左定Sturm-Liouville算子的特征值计算(英文)

研究了定义在有限区间[a,b]上的具有分离型和混合型边界条件的左定正则Sturm-Liouville算子的特征值问题.把具有混合型边界条件的左定正则Sturm-Liouville问题转化成二维的、具有分离型边界条件的右定正则Sturm-Liouville问题,给出了具有混合型边界条件的左定正则Sturm-Liouville算子的特征值的数值计算方法.     

A—光滑正则化算子

本文研究了紧算子方程的Moore-Penrose广义解的逼近,引进了A-导数的概念和对应的A-光滑正则化算子.这个双参数的A-光滑正则化算子族有明显的变分意义,并且包含正则化算子作为它的特殊情形,以(修正的)截断奇异值分解方法作为它的极限情形.这些正则化算子的性质表明它们有广阔的实际应用可能性.     

求解病态问题的一种改进的Tikhonov正则化：⑴正则化方法的建立

对于带有右扰动数据的第一类紧算子方程的病态问题。本文应用正则化子建立了一类新的正则化求解方法,称之为改进的Ｔｉｋｏｎｏｖ正则化;通过适当选取２正则参数,证明了正则解具有最优的渐近收敛阶,与通常的Ｔｉｋｈｏｎｏｖ正则化相比,这种改进的正则化可使正则解取到足够高的最优渐近阶。     

关于迭代Tikhonov正则化的最优正则参数选取

本文讨论了算子和右端都近似给定的第一类算子方程的迭代Ｔｉｋｈｏｎｏｖ正则化，给出了不依赖于准确解的任何信息但能得到最优收敛阶的正则参数选取法。     

正则Cosine算子函数的乘积扰动定理

本文研究了正则cosine算子函数的乘积扰动性,在正则化算子C的值域不 一定稠密的一般情形下,获得了若干正则cosine算子函数的乘积扰动定理.     

非线性第一类单调算子方程正则解的收敛率讨论

讨论非线性不适定单调算子方程正则解的收敛率问题。在一定的条件下,得到了正则解的收敛率为Ｏ（δ＾１３）,这里δ为近似数据的误差界。     

基于埃特金加速的改进Landweber迭代正则化方法

为克服Landweber迭代正则化方法在求解大规模不适定问题时收敛速度慢的不足,将埃特金加速技巧与不动点迭代相结合,构建了能快速收敛的改进Landweber迭代正则化方法.数值实验结果表明:改进的迭代正则化方法在稳定求解不适定问题时,能够快速地收敛至问题的最优解,较Landweber迭代正则化方法大大提高了收敛速度.     

退化正则半群

引入了退化正则半群的定义，给出退化正则半群的一些基本性质，并证明了用多值线性算子刻划的指数有界退化正则半群的生成定理．     

一个新的量子力学模型

讨论了具有一个Bose自由度的量子力学模型.用传统的方法构造量子力学模型时,需要知道相应的经典模型,并利用经典Poisson括号与正则量子对易子之间的对应关系来得到正则量子化条件.在讨论的量子模型里,不需要首先讨论相应的经典模型.此模型里,Lagrange量具有算子规范不变性,定域的理论中需引入相应的规范势.动力学变量的Euler-Lagrange运动方程就是通常的运动方程,而规范势仅仅给出一个约束,而此约束刚好是正则量子化条件.     

半正定算子方程正则解的收敛率和参数选取法

1 引言 关于第一类线性算子方程 Ax=y (1)已有很多文献和专著作过研究。由于方程(1)一般是不适定的.须用正则化方法求解.最著名的方法是Tikhonov正则化方法.关于其正则解的收敛性、收敛率及参数选取法,专著[2,3]已作了深入系统的研究.当A为半正定自共轭的有界线性算子时,可应用 Lavrent’ev正则化方法或称为简化正则化方法,由于其在计算上所具有的优越性,已引起不少学者的关注.本文将用简化正则化方法研究当A为半正定线性有界算子的情形.实际上,此时的A是一个单调算子,而对单调算子方程,已有很多研究结果,只不过主要是关于正则解的收敛性及有限维逼近的讨论,而未涉及正则解的收敛率问题。我们将在第2节中讨论正则解的收敛率.并给出一种后验的参数选取法,这种参数选取法比先验的参数选取法的优越之处在于它不依赖于解的“光滑性”条件”“,但当满足某种“光滑性”条件时,所得到的收敛率是最优的.第3节中我们讨论了当算子方程的右端数据及算子本身都为近似已知的情形,这种情形更接近于实际的数学模型。文献[13,14]曾作过研究.     

