A regularized Newton method for solving equilibrium programming problems with an inexactly specified set

Unstable equilibrium problems are examined in which the objective function and the set where the equilibrium point is sought are specified inexactly. A regularized Newton method, combined with penalty functions, is proposed for solving such problems, and its convergence is analyzed. A regularizing operator is constructed.     

A general regularized gradient-projection method for solving equilibrium and constrained convex minimization problems

In this article, we provide a general iterative method for solving an equilibrium and a constrained convex minimization problem. By using the idea of regularized gradient-projection algorithm (RGPA), we find a common element, which is also a solution of a variational inequality problem. Then the strong convergence theorems are obtained under suitable conditions.     

Preconditioner based on the Sherman-Morrison formula for regularized least squares problems

We propose a sparse approximate inverse preconditioner based on the Sherman-Morrison formula for Tikhonov regularized least square problems. Theoretical analysis shows that, the factorization method can take the advantage of the symmetric property of the coefficient matrix and be implemented cheaply. Combined with dropping rules, the incomplete factorization leads to a preconditioner for Krylov iterative methods to solve regularized least squares problems. Numerical experiments show that our preconditioner is competitive compared to existing methods, especially for ill-conditioned and rank deficient least squares problems.     

An optimal regularized projection method for solving ill-posed problems via dynamical systems method

A new regularized projection method was developed for numerically solving ill-posed equations of the first kind. This method consists of combining the dynamical systems method with an adaptive projection discretization scheme. Optimality of the proposed method was proved on wide classes of ill-posed problems.     

High-order adaptive Gegenbauer integral spectral element method for solving non-linear optimal control problems

In this work, we propose an adaptive spectral element algorithm for solving non-linear optimal control problems. The method employs orthogonal collocation at the shifted Gegenbauer–Gauss points combined with very accurate and stable numerical quadratures to fully discretize the multiple-phase integral form of the optimal control problem. The proposed algorithm relies on exploiting the underlying smoothness properties of the solutions for computing approximate solutions efficiently. In particular, the method brackets discontinuities and ‘points of nonsmoothness’ through a novel local adaptive algorithm, which achieves a desired accuracy on the discrete dynamical system equations by adjusting both the mesh size and the degree of the approximating polynomials. A rigorous error analysis of the developed numerical quadratures is presented. Finally, the efficiency of the proposed method is demonstrated on three test examples from the open literature.     

A block Arnoldi-type contour integral spectral projection method for solving generalized eigenvalue problems

For generalized eigenvalue problems, we consider computing all eigenvalues located in a certain region and their corresponding eigenvectors. Recently, contour integral spectral projection methods have been proposed for solving such problems. In this study, from the analysis of the relationship between the contour integral spectral projection and the Krylov subspace, we conclude that the Rayleigh–Ritz-type of the contour integral spectral projection method is mathematically equivalent to the Arnoldi method with the projected vectors obtained from the contour integration. By this Arnoldi-based interpretation, we then propose a block Arnoldi-type contour integral spectral projection method for solving the eigenvalue problem.     

Convergence analysis of a regularized approximation for solving Fredholm integral equations of the first kind

In this paper, we suggest a convergence analysis for solving Fredholm integral equations of the first kind using Tikhonov regularization under supremum norm. We also provide an a priori parameter choice strategy for choosing the regularization parameter and obtain an error estimate.     

Least‐squares spectral collocation method for the Stokes equations

First‐order system least‐squares spectral collocation methods are presented for the Stokes equations by adopting the first‐order system and modifying the least‐squares functionals in 2 . Then homogeneous Legendre and Chebyshev (continuous and discrete) functionals are shown to be elliptic and continuous with respect to appropriate product weighted norms. The spectral convergence is analyzed for the proposed methods with some numerical experiments. © 2003 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 20: 128–139, 2004     

On the iteratively regularized Gauss-Newton method for solving nonlinear ill-posed problems

The iteratively regularized Gauss-Newton method is applied to compute the stable solutions to nonlinear ill-posed problems when the data is given approximately by with . In this method, the iterative sequence is defined successively by

where is an initial guess of the exact solution and is a given decreasing sequence of positive numbers admitting suitable properties. When is used to approximate , the stopping index should be designated properly. In this paper, an a posteriori stopping rule is suggested to choose the stopping index of iteration, and with the integer determined by this rule it is proved that

with a constant independent of , where denotes the iterative solution corresponding to the noise free case. As a consequence of this result, the convergence of is obtained, and moreover the rate of convergence is derived when satisfies a suitable ``source-wise representation". The results of this paper suggest that the iteratively regularized Gauss-Newton method, combined with our stopping rule, defines a regularization method of optimal order for each . Numerical examples for parameter estimation of a differential equation are given to test the theoretical results.

