Iterative Methods without Inversion (e-bog) af Galperin, Anatoly
Galperin, Anatoly (forfatter)

Iterative Methods without Inversion e-bog

436,85 DKK (inkl. moms 546,06 DKK)
Iterative Methods without Inversion presents the iterative methods for solving operator equations f(x) = 0 in Banach and/or Hilbert spaces. It covers methods that do not require inversions of f (or solving linearized subproblems). The typical representatives of the class of methods discussed are Ulm's and Broyden's methods. Convergence analyses of the methods considered are based on Kantorovich...
E-bog 436,85 DKK
Forfattere Galperin, Anatoly (forfatter)
Udgivet 17 november 2016
Længde 230 sider
Genrer Mathematics
Sprog English
Format epub
Beskyttelse LCP
ISBN 9781315350745
Iterative Methods without Inversion presents the iterative methods for solving operator equations f(x) = 0 in Banach and/or Hilbert spaces. It covers methods that do not require inversions of f (or solving linearized subproblems). The typical representatives of the class of methods discussed are Ulm's and Broyden's methods. Convergence analyses of the methods considered are based on Kantorovich's majorization principle which avoids unnecessary simplifying assumptions like differentiability of the operator or solvability of the equation. These analyses are carried out under a more general assumption about degree of continuity of the operator than traditional Lipschitz continuity: regular continuity.Key FeaturesThe methods discussed are analyzed under the assumption of regular continuity of divided difference operator, which is more general and more flexible than the traditional Lipschitz continuity.An attention is given to criterions for comparison of merits of various methods and to the related concept of optimality of a method of certain class.Many publications on methods for solving nonlinear operator equations discuss methods that involve inversion of linearization of the operator, which task is highly problematic in infinite dimensions.Accessible for anyone with minimal exposure to nonlinear functional analysis.