WEIGHTED INEQUALITIES AND APPLICATIONS TO BEST LOCAL APPROXIMATION IN LUXEMBURG NORM

In this paper we establish a result about uniformly equivalent norms and the convergence of best approximant pairs on the unitary ball for a family of weighted Luxemburg norms with normalized weight functions depending on ε, when ε→ 0. It is introduced a general concept of Pade approximant and we study its relation with the best local quasi-rational approximant. We characterize the limit of the error for polynomial approximation. We also obtain a new condition over a weight function in order to obtain inequalities in Lp norm, which play an important role in problems of weighted best local Lp approximation in several variables.

Truncated sparse approximation property and truncated q-norm minimization

This paper considers approximately sparse signal and low-rank matrix’s recovery via truncated norm minimization minx∥xT∥q and minX∥XT∥Sq from noisy measurements.We first introduce truncated sparse approximation property,a more general robust null space property,and establish the stable recovery of signals and matrices under the truncated sparse approximation property.We also explore the relationship between the restricted isometry property and truncated sparse approximation property.And we also prove that if a measurement matrix A or linear map A satisfies truncated sparse approximation property of order k,then the first inequality in restricted isometry property of order k and of order 2k can hold for certain different constantsδk andδ2k,respectively.Last,we show that ifδs(k+|T^c|)<√(s-1)/s for some s≥4/3,then measurement matrix A and linear map A satisfy truncated sparse approximation property of order k.It should be pointed out that when Tc=Ф,our conclusion implies that sparse approximation property of order k is weaker than restricted isometry property of order sk.

Nonlinear Approximation in Linear 2 Normed Spaces

The best approximation, best Σ approximation and best sup approximation in linear 2 normed spaces are discussed in this paper. The characterization and uniqueness theorems for these nonlinear approximations are given.

Computing Approximation GCD of Several Polynomials by Structured Total Least Norm

The task of determining the greatest common divisors (GCD) for several polynomials which arises in image compression, computer algebra and speech encoding can be formulated as a low rank approximation problem with Sylvester matrix. This paper demonstrates a method based on structured total least norm (STLN) algorithm for matrices with Sylvester structure. We demonstrate the algorithm to compute an approximate GCD. Both the theoretical analysis and the computational results show that the method is feasible.

ON VECTOR VALUED FUNCTION APPROXIMATION USING A PEAK NORM

A peak norm is defined for Lp spaces of E-valued Bochner integrable functions, where E is a Banach space, and best approximations from a sun to elements of the space are characterized. Applications are given to some families of simultaneous best approximation problems.

Three-dimensional gravity inversion based on sparse recovery iteration using approximate zero norm

This research proposes a novel three-dimensional gravity inversion based on sparse recovery in compress sensing. Zero norm is selected as the objective function, which is then iteratively solved by the approximate zero norm solution. The inversion approach mainly employs forward modeling; a depth weight function is introduced into the objective function of the zero norms. Sparse inversion results are obtained by the corresponding optimal mathematical method. To achieve the practical geophysical and geological significance of the results, penalty function is applied to constrain the density values. Results obtained by proposed provide clear boundary depth and density contrast distribution information. The method's accuracy, validity, and reliability are verified by comparing its results with those of synthetic models. To further explain its reliability, a practical gravity data is obtained for a region in Texas, USA is applied. Inversion results for this region are compared with those of previous studies, including a research of logging data in the same area. The depth of salt dome obtained by the inversion method is 4.2 km, which is in good agreement with the 4.4 km value from the logging data. From this, the practicality of the inversion method is also validated.

Distribution function estimates by Wasserstein metric and Bernstein approximation for C^(-1) functions

The aim of the paper is to estimate the density functions or distribution functions measured by Wasserstein metric, a typical kind of statistical distances, which is usually required in the statistical learning. Based on the classical Bernstein approximation, a scheme is presented. To get the error estimates of the scheme, the problem turns to estimating the L1 norm of the Bernstein approximation for monotone C-1 functions, which was rarely discussed in the classical approximation theory. Finally, we get a probability estimate by the statistical distance.

