Scalarization for characterization of approximate strong/weak/proper efficiency in multi-objective optimization

In this article, approximate solutions of multi-objective optimization problems are analysed. The notion of approximate solution suggested by Kutateladze is dealt with, and, utilizing different scalarization approaches, some necessary and sufficient conditions for ?-(strong, weak, proper) efficiency are provided. Almost all of the provided results are established without any convexity assumption.     

对称广义中心对称半正定矩阵模型修正的矩阵逼近法及其应用

设X,B分别是实测的位移矩阵和载荷矩阵, C是有限元模型估计,找对称广义中心对称半正定矩阵(A)使‖C-(A)‖F=min(A:AX=B)‖C-A‖F.我们证明这样的(A)存在唯一,并应用它来修正动力模型.数值结果证明方法是行之有效的.     

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.     

Spectral approximation of Wiener-Hopf operators with almost periodic generating function

The paper deals with spectral approximation of Wiener-Hopf operators acting on L^p -spaces by their

finite sections. The generating functions of the Wiener-Hopf operators are supposed to be continuous plus almost

periodic.While the usual spectra of the finite sections drastically fail to converge to the spectrum of the Wiener-Hopf

operator,it turns out that other spectral approximants, viz. the pseudospectra and the numerical ranges, do converge

perfectly.The proof requires a modified approach to the finite section method for Wiener-Hopf operators. This note

generalizes results obtained by Böttcher, Grudsky and Silbermann for the case of continuous generating

functions.     

Toward Best Approximation of Nonlinear Systems: A Case of Models with Memory

The problem of nonlinear dynamical system modeling, considered in this paper, is motivated by restrictions arising in real-world tasks. The restrictions are that first, a system input cannot be entirely observed for one trial. Second, the system model must be subjected to the causality principle. Third, the input is corrupted by noise so that no relationship between the reference input and noise is known. Fourth, the model should have some degrees of freedom so that the associated accuracy can be regulated by a variation of these freedom degrees. We propose and justify new procedures for the nonlinear system modeling that are initialized by these motivations. The models are nonlinear and given by so called r-degree operators that can be reduced to a matrix form presentation. To satisfy the restrictions above, the matrices have special structures that we call the lower p-band matrices. The degree r of the models is the required degree of freedom. The rigorous analysis of errors associated with the presented techniques is given. Numerical experiments with real data demonstrate the efficiency of the proposed approach.     

Approximation Methods for Inverse Problems Governed by Nonlinear Parabolic Systems

We present a rigorous theoretical framework for approximation of nonlinear parabolic systems with delays in the context of inverse least squares problems. Convergence of approximate optimal parameters and that of forward solutions in the context of semidiscrete Galerkin schemes are given. Sample numerical results demonstrating the convergence are given for a model of dioxin uptake and elimination in a distributed liver model that is a special case of the general theoretical framework.     

Regularized Versions of Continuous Newton's Method and Continuous Modified Newton's Method Under General Source Conditions

Regularized versions of continuous analogues of Newton's method and modified Newton's method for obtaining approximate solutions to a nonlinear ill-posed operator equation of the form F(u) = f, where F is a monotone operator defined from a Hilbert space H into itself, have been studied in the literature. For such methods, error estimates are available only under Hölder-type source conditions on the solution. In this paper, presenting the background materials systematically, we derive error estimates under a general source condition. For the special case of the regularized modified Newton's method under a Hölder-type source condition, we also carry out error analysis by replacing the monotonicity of F by a weaker assumption. This analysis facilitates inclusion of certain examples of parameter identification problems, which was not possible otherwise. Moreover, an a priori stopping rule is considered when we have a noisy data f _δ instead of f. This rule yields not only convergence of the regularized approximations to the exact solution as the noise level δ tends to zero but also provides convergence rates that are optimal under the source conditions considered.     

Optimal Subspaces for n -Widths of Bounded Subsets in Hilbert Spaces

In this article, we consider the problem of proving the optimality of several approximation spaces by means of n-widths. Specifically, they are optimal subspaces for approximating bounded subsets in some Hilbert spaces with mesh-dependent norms. We prove that finite element spaces and newly developed generalized L-spline spaces are optimal subspaces for n-widths.     

A Note on Existence and Convergence of Best Proximity Points for Pointwise Cyclic Contractions

We introduce a notion of pointwise cyclic contraction T satisfying TA ? B and TB ? A to obtain the existence of a point x ∈ A, such that d(x, Tx) = dist(A, B), known as a best proximity point for such a map. We also prove that for any x ∈ A, the Picard iteration {T²ⁿx} converges to a best proximity point.