微分方程解的存在区间的确定

从六个方面说明了确定一阶微分方程解的存在区间的方法.     

Conjugate directions method for solving interval linear systems

We propose the interval version of the conjugate directions method, to solve the problem of linear systems, with symmetric and positive definite interval matrix A, and a right-hand side interval vector b. This revised version was published online in June 2006 with corrections to the Cover Date.     

基于次梯度选取的非光滑优化强次可行方向法

本文结合次梯度选取技术及割平面法和强次可行方向法的思想,提出了一个求解目标函数非光滑约束优化问题的强次可行方向算法.通过设计一个新的寻找搜索方向子问题和构造新型线搜索,算法不仅能接受不可行的初始点,而且能保持迭代点的强次可行性,同时避免在可行域外目标函数值的不适度增加.算法具备全局收敛性,且初步的数值试验表明算法是稳定有效的.     

韧性金属Kaiser效应方向独立性的实验研究

Kaiser效应是声发射技术实际应用的理论基础。本文以韧性金属材料Q345不同方向拉伸实验为例,探究力的方向对韧性金属Kaiser效应的影响。实验结果表明,韧性金属材料在某一方向加载时,表现出了Kaiser效应;当改变加载方向时,呈现出与第一次加载方向类似的声发射特性,说明声发射现象对其他加载方向上所承受的最大载荷不具有记忆的能力,而仅与该方向承载历史有关,即韧性金属Kaiser效应与力的方向性间具有独立性。同时,利用细观损伤理论对这一现象产生的机理进行了定性的解释。     

盲重构频域阵列信号的压缩感知水声目标方位估计

针对复杂海洋环境条件下压缩感知水声目标方位估计性能下降的问题,利用盲源分离能够提高信噪比的优势,提出了一种盲重构频域阵列信号的压缩感知水声目标方位估计方法。首先将阵元域信号通过傅里叶变换方法得到多个子带阵列信号;然后对各个子带阵列信号进行复数域盲源分离得到子带解混矩阵和子带分离信号估计,并对子带分离信号进行属性分析和处理;再根据处理后的子带分离信号和子带解混矩阵重构子带阵列信号,对重构的子带阵列信号采用频域压缩感知方法进行空间谱估计,得到各个子带的空间谱;最后将各子带得到的空间谱进行求和,搜索求和后空间谱的峰值则可实现目标方位估计。模拟器数据和海上实测数据验证结果表明,同等条件下该方法的目标检测能力优于经典的最小方差无失真响应(Minimum Variance Distortionless Response,MVDR)方法、频域压缩感知(Compressed Sensing,CS)方法、盲源分离(Blind Source Separation,BSS)与MVDR相结合的方法(BSS+MVDR方法),测向精度更高,明显提高了弱目标信号的空间谱能量,增强了声呐检测弱目标的能力。     

确定线性偏微分方程组可积系统的对合特征集方法

基于Ritt-Wu特征集方法和Riquier-Janet理论,给出一种将线性微分方程组化成简单标准形式的有效算法．该算法通过消去冗余和添加可积条件获得线性微分方程组的完全可积系统(有形式幂级数解)或不相容判定．该算法不仅适用于常系数的线性偏微分方程组,而且对于变系数(以函数为系数)仍然有效．作者还给出了完全可积系统判定定理及其严格证明．     

A Modified Alternating Direction Method for Variational Inequality Problems

The alternating direction method is an attractive method for solving large-scale variational inequality problems whenever the subproblems can be solved efficiently. However, the subproblems are still variational inequality problems, which are as structurally difficult to solve as the original one. To overcome this disadvantage, in this paper we propose a new alternating direction method for solving a class of nonlinear monotone variational inequality problems. In each iteration the method just makes an orthogonal projection to a simple set and some function evaluations. We report some preliminary computational results to illustrate the efficiency of the method. Accepted 4 May 2001. Online publication 19 October, 2001.     

Robust tensor train component analysis

Robust Principal Component Analysis plays a key role in various fields such as image and video processing, data mining, and hyperspectral data analysis. In this paper, we study the problem of robust tensor train (TT) principal component analysis from partial observations, which aims to decompose a given tensor into the low TT rank and sparse components. The decomposition of the proposed model is used to find the hidden factors and help alleviate the curse of dimensionality via a set of connected low-rank tensors. A relaxation model is to minimize a weighted combination of the sum of nuclear norms of unfolding matrices of core tensors and the tensor ${?}_1$ norm. A proximal alternating direction method of multipliers is developed to solve the resulting model. Furthermore, we show that any cluster point of the convergent subsequence is a Karush-Kuhn-Tucker point of the proposed model under some conditions. Extensive numerical examples on both synthetic data and real-world datasets are presented to demonstrate the effectiveness of the proposed approach.     

解无约束最优化问题的块共轭方向法

在给出块共轭概念的基础上，提出了适合并行计算的向量组的块共轭化方法，进而得到解无约束最优化问题的并行块共轭方向法．有大量数值结果表明块共轭方向法具有工作量少．适用函数范围广等特点，是一种比较有效的无约束最优化方法．     

二维抛物型方程参数反演的遗传算法

本文研究了二维抛物型方程参数反演问题.利用遗传算法求解此反演问题的方法,把参数反演问题转化为优化问题,通过演化计算方法求解.它从多个初始点开始寻优,借助交叉和变异算子来获得参数的全局最优解.且数值模拟结果表明,具有精度高、编程简单、易于计算机实现等特点.