CHEBYSHEV WEIGHTED NORM LEAST-SQUARES SPECTRAL METHODS FOR THE ELLIPTIC PROBLEM

We develop and analyze a first-order system least-squares spectral method for the second-order elhptic boundary value problem with variable coefficients. We first analyze the Chebyshev weighted norm least-squares functional defined by the sum of the Lw^2- and Hw^-1- norm of the residual equations and then we eplace the negative norm by the discrete negative norm and analyze the discrete Chebyshev weighted least-squares method. The spectral convergence is derived for the proposed method. We also present various numerical experiments. The Legendre weighted least-squares method can be easily developed by following this paper.     

H least-squares method for the velocity–pressure–stress formulation of Stokes equations

This paper studies the discrete H⁻¹-norm least-squares method for the incompressible Stokes equations based on the velocity–pressure–stress formulation by the least-squares functional defined as the sum of L²-norms and H⁻¹-norm of the residual equations. Some computational experiments by multigrid method and preconditioning conjugate gradient method (PCGM) on this method are shown by taking efficient and β in the discrete solution operator T_h=h²I+βB_h corresponding to the minus one norm. We also propose a new method and compare it with PCGM and multigrid method through the analysis of numerical experiments depending on the choice of β.     

Discrete ill-posed least-squares problems with a solution norm constraint

Straightforward solution of discrete ill-posed least-squares problems with error-contaminated data does not, in general, give meaningful results, because propagated error destroys the computed solution. Error propagation can be reduced by imposing constraints on the computed solution. A commonly used constraint is the discrepancy principle, which bounds the norm of the computed solution when applied in conjunction with Tikhonov regularization. Another approach, which recently has received considerable attention, is to explicitly impose a constraint on the norm of the computed solution. For instance, the computed solution may be required to have the same Euclidean norm as the unknown solution of the error-free least-squares problem. We compare these approaches and discuss numerical methods for their implementation, among them a new implementation of the Arnoldi–Tikhonov method. Also solution methods which use both the discrepancy principle and a solution norm constraint are considered.     

A least-squares approach based on a discrete minus one inner product for first order systems

The purpose of this paper is to develop and analyze a least-squares approximation to a first order system. The first order system represents a reformulation of a second order elliptic boundary value problem which may be indefinite and/or nonsymmetric. The approach taken here is novel in that the least-squares functional employed involves a discrete inner product which is related to the inner product in (the Sobolev space of order minus one on ). The use of this inner product results in a method of approximation which is optimal with respect to the required regularity as well as the order of approximation even when applied to problems with low regularity solutions. In addition, the discrete system of equations which needs to be solved in order to compute the resulting approximation is easily preconditioned, thus providing an efficient method for solving the algebraic equations. The preconditioner for this discrete system only requires the construction of preconditioners for standard second order problems, a task which is well understood.

     

Extensions of block-projections methods with relaxation parameters to inconsistent and rank-deficient least-squares problems

There exist many classes of block-projections algorithms for approximating solutions of linear least-squares problems. Generally, these methods generate sequences convergent to the minimal norm least-squares solution only for consistent problems. In the inconsistent case, which usually appears in practice because of some approximations or measurements, these sequences do no longer converge to a least-squares solution or they converge to the minimal norm solution of a “perturbed” problem. In the present paper, we overcome this difficulty by constructing extensions for almost all the above classes of block-projections methods. We prove that the sequences generated with these extensions always converge to a least-squares solution and, with a suitable initial approximation, to the minimal norm solution of the problem. Numerical experiments, described in the last section of the paper, confirm the theoretical results obtained.     

二阶常微初值问题的时间间断最小二乘有限元法

采用时间间断最小二乘线性有限元方法求解二阶常微分方程初值问题.利用回收技巧及离散Gronwall引理证明了方法的稳定性.通过引入有限元空间上的范数,给出了方法在该范数意义下丰满的误差估计.数值实验验证了理论分析结果.     

求解线性Sobolev方程的分裂型最小二乘混合元方法

本文通过引入适当的最小二乘极小化泛函,对一类线性Sobolev方程提出了两种分裂型最小二乘混合元格式,格式最大优点在于将耦合的方程组系统分裂成两个独立的子系统,进而极大降低了原问题求解的难度和规模,理论分析表明格式对原未知量及新引入的未知通量分别具有最优阶L2(Ω)模误差估计和次优阶H(div;Ω)模误差估计.数值试验很好的验证了这一点.     