A UNIFIED APPROACH TO CERTAIN PROBLEMS OF BEST LOCAL APPROXIMATION

In this paper we study best local quasi-rational approximation and best local approximation from finite dimensional subspaces of vectorial functions of several variables.Our approach extends and unifies several problems concerning best local multi-point approximation in different norms.

A Class of Constrained Inverse Eigenproblem and Associated Approximation Problem for Symmetric Reflexive Matrices

Let S∈Rn×n be a symmetric and nontrival involution matrix. We say that A∈E R n×n is a symmetric reflexive matrix if AT = A and SAS = A. Let S R r n×n(S)={A|A= AT,A = SAS, A∈Rn×n}. This paper discusses the following two problems. The first one is as follows. Given Z∈Rn×m (m < n),∧= diag(λ1,...,λm)∈Rm×m, andα,β∈R withα<β. Find a subset (?)(Z,∧,α,β) of SRrn×n(S) such that AZ = Z∧holds for any A∈(?)(Z,∧,α,β) and the remaining eigenvaluesλm+1 ,...,λn of A are located in the interval [α,β], Moreover, for a given B∈Rn×n, the second problem is to find AB∈(?)(Z,∧,α,β) such that where ||.|| is the Frobenius norm. Using the properties of symmetric reflexive matrices, the two problems are essentially decomposed into the same kind of subproblems for two real symmetric matrices with smaller dimensions, and then the expressions of the general solution for the two problems are derived.

UNIFORM NORMAL STRUCTURE AND SOLUTIONS OF REICH’S OPEN QUESTION

The open question raised by Reich is studied in a Banach space with uniform normal structure, whose norm is uniformly Gateaux differentiable. Under more suitable assumptions imposed on an asymptotically nonexpansive mapping, an affirmative answer to Reich＇ s open question is given. The results presented extend and improve Zhang Shisheng＇ s recent ones in the following aspects ： （i） Zhang＇ s stronger condition that the sequence of iterative parameters converges to zero is removed; （ii） Zhang＇ s stronger assumption that the asymptotically nonexpansive mapping has a fixed point is removed; （iii） Zhang＇ s stronger condition that the sequence generated by the Banach Contraction Principle is strongly convergent is also removed. Moreover, these also extend and improve the corresponding ones obtained previously by several authors including Reich, Shioji, Takahashi,Ueda and Wittmann.

Identification of Continuous-Time Hammerstein System with Nuclear Norm Convex Relaxation

The nuclear norm convex relaxation method is proposed to force the rank constraint in the identification of the continuous-time( CT) Hammerstein system. The CT Hammerstein system is composed of a linear time invariant( LTI) system and a static nonlinear function( the linear part is followed by the nonlinear part). The nonlinear function is approximated by the pseudospectral basis functions, which have a better performance than Hinge functions and Radial Basis functions. After the approximation on the nonlinear function, the CT Hammerstein system has been transformed into a multiple-input single-output( MISO) linear model system with the differential pre-filters. However, the coefficients of static nonlinearity and the numerators of the linear transfer function are coupled together to challenge the parameters identification of the Hammerstein system. This problem is solved by replacing the one-rank constraint of the regularization optimization with the nuclear norm convex relaxation. Finally, a numerical example is given to verify the accuracy and the efficiency of the method.

基于低秩ADMM的超声图像复原方法

本文提出了一个超声图像复原模型,该模型融合了加权核范数最小化和数据保真度。加权核范数最小化能够自适应处理奇异值以保留图像细节,数据保真度则增强了图像复原效果。本研究采用交替方向乘子法(Alternating Direction Method of Multipliers,ADMM)高效求解,并通过实验结果验证了该方法的优越性。

反中心对称矩阵反问题的最小二乘解

讨论反中心对称矩阵反问题的最小二乘解 ,得到了解的具体表达式 .并讨论了用反中心对称矩阵构造给定矩阵的最佳逼近问题 ,给出了该问题有解的充要条件和解的表达式 .

