Polynomial Acceleration Methods for Solving Singular Systems of Linear Equations

In this paper we study the polynomial acceleration methods for solving singular linear systems. We establish iterative schemes, show their convergence and find iteration error bounds.     

Locally Polynomial Method for Solving Systems of Linear Inequalities

Computational Mathematics and Mathematical Physics - A numerical method combining a gradient technique with the projection onto a linear manifold is proposed for solving systems of linear...     

On Reducibility of Systems of Linear Differential Equations with Quasiperiodic Skew-Adjoint Matrices

We prove that there exists an open set of irreducible systems in the space of systems of linear differential equations with quasiperiodic skew-adjoint matrices and fixed frequency module.     

Projective-Dual Method for Solving Systems of Linear Equations with Nonnegative Variables

In order to solve an underdetermined system of linear equations with nonnegative variables, the projection of a given point onto its solutions set is sought. The dual of this problem—the problem of unconstrained maximization of a piecewise-quadratic function—is solved by Newton’s method. The problem of unconstrained optimization dual of the regularized problem of finding the projection onto the solution set of the system is considered. A connection of duality theory and Newton’s method with some known algorithms of projecting onto a standard simplex is shown. On the example of taking into account the specifics of the constraints of the transport linear programming problem, the possibility to increase the efficiency of calculating the generalized Hessian matrix is demonstrated. Some examples of numerical calculations using MATLAB are presented.     

Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems

We consider the fundamental problem of solving quadratic systems of equations in , and is unknown. We propose a novel method, which starts with an initial guess computed by means of a spectral method and proceeds by minimizing a nonconvex functional as in the Wirtinger flow approach [13]. There are several key distinguishing features, most notably a distinct objective functional and novel update rules, which operate in an adaptive fashion and drop terms bearing too much influence on the search direction. These careful selection rules provide a tighter initial guess, better descent directions, and thus enhanced practical performance. On the theoretical side, we prove that for certain unstructured models of quadratic systems, our algorithms return the correct solution in linear time, i.e., in time proportional to reading the data {a_i} and {y_i} as soon as the ratio m/n between the number of equations and unknowns exceeds a fixed numerical constant. We extend the theory to deal with noisy systems in which we only have and prove that our algorithms achieve a statistical accuracy, which is nearly unimprovable. We complement our theoretical study with numerical examples showing that solving random quadratic systems is both computationally and statistically not much harder than solving linear systems of the same size—hence the title of this paper. For instance, we demonstrate empirically that the computational cost of our algorithm is about four times that of solving a least squares problem of the same size. © 2016 Wiley Periodicals, Inc.     

Newton-Type Method for Solving Systems of Linear Equations and Inequalities

Computational Mathematics and Mathematical Physics - A Newton-type method is proposed for numerical minimization of convex piecewise quadratic functions, and its convergence is analyzed....     

The Minimal Polynomial of Some Matrices Via Quaternions

This work provides explicit characterizations and formulae for the minimal polynomials of a wide variety of structured 4 × 4 matrices. These include symmetric, Hamiltonian and orthogonal matrices. Applications such as the complete determination of the Jordan structure of skew-Hamiltonian matrices and the computation of the Cayley transform are given. Some new classes of matrices are uncovered, whose behaviour insofar as minimal polynomials are concerned, is remarkably similar to those of skew-Hamiltonian and Hamiltonian matrices. The main technique is the invocation of the associative algebra isomorphism between the tensor product of the quaternions with themselves and the algebra of real 4 × 4 matrices. Extensions to higher dimensions via Clifford Algebras are discussed.     

动力系统方法解决无界线性算子方程

针对反问题中出现的第一类算子方程Au=f,其中A是实Hilbert空间H上的一个无界线性算子利用动力系统方法和正则化方法,求解上述问题的正则化问题的解:u'(t)=-A~*(Au(t)-f)利用线性算子半群理论可以得到上述正则化问题的解的半群表示,并证明了当t→∞时,所得的正则化解收敛于原问题的解.     

线性方程组的异步松弛迭代法*

本文考虑解线性方程组经典迭代法的异步形式,对系数矩阵为H矩阵,给出了异步迭代过程收敛性的充分条件,这不仅降低了文献[3]对系数矩阵的要求,而且收敛区域比文献[3]的大.     

以方程(组)为中心展开高等代数课程的教学

以方程(组)为中心阐述了高等代数各部分内容的内在联系,给出了高等代数课程教学的一种新的教学模式.     

