求解波场变换反演问题的连续正则化方法

本文研究波场变换反演问题.利用连续正则化方法求解波场变换反演问题,构造展平泛函,基于已经正则化的变分问题用差分法作有限维逼近.利用偏差原理和Newton三阶迭代收敛格式选出最优的正则化参数,实施数值求解.通过对数值计算结果与已知波场函数对比,证明该方法的有效性和可行性.与离散正则化算法相比,本文的连续正则化算法具有保结构和收敛速度快等优点.     

解第一类算子方程的一种新的正则化方法

对算子与右端都为近似给定的第一类算子方程提出一种新的正则化方法，依据广义Ａｒｃａｎｇｅｌｉ方法选取正则参数，建立了正则解的收敛性。这种新的正则化方法与通常的Ｔｉｋｈｏｎｏｖ正则化方法相比较，提高了正则解的渐近阶估计。     

切Poisson作用及约化

本文证明了单连通Poisson紧李群切作用及约化Poisson作用于Poisson流形，若带有等动量映射，则可通过调整Poisson流形的Poisson结构，变成保Poisson结构的Poisson作用，并且该作用限制到Poisson流形的辛叶片上，相对于新Poisson结构是Hamiltion作用。我们把Meyer-Marsden-Weinstein约化从Hamiltion作用推广到切Poisson作用，包括正则值和非正则值两种形式。     

基于奇异值分解建立的一种新的正则化方法

根据紧算子的奇异系统理论，引入一种正则化滤子函数，从而建立一种新的正则化方法来求解右端近似给定的第一类算子方程，并给出了正则解的误差分析。通过正则参数的先验选取，证明了正则解的误差具有渐进最优阶。      

某种Hilbert尺度上的Tихонов正则化方法

该文考虑在某种Ｈｉｌｂｅｒｔ尺度上求解不适定问题的Ｔихонов正则化方法，讨论了对应的正则化解的收敛特征，并用此文的结果分析求解解析延拓问题的ｎ阶Ｔихонов正则化方法的性质．     

某种Hilbert尺度上的ТИФОНОВ正则化方法

该文考虑在某种Ｈｉｌｂｅｒｔ尺度上求解不适定问题的ТИФОНОВ正则化方法，讨论了对应的正则化解的收敛特征，并用此文的结果分析求解解析延拓问题的ｎ阶ТИФОНОВ正则化方法的性质。     

同伦摄动稀疏正则化方法及其应用

稀疏正则化方法在参数重构中起到了越来越重要的作用.与传统的正则化方法相比,稀疏正则化方法能较好地重构稀疏变量.由于稀疏正则化的不可微性,需要对已有的经典算法进行改进.本文构建同伦摄动稀疏正则化方法克服标准稀疏正则化的不可微性,并将该方法应用到基于布莱克一斯科尔斯期权定价模型重构隐含波动率和基于托达罗模型重构政策参数.数值实验表明,所提出的方法是收敛和稳定的.     

一种新的正则化方法的正则参数的最优后验选取

应用紧算子的奇异系统和广义Arcangeli方法后验选取正则参数，证明了文[1]中所建立的求解第一类算子方程的正则化方法是收敛的，且正则解具有最优的渐近阶。     

一类逆时反问题的改进正则化方法的收敛性

1引 言 非线性反问题广泛地存在于许多科学和工程问题中,反问题求解的主要困难在于问题的不适定性,即待求函数或参量不连续依赖于观测数据.用来求解非线性不适定问题的方法主要有Tikhonov正则化方法和迭代正则化方法[1,2,3,4].Tikhonov正则化方法是通过引入正则化参数及稳定泛函,将目标泛函离散化,从而得到解的一个稳定近似,即正则化解.     

带复数核的第一类Fredholm积分方程的正则化方法及其应用

本文推广了Ｔｉｋｈｏｎｏｖ正则化方法，导出了带复数核的第一类Ｆｒｅｄｈｏｌｍ积分方程的正则解应满足的正则积分微分方程，并讨论了正则解的收敛性·作为这一方法的应用，数值求解了与二维摇板造波问题相应的一类逆问题，并给出了选择最佳正则参数的一个实用的方法     

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.     

Preconditioning non-monotone gradient methods for retrieval of seismic reflection signals

The core problem in seismic exploration is to invert the subsurface reflectivity from the surface recorded seismic data. However, most of the seismic inverse problems are ill-posed by nature. To overcome the ill-posedness, different regularized least squares methods are introduced in the literature. In this paper, we developed a preconditioning non-monotone gradient method, proved it converges with R-superlinear rate and applied it to seismic deconvolution and imaging. Numerical examples demonstrate that the method is efficient. It helps to improve the resolution of the seismic inversions.     