ON THE BREAKDOWNS OF THE GALERKIN AND LEAST-SQUARES METHODS

1　ＩｎｔｒｏｄｕｃｔｉｏｎＷｅｃｏｎｓｉｄｅｒｌｉｎｅａｒｓｙｓｔｅｍｓｏｆｔｈｅｆｏｒｍＡｘ=ｂ,(1 )ｗｈｅｒｅＡ∈ＣＮ×Ｎｉｓｎｏｎｓｉｎｇｕｌａｒａｎｄｐｏｓｓｉｂｌｙｎｏｎ Ｈｅｒｍｉｔｉａｎ .Ａｍａｊｏｒｃｌａｓｓｏｆｍｅｔｈｏｄｓｆｏｒｓｏｌｖｉｎｇ (1 )ｉｓｔｈｅｃｌａｓｓｏｆＫｒｙｌｏｖｓｕｂｓｐａｃｅｍｅｔｈｏｄｓ (ｓｅｅ[6] ,[1 3]ｆｏｒｏｖｅｒｖｉｅｗｓｏｆｓｕｃｈｍｅｔｈｏｄｓ) ,ｄｅｆｉｎｅｄｂｙｔｈｅｐｒｏｐｅｒｔｉｅｓｘｍ ∈ｘ0 +Ｋｍ(ｒ0 ,Ａ) ;(2 )ｒｍ ⊥Ｌｍ, (3)ｗｈｅ…     

The solution of orthogonal Procrustes problems for a family of orthogonally invariant norms

The orthogonal Procrustes problem involves finding an orthogonal matrix which transforms one given matrix into another in the least-squares sense, and thus it requires the minimization of the Frobenius matrix norm. We consider, the solution of this problem for a family of orthogonally invariant norms which includes the Frobenius norm as a special case.     

Crank-Nicolson split least-squares procedure for nonlocal reactive flow in porous media

In this paper, we introduce a Crank-Nicolson split least-squares Galerkin finite element procedure for parabolic integro-differential equations, arising in the modeling of nonlocal reactive flows in porous media. By selecting the least-squares functional properly, the procedure can be split into two independent sub-procedures, one of which is for the primitive unknown and the other is for the flux. By carefully choosing projections, we get optimal order H ¹(Ω) and L ²(Ω) norm error estimates for u and sub-optimal (L ²(Ω)) ^d norm error estimate for σ with second-order accuracy in time increment. The numerical examples are given to testify the efficiency of the introduced scheme.     

A least-squares finite element method based on the Helmholtz decomposition for hyperbolic balance laws

In this paper, a least-squares finite element method for scalar nonlinear hyperbolic balance laws is proposed and studied. The approach is based on a formulation that utilizes an appropriate Helmholtz decomposition of the flux vector and is related to the standard notion of a weak solution. This relationship, together with a corresponding connection to negative-norm least-squares, is described in detail. As a consequence, an important numerical conservation theorem is obtained, similar to the famous Lax–Wendroff theorem. The numerical conservation properties of the method in this paper do not fall precisely in the framework introduced by Lax and Wendroff, but they are similar in spirit as they guarantee that when L² convergence holds, the resulting approximations approach a weak solution to the hyperbolic problem. The least-squares functional is continuous and coercive in an H⁻¹-type norm, but not L²-coercive. Nevertheless, the L² convergence properties of the method are discussed. Convergence can be obtained either by an explicit regularization of the functional, that provides control of the L² norm, or by properly choosing the finite element spaces, providing implicit control of the L² norm. Numerical results for the inviscid Burgers equation with discontinuous source terms are shown, demonstrating the L² convergence of the obtained approximations to the physically admissible solution. The numerical method utilizes a least-squares functional, minimized on finite element spaces, and a Gauss–Newton technique with nested iteration. We believe that the linear systems encountered with this formulation are amenable to multigrid techniques and combining the method with adaptive mesh refinement would make this approach an efficient tool for solving balance laws (this is the focus of a future study).     

Short Note: A Note on Nonstandard Finite Difference Schemes for Reaction–Diffusion Equations Having Linear Advection

Nonstandard modified upwind difference scheme for one dimensional nonlinear reaction–diffusion equation with linear advection is given in this note. The use of a positivity condition allows the determination of a functional relation between the time and space step sizes, and it is weaker than that of the corresponding simple upwind difference scheme. Error estimate in the discrete l ^∞ norm is provided under suitable assumptions.     

