Conjugate residual methods for almost symmetric linear systems

This paper concerns the use of conjugate residual methods for the solution of nonsymmetric linear systems arising in applications to differential equations. We focus on an application derived from a seismic inverse problem. The linear system is a small perturbation to a symmetric positive-definite system, the nonsymmetries arising from discretization errors in the solution of certain boundary-value problems. We state and prove a new error bound for a class of generalized conjugate residual methods; we show that, in some cases, the perturbed symmetric problem can be solved with an error bound similar to the one for the conjugate residual method applied to the symmetric problem. We also discuss several applications for special distributions of eigenvalues.This work was supported in part by the National Science Foundation, Grants DMS-84-03148 and DCR-81-16779, and by the Office of Naval Research, Contract N00014-85-K-0725.     

Convergences of splitting iterative methods for symmetric indefinite linear systems

In this paper, we generalize the saddle point problem to general symmetric indefinite systems, we also present a kind of convergent splitting iterative methods for the symmetric indefinite systems. A special divergent splitting is introduced. The sufficient condition is discussed that the eigenvalues of the iteration matrix are real. The spectral radius of the iteration matrix is discussed in detail, the convergence theories of the splitting iterative methods for the symmetric indefinite systems are obtained. Finally, we present a preconditioner and discuss the eigenvalues of preconditioned matrix.     

Conjugate direction methods for solving systems of linear equations

A generalization of the notion of a set of directions conjugate to a matrix is shown to lead to a variety of finitely terminating iterations for solving systems of linear equations. The errors in the iterates are characterized in terms of projectors constructable from the conjugate directions. The natural relations of the algorithms to well known matrix decompositions are pointed out. Some of the algorithms can be used to solve linear least squares problems.This work was supported by the Office of Naval Research under contract number N 00014-67-A-0126.     

Real valued iterative methods for solving complex symmetric linear systems

Complex valued systems of equations with a matrix R + 1S where R and S are real valued arise in many applications. A preconditioned iterative solution method is presented when R and S are symmetric positive semi‐definite and at least one of R, S is positive definite. The condition number of the preconditioned matrix is bounded above by 2, so only very few iterations are required. Applications when solving matrix polynomial equation systems, linear systems of ordinary differential equations, and using time‐stepping integration schemes based on Padé approximation for parabolic and hyperbolic problems are also discussed. Numerical comparisons show that the proposed real valued method is much faster than the iterative complex symmetric QMR method. Copyright © 2000 John Wiley & Sons, Ltd.     

Conjugate gradient type methods for unsymmetric and inconsistent systems of linear equations

Conjugate gradient type methods are discussed for unsymmetric and inconsistent system of equations. For unsymmetric problems, besides conjugate gradient methods based on the normal equations, we also present a (modified) minimal residual (least square) method, which converges for systems with matrices that have a positive definite symmetric part. For inconsistent problems, for completeness we discuss briefly various (well-known) versions of the conjugate gradient method. Preconditioning and rate of convergence are also discussed.     

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.

     

A hybrid iterative method for symmetric indefinite linear systems

Hybrid iterative methods that combine a conjugate direction method with a simpler iteration scheme, such as Chebyshev or Richardson iteration, were first proposed in the 1950s. The ease with which Chebyshev and Richardson iteration can be implemented efficiently on a large variety of computer architectures has in recent years lead to renewed interest in iterative methods that use Chebyshev or Richardson iteration. This paper presents a new hybrid iterative method for the solution of linear systems of equations with a symmetric indefinite matrix. Our method combines the conjugate residual method with Richardson iteration. Special attention is paid to the determination of two real intervals, one on each side of the origin, that contain most of the eigenvalues of the matrix. These intervals are used to compute suitable iteration parameters for Richardson iteration. We also discuss when to switch between the methods. The hybrid scheme typically uses the Richardson method for most iterations, and this reduces the number of arithmetic vector operations significantly compared with the number of arithmetic vector operations required when only the conjugate residual method is used. Computed examples illustrate the competitiveness of the hybrid scheme.     

Conjugate gradient predictor corrector method for solving large scale problems

In this paper, we give a new method for solving large scale problems. The basic idea of this method depends on implementing the conjugate gradient as a corrector into a continuation method. We use the Euler method as a predictor. Adaptive steplength control is used during the tracing of the solution curve. We present some of our experimental examples to demonstrate the efficiency of the method.