Semi-smooth Newton methods for the Signorini problem

Semi-smooth Newton methods are analyzed for the Signorini problem. A proper regularization is introduced which guarantees that the semi-smooth Newton method is superlinearly convergent for each regularized problem. Utilizing a shift motivated by an augmented Lagrangian framework, to the regularization term, the solution to each regularized problem is feasible. Convergence of the regularized problems is shown and a report on numerical experiments is given.     

Regularized modified α-processes for nonlinear equations with monotone operators

For a stable approximation of the solution to a nonlinear irregular equation with a monotone operator, a two-step method based on Lavrent’ev scheme and nonlinear regularized α-processes is constructed. These processes are shown to have a linear convergence rate when used to approximate the solution of a regularized equation. The error of the regularized solution is estimated, and the two-step method is shown to be order optimal in the well-posedness class of sourcewise representable solutions.     

关于正定可对称化线性代数方程组的预对称正则化算法

1引言考虑线性代数方程组A_x=b,A∈R~(n×n)非奇异,x,b∈R~n(1)的求解．当系数矩阵是大型稀疏的正定可对称化矩阵,文[1,2]讨论了一类预对称共轭梯度算法(LRSCG算法是其中之一),这类算法的实质是利用非对称的系数矩阵可对称化的性质,并结合共轭梯度法而构造的一种预处理的共轭梯度法[12,16,17]．但非对称的系数     

Numerical simulation of two-dimensional Bingham fluid flow by semismooth Newton methods

This paper is devoted to the numerical simulation of two-dimensional stationary Bingham fluid flow by semismooth Newton methods. We analyze the modeling variational inequality of the second kind, considering both Dirichlet and stress-free boundary conditions. A family of Tikhonov regularized problems is proposed and the convergence of the regularized solutions to the original one is verified. By using Fenchel’s duality, optimality systems which characterize the original and regularized solutions are obtained. The regularized optimality systems are discretized using a finite element method with (cross-grid P₁)-Q₀ elements for the velocity and pressure, respectively. A semismooth Newton algorithm is proposed in order to solve the discretized optimality systems. Using an additional relaxation, a descent direction is constructed from each semismooth Newton iteration. Local superlinear convergence of the method is also proved. Finally, we perform numerical experiments in order to investigate the behavior and efficiency of the method.     

Nonlinear regression modeling using regularized local likelihood method

We introduce a nonlinear regression modeling strategy, using a regularized local likelihood method. The local likelihood method is effective for analyzing data with complex structure. It might be, however, pointed out that the stability of the local likelihood estimator is not necessarily guaranteed in the case that the structure of system is quite complex. In order to overcome this difficulty, we propose a regularized local likelihood method with a polynomial function which unites local likelihood and regularization. A crucial issue in constructing nonlinear regression models is the choice of a smoothing parameter, the degree of polynomial and a regularization parameter. In order to evaluate models estimated by the regularized local likelihood method, we derive a model selection criterion from an information-theoretic point of view. Real data analysis and Monte Carlo experiments are conducted to examine the performance of our modeling strategy.     

Truncated regularized Newton method for convex minimizations

Recently, Li et al. (Comput. Optim. Appl. 26:131–147, 2004) proposed a regularized Newton method for convex minimization problems. The method retains local quadratic convergence property without requirement of the singularity of the Hessian. In this paper, we develop a truncated regularized Newton method and show its global convergence. We also establish a local quadratic convergence theorem for the truncated method under the same conditions as those in Li et al. (Comput. Optim. Appl. 26:131–147, 2004). At last, we test the proposed method through numerical experiments and compare its performance with the regularized Newton method. The results show that the truncated method outperforms the regularized Newton method. The work was supported by the 973 project granted 2004CB719402 and the NSF project of China granted 10471036.     

A method for structured linear total least norm on blind deconvolution problem

The regularized structured total least norm (RSTLN) method finds an approximate solutionx and error matrixE to the overdetermined linear system (H+E)x≈b, preserving structure ofH. A new separation scheme by parts of variables for the regularized structured total least norm on blind deconvolution problem is suggested. A method combining the regularized structured total least norm method with a separation by parts of variables can be obtain a better approximated solution and a smaller residual. Computational results for the practical problem with Block Toeplitz with Toeplitz Block structure show the new method ensures more efficiency on image restoration.     

Numerical methods for Fredholm integral equations with singular right-hand sides

Fredholm integral equations with the right-hand side having singularities at the endpoints are considered. The singularities are moved into the kernel that is subsequently regularized by a suitable one-to-one map. The Nyström method is applied to the regularized equation. The convergence, stability and well conditioning of the method is proved in spaces of weighted continuous functions. The special case of the weakly singular and symmetric kernel is also investigated. Several numerical tests are included.