Results 1  10
of
2,015
QMR: a QuasiMinimal Residual Method for NonHermitian Linear Systems
, 1991
"... ... In this paper, we present a novel BCGlike approach, the quasiminimal residual (QMR) method, which overcomes the problems of BCG. An implementation of QMR based on a lookahead version of the nonsymmetric Lanczos algorithm is proposed. It is shown how BCG iterates can be recovered stably from t ..."
Abstract

Cited by 397 (26 self)
 Add to MetaCart
... In this paper, we present a novel BCGlike approach, the quasiminimal residual (QMR) method, which overcomes the problems of BCG. An implementation of QMR based on a lookahead version of the nonsymmetric Lanczos algorithm is proposed. It is shown how BCG iterates can be recovered stably from the QMR process. Some further properties of the QMR approach are given and an error bound is presented. Finally, numerical experiments are reported.
A Flexible InnerOuter Preconditioned GMRES Algorithm
, 1993
"... We present a variant of the GMRES lgorithm which l]ows changes in the prcconditioning at every step. There arc many possible applications o the new lgorithm some o which arc briefly discussed. In particular, a result o the flexibility o the new variant is that any iterative method can bc used as a p ..."
Abstract

Cited by 357 (31 self)
 Add to MetaCart
We present a variant of the GMRES lgorithm which l]ows changes in the prcconditioning at every step. There arc many possible applications o the new lgorithm some o which arc briefly discussed. In particular, a result o the flexibility o the new variant is that any iterative method can bc used as a prcconditioncr. For example, the standard GMRES lgorithm itself can bc used as a prcconditioncr, as can CGNR (or CGNE) the conjugate gradient method applied to the normal equations. However, the more appealing utilization o the method is in conjunction with relaxation techniques, possibly multilevel techniques. The possibility o changing prcconditioncrs may bc exploited to develop efficient iterative methods and to enhance robustness. A cw numcricM experiments arc reported to illustrate this act.
PETSc users manual
 ANL95/11  Revision 2.1.0, Argonne National Laboratory
, 2001
"... tract W31109Eng38. 2 This manual describes the use of PETSc for the numerical solution of partial differential equations and related problems on highperformance computers. The Portable, Extensible Toolkit for Scientific Computation (PETSc) is a suite of data structures and routines that provid ..."
Abstract

Cited by 278 (20 self)
 Add to MetaCart
(Show Context)
tract W31109Eng38. 2 This manual describes the use of PETSc for the numerical solution of partial differential equations and related problems on highperformance computers. The Portable, Extensible Toolkit for Scientific Computation (PETSc) is a suite of data structures and routines that provide the building blocks for the implementation of largescale application codes on parallel (and serial) computers. PETSc uses the MPI standard for all messagepassing communication. PETSc includes an expanding suite of parallel linear, nonlinear equation solvers and time integrators that may be used in application codes written in Fortran, C, and C++. PETSc provides many of the mechanisms needed within parallel application codes, such as parallel matrix and vector assembly routines. The library is organized hierarchically, enabling users to employ the level of abstraction that is most appropriate for a particular problem. By using techniques of objectoriented programming, PETSc provides enormous flexibility for users. PETSc is a sophisticated set of software tools; as such, for some users it initially has a much steeper
ARPACK Users Guide: Solution of Large Scale Eigenvalue Problems by Implicitly Restarted Arnoldi Methods.
, 1997
"... this document is intended to provide a cursory overview of the Implicitly Restarted Arnoldi/Lanczos Method that this software is based upon. The goal is to provide some understanding of the underlying algorithm, expected behavior, additional references, and capabilities as well as limitations of the ..."
Abstract

Cited by 215 (18 self)
 Add to MetaCart
(Show Context)
this document is intended to provide a cursory overview of the Implicitly Restarted Arnoldi/Lanczos Method that this software is based upon. The goal is to provide some understanding of the underlying algorithm, expected behavior, additional references, and capabilities as well as limitations of the software. 1.7 Dependence on LAPACK and BLAS
FastHenry: A MultipoleAccelerated 3D Inductance Extraction Program
, 1993
"... ... based on mesh analysis can be combined with a GMRESstyle iterative matrix solution technique to make a reasonably fast 3D frequency dependent inductance and resistance extraction algorithm. Unfortunately, both the computation time and memory re quired for that approach grow faster than n 2, w ..."
Abstract

Cited by 214 (48 self)
 Add to MetaCart
(Show Context)
... based on mesh analysis can be combined with a GMRESstyle iterative matrix solution technique to make a reasonably fast 3D frequency dependent inductance and resistance extraction algorithm. Unfortunately, both the computation time and memory re quired for that approach grow faster than n 2, where n is the number of volumefilaments. In this paper, we show that it is possible to use multipoleacceleration to reduce both required memory and computation time to nearly order n. Results from examples are given to demonstrate that the multipole acceleration can reduce required computation time and memory by more than an order of magnitude for realistic packaging problems.
Jacobianfree NewtonKrylov methods: a survey of approaches and applications
 J. Comput. Phys
"... Jacobianfree NewtonKrylov (JFNK) methods are synergistic combinations of Newtontype methods for superlinearly convergent solution of nonlinear equations and Krylov subspace methods for solving the Newton correction equations. The link between the two methods is the Jacobianvector product, which ..."
Abstract

