of this problem. At each iteration step two auxiliary mixed well-posed boundary value problems are solved. The analysis of temporary problems is performed in suitable function spaces. This creates the basis for the convergence argument. The theoretical results are supported with numerical experiments.     

Gradient based and least squares based iterative algorithms for matrix equations AXB + CXD = F

This paper develops a gradient based and a least squares based iterative algorithms for solving matrix equation AXB + CX^TD = F. The basic idea is to decompose the matrix equation (system) under consideration into two subsystems by applying the hierarchical identification principle and to derive the iterative algorithms by extending the iterative methods for solving Ax = b and AXB = F. The analysis shows that when the matrix equation has a unique solution (under the sense of least squares), the iterative solution converges to the exact solution for any initial values. A numerical example verifies the proposed theorems.     

An iterative method for equilibrium problems and a finite family of relatively nonexpansive mappings in a Banach space

In this paper, we introduce a new iterative method for finding a common element of the set of fixed points of a finite family of relatively nonexpansive mappings and the set of solutions of an equilibrium problem in uniformly convex and uniformly smooth Banach spaces. Then we prove a strong convergence theorem by using the generalized projection.     

On the Consistency of Prony's Method and Related Algorithms

Abstract

Modifications of Prony's classical technique for estimating rate constants in exponential fitting problems have many contemporary applications. In this article the consistency of Prony's method and of related algorithms based on maximum likelihood is discussed as the number of observations n → ∞ by considering the simplest possible models for fitting sums of exponentials to observed data. Two sampling regimes are relevant, corresponding to transient problems and problems of frequency estimation, each of which is associated with rather different kinds of behavior. The general pattern is that the stronger results are obtained for the frequency estimation problem. However, the algorithms considered are all scaling dependent and consistency is not automatic. A new feature that emerges is the importance of an appropriate choice of scale in order to ensure consistency of the estimates in certain cases. The tentative conclusion is that algorithms referred to as Objective function Reweighting Algorithms (ORA's) are superior to their exact maximum likelihood counterparts, referred to as Gradient condition Reweighting Algorithms (GRA's), especially in the frequency estimation problem. This conclusion does not extend to fitting other families of functions such as rational functions.     

An Extension of Least Angle Regression Based on the Information Geometry of Dually Flat Spaces

We extend the least angle regression algorithm using the information geometry of dually flat spaces. The extended least angle regression algorithm is used for estimating parameters in generalized linear regression, and it can be also used for selecting explanatory variables. We use the fact that a model manifold of an exponential family is a dually flat space. In estimating parameters, curves corresponding to bisectors in the Euclidean space play an important role. Originally, the least angle regression algorithm is used for estimating parameters and selecting explanatory variables in linear regression. It is an efficient algorithm in the sense that the number of iterations is the same as the number of explanatory variables. We extend the algorithm while keeping this efficiency. However, the extended least angle regression algorithm differs significantly from the original algorithm. The extended least angle regression algorithm reduces one explanatory variable in each iteration while the original algorithm increases one explanatory variable in each iteration. We show results of the extended least angle regression algorithm for two types of datasets. The behavior of the extended least angle regression algorithm is shown. Especially, estimates of parameters become smaller and smaller, and vanish in turn.     

用正交投影迭代法求矩阵方程AXA~H=B的Hermite解

本文讨论了矩阵方程AXAH=B的Hermite解及其最佳逼近的正交投影迭代法,证明了算法的收敛性,得到收敛速率的估计式.通过数值试验也检验了算法的有效性.     

Accuracy and complexity evaluation of defuzzification strategies for the discretised interval type-2 fuzzy set

The work reported in this paper addresses the challenge of the efficient and accurate defuzzification of discretised interval type-2 fuzzy sets. The exhaustive method of defuzzification for type-2 fuzzy sets is extremely slow, owing to its enormous computational complexity. Several approximate methods have been devised in response to this bottleneck. In this paper we survey four alternative strategies for defuzzifying an interval type-2 fuzzy set: (1) The Karnik–Mendel Iterative Procedure, (2) the Wu–Mendel Approximation, (3) the Greenfield–Chiclana Collapsing Defuzzifier, and (4) the Nie–Tan Method.We evaluated the different methods experimentally for accuracy, by means of a comparative study using six representative test sets with varied characteristics, using the exhaustive method as the standard. A preliminary ranking of the methods was achieved using a multi-criteria decision making methodology based on the assignment of weights according to performance. The ranking produced, in order of decreasing accuracy, is (1) the Collapsing Defuzzifier, (2) the Nie–Tan Method, (3) the Karnik–Mendel Iterative Procedure, and (4) the Wu–Mendel Approximation.Following that, a more rigorous analysis was undertaken by means of the Wilcoxon Nonparametric Test, in order to validate the preliminary test conclusions. It was found that there was no evidence of a significant difference between the accuracy of the Collapsing and Nie–Tan Methods, and between that of the Karnik–Mendel Iterative Procedure and the Wu–Mendel Approximation. However, there was evidence to suggest that the collapsing and Nie–Tan Methods are more accurate than the Karnik–Mendel Iterative Procedure and the Wu–Mendel Approximation.In relation to efficiency, each method’s computational complexity was analysed, resulting in a ranking (from least computationally complex to most computationally complex) as follows: (1) the Nie–Tan Method, (2) the Karnik–Mendel Iterative Procedure (lowest complexity possible), (3) the Greenfield–Chiclana Collapsing Defuzzifier, (4) the Karnik–Mendel Iterative Procedure (highest complexity possible), and (5) the Wu–Mendel Approximation.     

Interference suppression for the LTE uplink

Interference cancellation is expected to have significant importance for next-generation wireless communication systems due to various co-channel deployment scenarios and denser frequency reuse. In this study, an interference cancellation receiver that exploits the unique characteristics of single-carrier frequency-division multiple access based systems is proposed. The proposed receiver suppresses the co-channel dominant interference by blanking frequency-domain samples where the desired and interfering signals overlap. In order to improve the performance, demodulation and regeneration stages can be introduced and repeated multiple times. Further enhancement is possible by initially accommodating a group of reliable symbols before the iterations. The simulation results indicate that proposed methods work particularly well for low overlap ratios compared to interference coordination and no cancellation schemes.     

Dissipativity and global attractors for a class of quasilinear parabolic systems

We show that a-priori weak L^p dissipativity implies strong L ^∞ dissipativity for a class of weakly coupled quasilinear parabolic systems satisfies general structure conditions. The existence of global attractors of general nonlinear reaction diffusion systems will be proven.     

一类四阶微积分方程的Legendre-Galerkin谱逼近

针对研究吊桥模型而建立的四阶微积分方程, 提出Legendre谱逼近法进行求解.构造迭代算法来求解得到的线性系统, 证明了迭代格式的收敛性, 对问题进行了误差分析.数值算例验证了迭代的收敛性和方法的高精度.