     

Symmetric indefinite systems for interior point methods

We present a unified framework for solving linear and convex quadratic programs via interior point methods. At each iteration, this method solves an indefinite system whose matrix is instead of reducing to obtain the usualAD ² A ^T system. This methodology affords two advantages: (1) it avoids the fill created by explicitly forming the productAD ² A ^T whenA has dense columns; and (2) it can easily be used to solve nonseparable quadratic programs since it requires only thatD be symmetric. We also present a procedure for converting nonseparable quadratic programs to separable ones which yields computational savings when the matrix of quadratic coefficients is dense.     

Conjugate directions method for solving interval linear systems

We propose the interval version of the conjugate directions method, to solve the problem of linear systems, with symmetric and positive definite interval matrix A, and a right-hand side interval vector b. This revised version was published online in June 2006 with corrections to the Cover Date.     

A new block preconditioner for complex symmetric indefinite linear systems

Using the equivalent block two-by-two real linear systems and relaxing technique, we establish a new block preconditioner for a class of complex symmetric indefinite linear systems. The new preconditioner is much closer to the original block two-by-two coefficient matrix than the Hermitian and skew-Hermitian splitting (HSS) preconditioner. We analyze the spectral properties of the new preconditioned matrix, discuss the eigenvalue distribution and derive an upper bound for the degree of its minimal polynomial. Finally, some numerical examples are provided to show the effectiveness and robustness of our proposed preconditioner.     

Preconditioning and iterative solution of symmetric indefinite linear systems arising from interior point methods for linear programming

We propose to compute the search direction at each interior-point iteration for a linear program via a reduced augmented system that typically has a much smaller dimension than the original augmented system. This reduced system is potentially less susceptible to the ill-conditioning effect of the elements in the (1,1) block of the augmented matrix. A preconditioner is then designed by approximating the block structure of the inverse of the transformed matrix to further improve the spectral properties of the transformed system. The resulting preconditioned system is likely to become better conditioned toward the end of the interior-point algorithm. Capitalizing on the special spectral properties of the transformed matrix, we further proposed a two-phase iterative algorithm that starts by solving the normal equations with PCG in each IPM iteration, and then switches to solve the preconditioned reduced augmented system with symmetric quasi-minimal residual (SQMR) method when it is advantageous to do so. The experimental results have demonstrated that our proposed method is competitive with direct methods in solving large-scale LP problems and a set of highly degenerate LP problems. Research supported in parts by NUS Research Grant R146-000-076-112 and SMA IUP Research Grant.     

Conjugate gradient method for systems of nonlinear equations

One presents an iteration method for solving nonlinear algebraic systems, based on the ideas of the conjugate gradient method. One proves the convergence of the method and one obtains estimates for the rate of convergence.Translated from Zapiski Nauchnykh Seminarov Leningradskogo Otdeleniya Matematicheskogo Instituta im. V. A. Steklova AN SSSR, Vol. 70, pp. 178–183, 1977.     

信赖域子问题使用重新开始策略的共轭梯度法

给出了用共轭梯度法解信赖域子问题的重新开始策略，并证明了方法的收敛性，数值结果表明该策略可以大大提高算法的收敛速度．     

On block minimal residual methods

In the present work, we give some new results for block minimal residual methods when applied to multiple linear systems. Using the Schur complement, we develop new expressions for the approximation obtained, for the corresponding residual and for the Frobenius residual norm. These results could be used to derive new convergence properties for the block minimal residual methods.     

An improved block splitting preconditioner for complex symmetric indefinite linear systems

In this paper, an improved block splitting preconditioner for a class of complex symmetric indefinite linear systems is proposed. By adopting two iteration parameters and the relaxation technique, the new preconditioner not only remains the same computational cost with the block preconditioners but also is much closer to the original coefficient matrix. The theoretical analysis shows that the corresponding iteration method is convergent under suitable conditions and the preconditioned matrix can have well-clustered eigenvalues around (0,1) with a reasonable choice of the relaxation parameters. An estimate concerning the dimension of the Krylov subspace for the preconditioned matrix is also obtained. Finally, some numerical experiments are presented to illustrate the effectiveness of the presented preconditioner.     

Conjugate gradient type methods for the nondifferentiable convex minimization

We proposed implementable conjugate gradient type methods for solving a possibly nondifferentiable convex minimization problem by converting the original objective function to a once continuously differentiable function by way of the Moreau–Yosida regularization. The proposed methods make use of approximate function and gradient values of the Moreau–Yosida regularization instead of the corresponding exact values. The global convergence is established under mild conditions.