基于熵特征的雷达辐射源信号识别

针对现有方法识别率低和没有考虑噪声影响的问题,提出一种新的雷达辐射源信号识别方法。将近似熵(ApEn)和范数熵(NoEn)构成特征向量,用神经网络分类器实现自动分类识别。ApEn是定量描述信号复杂性和不规则性的有效测度,NoEn是定量表征信号能量分布的有效参数。理论分析和实验结果表明,熵特征类内聚集性强、类间分离度大,在较大信噪比范围内均能获得非常满意的正确识别率,证实了所提出方法的有效性。

基于稀疏重构的跳频信号时频分析方法

针对现有时频分析方法存在噪声抑制能力弱、时频聚集性不强的缺点,提出了一种基于稀疏重构的跳频信号时频分析方法来获取清晰的、高聚集度的时频图。首先根据惩罚函数的思想建立了跳频信号无约束的稀疏重构模型;然后理论分析了罚函数因子的取值标准;最后用近似l0范数算法求解得出跳频信号的时频图。仿真结果表明该算法能够有效地获取跳频信号的时频图。

基于似零范数和混合优化的压缩感知信号快速重构算法

欠定系统(又称超完备系统)的稀疏信号恢复在压缩感知、源信号分离和信号采集等领域中被广泛研究.目前这类问题主要采用l1范数约束结合线性规划优化或贪婪算法进行求解,但这些方法存在收敛速度慢、恢复精度不高等缺陷.提出一种快速恢复稀疏信号的算法,该算法采用一种新的近似l0范数代替l1范数构造代价函数,并融合牛顿法和最陡梯度法推导出寻优迭代式,以获得似零范数代价函数的最优解.仿真实验和真实数据实验结果表明,与经典算法相比,该算法在能提供相同精度、甚至更好精度的条件下,收敛速度更快.

非比例阻尼结构体系近似解耦分析中的误差研究

对于非比例阻尼结构体系的动力分析，一种常用的方法就是用一个选定的对角矩阵来等效耦合的模态阻尼矩阵，使体系方程解耦。本文研究了这种近似解耦方法所引起的近似误差，导出了误差范数，并得到了体系精确解所在范围。研究结果表明：近似解耦方法有一定的适用性。

利用先验正态分布的贝叶斯网络参数学习

针对贝叶斯网络参数的近似等式约束,提出采用正态分布构建该类约束的数学模型;然后用Dirichlet分布逼近正态分布,并通过目标优化计算Dirichlet分布的超参数;最后采用贝叶斯最大后验概率(maximum a posterior,MAP)估计方法计算网络参数值。在不同样本量的数据集下进行实验测试,将本文方法与其他4种主要方法进行比较,结果表明:该方法的参数学习精度都好于其他4种方法,尤其是在样本量较小的情况下。该方法的运行时间高于其他4种方法,但相同样本量的数据集下,学习精度的提高倍数要高于时间增加的倍数。

弹性力学问题的局部边界积分方程方法

提出了弹性力学平面问题的局部边界积分方程方法．这种方法是一种无网格方法，它采用移动最小二乘近似试函数，且只包含中心在所考虑节点的局部边界上的边界积分．它易于施加本质边界条件．所得系统矩阵是一个带状稀疏矩阵．它组合了伽辽金有限元法、整体边界元法和无单元伽辽金法的优点．该方法可以容易推广到求解非线性问题以及非均匀介质的力学问题。 计算了两个弹性力学平面问题的例子，给出了位移和能量的索波列夫模，所得计算结果证明：该方法是一种具有收敛快、精度高、简便有效的通用方法．

移动最小二乘近似函数中样条权函数的研究

局部边界积分方程方法是无网格方法的一种,它采用移动最小二乘近似试函数,且只包含中心在所考虑节点的局部边界上的边界积分.本文详细研究了移动最小二乘法中样条权函数的构造及其性质,并将各种样条权函数应用于弹性力学平面问题的局部边界积分方程方法中,研究了它对计算结果的收敛性、稳定性和精度的影响.算例表明,高阶样条权函数在局部边界积分方程方法中有好的收敛性、稳定性和精度.