Solving systems of linear algebraic equations on array processors

Conclusion We have proposed a modification of the orthogonal Faddeev method [6] for solving various SLAE and also for inversion and pseudoinversion of matrices. The proposed version of the method relies on Householder and Jordan-Gauss methods and its computational complexity is approximately half that of [6]. This method, combined with the matrix-graph method [9] of formalized SPPC structure design, has been applied to synthesize a number of AP architectures that efficiently implement the proposed method. Goal-directed isomorphic and homeomorphic transformations of the LFG of the original algorithm (5) lead to a one-dimensional (linear) AP of fixed size, with minimum hardware and time costs and with minimized input-output channel width. The proposed algorithm (5) has been implemented using a 4-processor AP, with Motorola DSP96002 processors as PEs (Fig. 7). Application of the algorithm (5) to solve an SLAE with a coefficient matrixA withM=N=100 and one righthand side on this AP produced a load factor η=0.82; for inversion of the matrixA of the same size we achieved η=0.77. The sequence of transformations and the partitioning of a trapezoidal planaer LFG described in this article have been generalized to the case of other LA algorithms decribed by triangular planar LFGs and executed on linear APs. It is shown that the AP structures synthesized in this study execute all the above-listed algorithms no less efficiently than the modified Faddeev algorithm, provided their PEs are initially tuned to the execution of the corresponding operators. Translated from Kibernetika i Sistemnyi Analiz, No. 2, pp. 47–66, March–April, 1996.     

An iterative method for solving partitioned linear equations

A new iterative scheme, using two partitions of the coefficient matrix of a given linear and non-singular system of equationsAx=b, is shown to always converge to the solution. The concept of two vector spaces approaching orthogonality is quantified and used to show that the eigenvalues of the iteration matrix approach zero as the vector spaces defined by the two partitions ofA approach orthogonality.     

带状线性方程组的并行交替方向算法

提出了分布式存储环境下求解带状线性方程组的并行交替方向迭代算法。充分利用系数矩阵的结构特点,给出了在系数矩阵分别为Hermite正定矩阵和M-矩阵时算法的充分条件,并针对采用的分裂方式,讨论了参数的收敛范围,最后在HPrx2600集群系统上进行了数值计算,结果表明实算与理论相一致,算法简便可行且具有良好的并行性。     

Systolic networks for iterative methods for linear equations with cyclic reduction II

This paper presents systolic networks for the application of cyclic reduction to iterative methods for the solution of a linear system of equations A x=b where A is a p-cyclic matrix derived from multi-colouring ordered difference schemes on a regular mesh.     

Finite iterative algorithm for solving coupled Lyapunov equations appearing in continuous-time Markov jump linear systems

An iterative algorithm for solving coupled algebraic Lyapunov equations appearing in continuous-time linear systems with Markovian transitions is established. The algorithm is computationally efficient since it can obtain the solution within finite steps in the absence of round-off errors.     

Explicit isomorphic iterative methods for solving arrow-type linear systems

A new class of approximate inverse arrow-type matrix techniques based on the concept of sparse approximate LU-type factorization procedures is introduced for computing explicitly approximate inverses without inverting the decomposition factors. Isomorphic methods in conjunction with explicit preconditioned schemes based on approximate inverse matrix techniques are presented for the efficient solution of arrow-type linear systems. Applications of the proposed method on linear systems is discussed and numerical results are given     

Systolic array designs for matrix iterative processes

In this paper, systolic designs for the 2nd order Richardson/Chebyshev iterative methods for solving matrix linear systems are proposed.     

Gradient-based maximal convergence rate iterative method for solving linear matrix equations

This paper is concerned with numerical solutions to general linear matrix equations including the well-known Lyapunov matrix equation and Sylvester matrix equation as special cases. Gradient based iterative algorithm is proposed to approximate the exact solution. A necessary and sufficient condition guaranteeing the convergence of the algorithm is presented. A sufficient condition that is easy to compute is also given. The optimal convergence factor such that the convergence rate of the algorithm is maximized is established. The proposed approach not only gives a complete understanding on gradient based iterative algorithm for solving linear matrix equations, but can also be served as a bridge between linear system theory and numerical computing. Numerical example shows the effectiveness of the proposed approach.     

On the convergence rate of an iterative method for solving nonsymmetric algebraic Riccati equations

This paper is devoted to the convergence analysis of an iterative method for solving a nonsymmetric algebraic Riccati equation arising in transport theory. We give the convergence rate, and show that the iterative method converges linearly in one case and sublinearly in the other case.     

Quantitative criterion of conditioning for systems of linear algebraic equations

Conventional conditioning criteria are shown to be invalid for estimating the accuracy of a numerical calculation for systems of linear algebraic equations. A new conditioning criterion is proposed, quantitatively describing the actual loss of decimal digits in calculations. The adequacy of this criterion is confirmed by examples of numerical calculations. Conditioning criteria have been obtained for some types of linear systems arising in important applied problems, in particular, in the difference solution of differential or integral equations.     

Parallel p-adic method for solving linear systems of equations

We present a parallel algorithm for an exact solution of an integer linear system of equations using the single modulus p-adic expansion technique. More specifically, we parallelize an algorithm of Dixon, and present our implementation results on a distributed-memory multiprocessor. The parallel algorithm presented here can be used together with the multiple moduli algorithms and parallel Chinese remainder algorithms for fast computation of the exact solution of a system of linear equations with integer entries.     

Parallel algorithms for algebraic Riccati equations

The matrix sign function is the basis of a parallel algorithm for solving the generalized algebraic Riccati equation. Three forms of the algorithm were implemented and tested on a distributed memory hypercube multiprocessor. Performance results indicate that the method is an excellent means of solving large-scale problems on a parallel computer.     

Symplectic factorizations and parallel iterative algorithms for tridiagonal systems of equations

Parallel iterative algorithms for solving tridiagonal systems of equations are derived from the symplectic factorization of the odd-even permuted matrix of coefficients. These algorithms have halved parallel computational costs with respect to Accelerated Parallel Gauss, under weaker conditions for convergence.