     

Three new algorithms based on the sequential secant method

We introduce three new implementations of the sequential secant method for solving nonlinear simultaneous equations. Following the ideas of Gragg and Stewart, we store orthogonal factorizations of some of the matrices involved. Degeneracy in the increments of the independent variable is corrected according to simple and theoretically justified procedures. Some numerical experiences are also given.     

求解非光滑方程组的三次正则化方法

考虑求解非光滑方程组的三次正则化方法及其收敛性分析.利用信赖域方法的技巧,保证该方法是全局收敛的.在子问题非精确求解和BD正则性条件成立的前提下,分析了非光滑三次正则化方法的局部收敛速度.最后,数值实验结果验证了该算法的有效性.     

Error analysis for solving the Korteweg‐de Vries equation by a Legendre pseudo‐spectral method

A Legendre pseudo‐spectral method is proposed for the Korteweg‐de Vries equation with nonperiodic boundary conditions. Appropriate base functions are chosen to get an efficient algorithm. Error analysis is given for both semi‐discrete and fully discrete schemes. The numerical results confirm to the theoretical analysis. © (2000) John Wiley & Sons, Inc. Numer Methods Partial Differential Eq 16: 513–534, (2000)     

Spectral residual method without gradient information for solving large-scale nonlinear systems of equations

A fully derivative-free spectral residual method for solving large-scale nonlinear systems of equations is presented. It uses in a systematic way the residual vector as a search direction, a spectral steplength that produces a nonmonotone process and a globalization strategy that allows for this nonmonotone behavior. The global convergence analysis of the combined scheme is presented. An extensive set of numerical experiments that indicate that the new combination is competitive and frequently better than well-known Newton-Krylov methods for large-scale problems is also presented.

     

FOURIER－CHEBYSHEV SPECTRAL METHOD FOR SOLVING THREE－DIMENSIONAL VORTICITY EQUATION

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Legendre spectral method for solving mixed boundary value problems on rectangle

In this paper, we develop a spectral method for mixed inhomogeneous Dirichlet/Neumann/Robin boundary value problems defined on rectangle. Some results on two‐dimensional Legendre approximation in Jacobi‐weighted Sobolev space are established. As examples of applications, spectral schemes are provided for two model problems with mixed inhomogeneous boundary conditions. The spectral accuracy in space of proposed algorithms are proved. Efficient implementations are presented. Numerical results demonstrate their high accuracy and confirm the theoretical analysis well. Copyright © 2016 John Wiley & Sons, Ltd.     

Error analysis of spectral method on a triangle

In this paper, the orthogonal polynomial approximation on triangle, proposed by Dubiner, is studied. Some approximation results are established in certain non-uniformly Jacobi-weighted Sobolev space, which play important role in numerical analysis of spectral and triangle spectral element methods for differential equations on complex geometries. As an example, a model problem is considered. Mathematics subject classifications (2000)  33C45, 41A10, 41A25, 65N35 Ben-yu Guo: The work of this author is supported in part by NSF of China, N.10471095, Science Foundation of Shanghai, N. 04JC14062, The Special Funds for Doctorial Authorities of Education Ministry of China, N. 20040270002, E-institutes of Shanghai Municipal Education Commission, N.E03004, The Shanghai Leading Academic Discipline Project N. T0401 and The Fund N.04DB15 of Shanghai Education Commission.     

A numerical technique for solving IHCPs using Tikhonov regularization method

This study is intended to provide a numerical algorithm for solving a one-dimensional inverse heat conduction problem. The given heat conduction equation, the boundary conditions, and the initial condition are presented in a dimensionless form. The numerical approach is developed based on the use of the solution to the auxiliary problem as a basis function. To regularize the resultant ill-conditioned linear system of equations, we apply the Tikhonov regularization method to obtain the stable numerical approximation to the solution.     

A numerical method for solving nonlinear ill-posed problems

A two-step iterative process for the numerical solution of nonlinear problems is suggested. In order to avoid the ill-posed inversion of the Fréchet derivative operator, some regularization parameter is introduced. A convergence theorem is proved. The proposed method is illustrated by a numerical example in which a nonlinear inverse problem of gravimetry is considered. Based on the results of the numerical experiments practical recommendations for the choice of the regularization parameter are given. Some other iterative schemes are considered.