An inexact Newton algorithm for large sparse equality constrained non-linear programming problems is proposed. This algorithm is based on an indefinitely preconditioned smoothed conjugate gradient method applied to the linear KKT system and uses a simple augmented Lagrangian merit function for Armij
Krylov subspace accelerated inexact Newton method for linear and nonlinear equations
β Scribed by Robert J. Harrison
- Publisher
- John Wiley and Sons
- Year
- 2003
- Tongue
- English
- Weight
- 88 KB
- Volume
- 25
- Category
- Article
- ISSN
- 0192-8651
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β¦ Synopsis
Abstract
A Krylov subspace accelerated inexact Newton (KAIN) method for solving linear and nonlinear equations is described, and its relationship to the popular direct inversion in the iterative subspace method [DIIS; Pulay, P., Chem Phys Lett 1980, 393, 73] is analyzed. The two methods are compared with application to simple test equations and the location of the minimum energy crossing point of potential energy surfaces. KAIN is no more complicated to implement than DIIS, but can accommodate a wider variety of preconditioning and performs substantially better with poor preconditioning. With perfect preconditioning, KAIN is shown to be very similar to DIIS. For these reasons, KAIN is recommended as a replacement for DIIS. Β© 2003 Wiley Periodicals, Inc. J Comput Chem 25: 328β334, 2004
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Building on the method of Kantorovich majorants, we give convergence results and error estimates for the two-step Newton method for the approximate solution of a nonlinear operator equation.