4 edition of **Tensor-GMRES method for large sparse systems of nonlinear equations** found in the catalog.

Tensor-GMRES method for large sparse systems of nonlinear equations

- 108 Want to read
- 36 Currently reading

Published
**1994**
by Research Institute for Advanced Computer Science, NASA Ames Research Center, National Technical Information Service, distributor in [Moffett Field, Calif.], [Springfield, Va
.

Written in English

**Edition Notes**

Other titles | Tensor GMRES method for large sparse systems of nonlinear equations. |

Statement | Dan Feng and Thomas H. Pulliam. |

Series | [NASA contractor report] -- NASA CR-196379., RIACS technical report -- 94.12., NASA contractor report -- NASA CR-196379., RIACS technical report -- TR 94.12. |

Contributions | Pulliam, Thomas H., Research Institute for Advanced Computer Science (U.S.) |

The Physical Object | |
---|---|

Format | Microform |

Pagination | 1 v. |

ID Numbers | |

Open Library | OL16999825M |

OCLC/WorldCa | 31950094 |

Solution of matrix equations using sparse techniques. NASA Technical Reports Server (NTRS) Baddourah, Majdi. The solution of large systems of matrix equations is key t. E-LETTER on Systems, Control, and Signal Processing ISSUE No. , August 1, E-mail: [email protected] Editors: Anton A. Stoorvogel Dept. of Mathematics & Computing.

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This paper introduces a tensor-Krylov method, the tensor-GMRES method, for large sparse systems of nonlinear equations. This method is a coupling of tensor model forma-tion and solution techniques for nonlinear equations with Krylov subspace projection techniques for unsymmetric systems of linear equations.

Traditional tensor methods. This paper introduces a tensor-Krylov method, the tensor-GMRES method, for large sparse systems of nonlinear equations. This method is a coupling of tensor model formation and solution techniques.

Tensor-GMRES method for large sparse systems of nonlinear equations (SuDoc NAS ) [Dan Feng] on *FREE* shipping on qualifying : Dan Feng. Get this from a library.

Tensor-GMRES method for large sparse systems of nonlinear equations. [Dan Feng; Thomas H Pulliam; Research Institute for Advanced Computer Science (U.S.)]. Buy Tensor-GMRES method for large sparse systems of nonlinear equations (SuDoc NAS ) by Feng, Dan (ISBN:) from Amazon's Book Store.

Everyday low Author: Dan Feng. Tensor-GMRES method for large sparse systems of nonlinear equations [microform] / Dan Feng and Thomas H. Pulliam Research Institute for Advanced Computer Science, NASA Ames Research Center ; National Technical Information Service, distributor [Moffett Field, Calif.]: [Springfield, Va Australian/Harvard Citation.

Feng, Dan. A number of interesting linear algebraic implementation issues are addressed. The test results of the tensor method applied to a set of sparse nonlinear least squares problems compared with those of the standard Gauss--Newton method reveal that the tensor method is significantly more robust and efficient than the standard Gauss--Newton by: 8.

This paper introduces censor methods for solving, large sparse systems of nonlinear equations. Tensor methods for nonlinear equations were developed in the context of solving small to medium- sized dense problems.

They base each iteration on a quadratic model of the nonlinear equations. where the. This paper introduces tensor methods for solving large, sparse nonlinear least squares problems where the Jacobian either is analytically available or is computed by finite difference approximation Cited by: 8.

Tensor-GMRES method for large sparse systems of nonlinear equations eBook: National Aeronautics and Space Administration NASA: : Kindle StoreAuthor: National Aeronautics and Space Administration NASA.

Technical Report: Tensor methods for large, sparse unconstrained optimization. Tensor systems involving tensor-vector products (or polynomial systems) are considered.

We solve these tensor systems, especially focusing on symmetric $${\\mathcal {M}}$$ M -tensor systems, by some tensor methods.

A new tensor method is proposed based on the rank-1 approximation of the coefficient tensor. Numerical examples show that the tensor methods could be more efficient than the Cited by: Tensor-Krylov Methods for Solving Large-Scale Systems of Nonlinear Equations Article in SIAM Journal on Numerical Analysis 43(3) January with 54 Reads How we measure 'reads'.

it works exactly the same as it does with linear equations, you don't need to do any differentiation or anything fancy with this method, just have to plug in values of x, so it shouldn't make a. Consider the linear system of equations AX = Y where X is a n x 1 matrix of variables, Y is a n x 1 matrix of constants, and A is an n x n matrix of coefficients.

Provided A is not a singular. The paper begins with a review of the convergence theory for Newton’s method near simple and other regular singularities, followed by a brief discussion of the inherent difficulty of singular probl.

Several implementations of Newton-like iteration schemes based on Krylov subspace projection methods for solving nonlinear equations are considered. The simplest such class of methods is Newton’s a Cited by: Available in the National Library of Australia collection.

Author: Pulliam, H. Ronald; Format: Book; ix, p. ; 22 cm. SIAM J. on Applied Dynamical Systems. Browse SIADS; SIAM J.

on Applied Mathematics. Browse SIAP; SIAM J. on Computing. Browse SICOMP; SIAM J. on Control and Optimization. Browse SICON; SIAM J. on Discrete Mathematics. Browse SIDMA; SIAM J. on Financial Mathematics. Browse SIFIN; SIAM J. on Imaging Sciences. Browse SIIMS; SIAM J. on Mathematical Cited by: NA Digest Sunday, J Volume Issue 28 Today's Editor: Cleve Moler The MathWorks, Inc.

[email protected] Today's Topics. A classical algorithm for solving the system of nonlinear equations $F(x) = 0$ is Newton’s method \[ x_{k + 1} = x_k + s_k,\quad {\text{where }}F'(x_k)s_k = - F(x Cited by: Tensor-GMRES method for large sparse systems of nonlinear equations | NASA, National Aeronautics and Space Administration | ISBN: | Kostenloser Versand für alle Bücher mit Versand und Verkauf duch : Taschenbuch.Noté /5.

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