Exact penalty operators and regularization of parabolic variational inequalities with an obstacle inside a domain

A class of parabolic variational inequalities with zero obstacle inside the domain is considered. For this class, exact penalty operators are defined, which are then used to construct and study regularization methods. The following estimates are obtained for the closeness of the original and regularized problem:

$$u - \varepsilon _1 \leqslant u_\varepsilon \leqslant u + \varepsilon _1 and \left\| {u - u_\varepsilon } \right\|_{L_2 (0,T;V^0 )} = O(\varepsilon ^{3/4} ).$$

     

Erdős-Ko-Rado theorems in certain semilattices

Suda (2012) extended the Erds-Ko-Rado theorem to designs in strongly regularized semilattices.In this paper we generalize Suda’s results in regularized semilattices and partition regularized semilattices,give many examples for these semilattices and obtain their intersection theorems.     

Semi-discrete stabilized finite element methods for Navier–Stokes equations with nonlinear slip boundary conditions based on regularization procedure

Based on the pressure projection stabilized methods, the semi-discrete finite element approximation to the time-dependent Navier–Stokes equations with nonlinear slip boundary conditions is considered in this paper. Because this class of boundary condition includes the subdifferential property, then the variational formulation is the Navier–Stokes type variational inequality problem. Using the regularization procedure, we obtain a regularized problem and give the error estimate between the solutions of the variational inequality problem and the regularized problem with respect to the regularized parameter \({\varepsilon}\), which means that the solution of the regularized problem converges to the solution of the Navier–Stokes type variational inequality problem as the parameter \({\varepsilon\longrightarrow 0}\). Moreover, some regularized estimates about the solution of the regularized problem are also derived under some assumptions about the physical data. The pressure projection stabilized finite element methods are used to the regularized problem and some optimal error estimates of the finite element approximation solutions are derived.     

A class of semigroups regularized in space and time

We consider α-times integrated C-regularized semigroups, which are a hybrid between semigroups regularized in space (C-semigroups) and integrated semigroups regularized in time. We study the basic properties of these objects, also in absence of exponential boundedness. We discuss their generators and establish an equivalence theorem between existence of integrated regularized semigroups and well-posedness of certain Cauchy problems. We investigate the effect of smoothing regularized semigroups by fractional integration.     

A new regularized quasi-Newton method for unconstrained optimization

In this paper, we propose a new regularized quasi-Newton method for unconstrained optimization. At each iteration, a regularized quasi-Newton equation is solved to obtain the search direction. The step size is determined by a non-monotone Armijo backtracking line search. An adaptive regularized parameter, which is updated according to the step size of the line search, is employed to compute the next search direction. The presented method is proved to be globally convergent. Numerical experiments show that the proposed method is effective for unconstrained optimizations and outperforms the existing regularized Newton method.     

双连续C正则预解算子族的生成定理

基于C正则预解算子族和双连续C_0半群引入了双连续C正则预解算子族的概念,考察了双连续C正则预解算子族生成元与预解式之间的关系,给出了双连续C正则预解算子族Hille-Yosida型生成定理,从而对Bananch空间强连续半群的生成定理进行了推广.     

Projection Method Approach for General Regularized Non-convex Variational Inequalities

In this paper, we investigate or analyze non-convex variational inequalities and general non-convex variational inequalities. Two new classes of non-convex variational inequalities, named regularized non-convex variational inequalities and general regularized non-convex variational inequalities, are introduced, and the equivalence between these two classes of non-convex variational inequalities and the fixed point problems are established. A projection iterative method to approximate the solutions of general regularized non-convex variational inequalities is suggested. Meanwhile, the existence and uniqueness of solution for general regularized non-convex variational inequalities is proved, and the convergence analysis of the proposed iterative algorithm under certain conditions is studied.     

A minimum effort optimal control problem for the wave equation

A minimum effort optimal control problem for the undamped wave equation is considered which involves L ^∞-control costs. Since the problem is non-differentiable a regularized problem is introduced. Uniqueness of the solution of the regularized problem is proven and the convergence of the regularized solutions is analyzed. Further, a semi-smooth Newton method is formulated to solve the regularized problems and its superlinear convergence is shown. Thereby special attention has to be paid to the well-posedness of the Newton iteration. Numerical examples confirm the theoretical results.     

Generalization performance of least-square regularized regression algorithm with Markov chain samples

The previously known works describing the generalization of least-square regularized regression algorithm are usually based on the assumption of independent and identically distributed (i.i.d.) samples. In this paper we go far beyond this classical framework by studying the generalization of least-square regularized regression algorithm with Markov chain samples. We first establish a novel concentration inequality for uniformly ergodic Markov chains, then we establish the bounds on the generalization of least-square regularized regression algorithm with uniformly ergodic Markov chain samples, and show that least-square regularized regression algorithm with uniformly ergodic Markov chains is consistent.