Strang-Type Preconditioners for Solving Linear Systems from Delay Differential Equations

We consider the solution of delay differential equations (DDEs) by using boundary value methods (BVMs). These methods require the solution of one or more nonsymmetric, large and sparse linear systems. The GMRES method with the Strang-type block-circulant preconditioner is proposed for solving these linear systems. We show that if a P _{k 1},k ₂-stable BVM is used for solving an m-by-m system of DDEs, then our preconditioner is invertible and all the eigenvalues of the preconditioned system are clustered around 1. It follows that when the GMRES method is applied to solving the preconditioned systems, the method may converge fast. Numerical results are given to illustrate the effectiveness of our methods.     

Convergence Domains of AOR Type Iterative Matrices for Solving Non-Hermitian Linear Systems

We discuss AOR type iterative methods for solving non-Hermitian linear systems based on Hermitian splitting and skew-Hermitian splitting. Convergence domains of iterative matrices are given and optimal parameters are investigated for skew-Hermitian splitting. Numerical examples are presented to compare the effectiveness of the iterative methods in different points in the domain. In addition, a model problem of three-dimensional convection-diffusion equation is used to illustrated the application of our results.     

The Use of Pre-conditioning in Iterative Methods for Solving Linear Equations with Symmetric Positive Definite Matrices

The asymptotic convergence rates of many standard iterativemethods for the solution of linear equations can be shown todepend inversely on the P-condition number of the co-efficientmatrix. The notion of minimizing the P-condition number andhence maximizing the convergence rate by the introduction ofa new pre-conditioning factor is shown to be computationallyfeasible. The application of this idea to the method of SimultaneousDisplacement, Richardson's method and other iterative methods,are discussed and numerical examples given to illustrate itseffectiveness.     

The Core-EP,Weighted Core-EP Inverse of Matrices and Constrained Systems of Linear Equations

We study the constrained systemof linear equations Ax=b,x∈R(A^k)for A∈C^n×nand b∈Cn,k=Ind(A).When the system is consistent,it is well known that it has a unique A^Db.If the system is inconsistent,then we seek for the least squares solution of the problem and consider min_x∈R(A^k)||b?Ax||2,where||·||2 is the 2-norm.For the inconsistent system with a matrix A of index one,it was proved recently that the solution is A^■b using the core inverse A^■of A.For matrices of an arbitrary index and an arbitrary b,we show that the solution of the constrained system can be expressed as A^■b where A^■is the core-EP inverse of A.We establish two Cramer’s rules for the inconsistent constrained least squares solution and develop several explicit expressions for the core-EP inverse of matrices of an arbitrary index.Using these expressions,two Cramer’s rules and one Gaussian elimination method for computing the core-EP inverse of matrices of an arbitrary index are proposed in this paper.We also consider the W-weighted core-EP inverse of a rectangular matrix and apply the weighted core-EP inverse to a more general constrained system of linear equations.     

线性方程组的分块矩阵解法

本文利用分块矩阵证明线性方程组解的基本定理，并给出解线性方程组的一种改进方法．     

Overdetermined Systems of Sparse Polynomial Equations

Foundations of Computational Mathematics - In this paper, we show that for a system of univariate polynomials given in sparse encoding we can compute a single polynomial defining the same zero set...     

State-Space Minimal Realizations and Latent Structures of Column-Reduced and Nonsingular Polynomial Matrices

The concept of a column-reduced polynomial matrix is an importantone in the theory of linear systems. The theory of Jordan chainsand minimal realizations is developed for such matrices. Also,the relationships between generalized latent vectors of thenonsingular polynomial matrix and their associated generalizedeigenvectors of the system map are explored in this paper. Thispermits the spectral analysis of an arbitrary nonsingular polynomialmatrix, extending previous work for the monic case.     

Factorized-Sparse-Approximate-Inverse Preconditionings of Linear Systems with Unsymmetric Matrices

The paper considers the application of factorized-sparse-approximate-inverse (FSAI) preconditionings to linear algebraic systems with nonsingular unsymmetric coefficient matrices. Special attention is paid to methods for optimizing the sparsity pattern of such preconditioners. It is numerically demonstrated that the efficiency of FSAI preconditionings can be substantially improved by applying the so-called postfiltering and prefiltering in order to sparsify preconditioning matrices. Bibliography: 20 titles.     

A Galerkin Approach for Solving Matrix Equations with Hierarchical Matrices

We consider a new approach for computing solutions of certain matrix equations, for example AXA = C, AX + XB = C or AX = I. This approach is based on a variational formulation in the matrix space, employing the Frobenius inner product. Using the space of ℋ²-matrices as trial space leads to a sparse linear system that can be solved by iterative methods to compute an approximate solution of the underlying matrix equation. (© 2013 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)