Cited by 192 (6 self)
 Add to MetaCart
(Show Context)
Jacobianfree NewtonKrylov (JFNK) methods are synergistic combinations of Newtontype methods for superlinearly convergent solution of nonlinear equations and Krylov subspace methods for solving the Newton correction equations. The link between the two methods is the Jacobianvector product, which may be probed approximately without forming and storing the elements of the true Jacobian, through a variety of means. Various approximations to the Jacobian matrix may still be required for preconditioning the resulting Krylov iteration. As with Krylov methods for linear problems, successful application of the JFNK method to any given problem is dependent on adequate preconditioning. JFNK has potential for application throughout problems governed by nonlinear partial dierential equations and integrodierential equations. In this survey article we place JFNK in context with other nonlinear solution algorithms for both boundary value problems (BVPs) and initial value problems (IVPs). We provide an overview of the mechanics of JFNK and attempt to illustrate the wide variety of preconditioning options available. It is emphasized that JFNK can be wrapped (as an accelerator) around another nonlinear xed point method (interpreted as a preconditioning process, potentially with signicant code reuse). The aim of this article is not to trace fully the evolution of JFNK, nor to provide proofs of accuracy or optimal convergence for all of the constituent methods, but rather to present the reader with a perspective on how JFNK may be applicable to problems of physical interest and to provide sources of further practical information. A review paper solicited by the EditorinChief of the Journal of Computational
Preconditioning techniques for large linear systems: A survey
 J. COMPUT. PHYS
, 2002
"... This article surveys preconditioning techniques for the iterative solution of large linear systems, with a focus on algebraic methods suitable for general sparse matrices. Covered topics include progress in incomplete factorization methods, sparse approximate inverses, reorderings, parallelization i ..."
Abstract

Cited by 189 (5 self)
 Add to MetaCart
(Show Context)
This article surveys preconditioning techniques for the iterative solution of large linear systems, with a focus on algebraic methods suitable for general sparse matrices. Covered topics include progress in incomplete factorization methods, sparse approximate inverses, reorderings, parallelization issues, and block and multilevel extensions. Some of the challenges ahead are also discussed. An extensive bibliography completes the paper.
SUNDIALS: Suite of Nonlinear and Differential/ Algebraic Equation Solvers
 ACM Trans. Math. Software
, 2005
"... SUNDIALS is a suite of advanced computational codes for solving largescale problems that can be modeled as a system of nonlinear algebraic equations, or as initialvalue problems in ordinary differential or differentialalgebraic equations. The basic versions of these codes are called KINSOL, CVOD ..."
Abstract

Cited by 150 (3 self)
 Add to MetaCart
SUNDIALS is a suite of advanced computational codes for solving largescale problems that can be modeled as a system of nonlinear algebraic equations, or as initialvalue problems in ordinary differential or differentialalgebraic equations. The basic versions of these codes are called KINSOL, CVODE, and IDA, respectively. The codes are written in ANSI standard C and are suitable for either serial or parallel machine environments. Common and notable features of these codes include inexact NewtonKrylov methods for solving largescale nonlinear systems; linear multistep methods for timedependent problems; a highly modular structure to allow incorporation of different preconditioning and/or linear solver methods; and clear interfaces allowing for users to provide their own data structures underneath the solvers. We describe the current capabilities of the codes, along with some of the algorithms and heuristics used to achieve efficiency and robustness. We also describe how the codes stem from previous and widely used Fortran 77 solvers, and how the codes have been augmented with forward and adjoint methods for carrying out firstorder sensitivity analysis with respect to model parameters or initial conditions.
SuperLU DIST: A scalable distributedmemory sparse direct solver for unsymmetric linear systems
 ACM Trans. Mathematical Software
, 2003
"... We present the main algorithmic features in the software package SuperLU DIST, a distributedmemory sparse direct solver for large sets of linear equations. We give in detail our parallelization strategies, with a focus on scalability issues, and demonstrate the software’s parallel performance and sc ..."
Abstract

Cited by 144 (18 self)
 Add to MetaCart
(Show Context)
We present the main algorithmic features in the software package SuperLU DIST, a distributedmemory sparse direct solver for large sets of linear equations. We give in detail our parallelization strategies, with a focus on scalability issues, and demonstrate the software’s parallel performance and scalability on current machines. The solver is based on sparse Gaussian elimination, with an innovative static pivoting strategy proposed earlier by the authors. The main advantage of static pivoting over classical partial pivoting is that it permits a priori determination of data structures and communication patterns, which lets us exploit techniques used in parallel sparse Cholesky algorithms to better parallelize both LU decomposition and triangular solution on largescale distributed machines.