Compression of Multivariate Discrete Measures and Applications

In this article, we discuss two methods for the compression of multivariate discrete measures, with applications to node reduction in numerical cubature and least-squares approximation. The methods are implemented in the Matlab computing environment, in dimension two.     

Sylvester矩阵方程极小范数最小二乘解的迭代解法

研究了Sylvester矩阵方程最小二乘解以及极小范数最小二乘解的迭代解法,首先利用递阶辨识原理,得到了求解矩阵方程AX+YB=C的极小范数最小二乘解的一种迭代算法,进而,将这种算法推广到一般线性矩阵方程A_iX_iB_i=C的情形,最后,数值例子验证了算法的有效性.     

Mathematical programming via the least-squares method

The least-squares method is used to obtain a stable algorithm for a system of linear inequalities as well as linear and nonlinear programming. For these problems the solution with minimal norm for a system of linear inequalities is found by solving the non-negative least-squares (NNLS) problem. Approximate and exact solutions of these problems are discussed. Attention is mainly paid to finding the initial solution to an LP problem. For this purpose an NNLS problem is formulated, enabling finding the initial solution to the primal or dual problem, which may turn out to be optimal. The presented methods are primarily suitable for ill-conditioned and degenerate problems, as well as for LP problems for which the initial solution is not known. The algorithms are illustrated using some test problems.     

Discrete linearized least-squares rational approximation on the unit circle

We already generalized the Rutishauser—Gragg—Harrod—Reichel algorithm for discrete least-squares polynomial approximation on the real axis to the rational case. In this paper, a new method for discrete least-squares linearized rational approximation on the unit circle is presented. It generalizes the algorithms of Reichel—Ammar—Gragg for discrete least-squares polynomial approximation on the unit circle to the rationale case. The algorithm is fast in the sense that it requires order m computation time where m is the number of data points and is the degree of the approximant. We describe how this algorithm can be implemented in parallel. Examples illustrate the numerical behavior of the algorithm.     

Local parameter identifiability for one class of hybrid systems

We consider the problem of local parameter identifiability for a hybrid system with components that are continuous and discrete in time. The set of observations is the vector-solution (depending on the parameter that is continuous in time) of the discrete component. Sufficient conditions of local parameter identifiability have been formulated using the earlier introduced notion of normalized separability of the set of parameters from the kernel of a special functional. An example where the condition of normed separability is reduced to some rank criterion is given.     

Algorithms for the computation of functionals defined on the solution of a discrete ill-posed problem

We study a linear, discrete ill-posed problem, by which we mean a very ill-conditioned linear least squares problem. In particular we consider the case when one is primarily interested in computing a functional defined on the solution rather than the solution itself. In order to alleviate the ill-conditioning we require the norm of the solution to be smaller than a given constant. Thus we are lead to minimizing a linear functional subject to two quadratic constraints. We study existence and uniqueness for this problem and show that it is essentially equivalent to a least squares problem with a linear and a quadratic constraint, which is easier to handle computationally. Efficient algorithms are suggested for this problem.     

On the Γ-Convergence of Discrete Dynamics and Variational Integrators

Summary For a simple class of Lagrangians and variational integrators, derived by time discretization of the action functional, we establish (i) the Γ-convergence of the discrete action sum to the action functional; (ii) the relation between Γ-convergence and weak* convergence of the discrete trajectories in {itW{su1,℞}}({ofR};{ofr{sun}; and (iii) the relation between Γ-convergence and the convergence of the Fourier transform of the discrete trajectories as measured in the flat norm.     

Application of denoising methods to regularizationof ill-posed problems

Linear systems of equations and linear least-squares problems with a matrix whose singular values “cluster” at the origin and with an error-contaminated data vector arise in many applications. Their numerical solution requires regularization, i.e., the replacement of the given problem by a nearby one, whose solution is less sensitive to the error in the data. The amount of regularization depends on a parameter. When an accurate estimate of the norm of the error in the data is known, this parameter can be determined by the discrepancy principle. This paper is concerned with the situation when the error is white Gaussian and no estimate of the norm of the error is available, and explores the possibility of applying a denoising method to both reduce this error and to estimate its norm. Applications to image deblurring are presented.