Newton's method as an iterative scheme to compute both unstable and stable fixed points of a discrete dynamical system is considered. It is shown for Newton iterations that the basins of attraction are intertwined in a complicated manner. This complex structure appears to be fractal, and its dimension is estimated. Consequences of predictability for the final state are given in terms of imprecision in the initial data. Keywords include: Newton's method, Predictability, Basin boundaries, Fractal, Nonlinear dynamic.

This volume is a collection of more than 7000 full titles of books and papers related to chaotic behaviour in nonlinear dynamics. Emphasis has been made on recent publications, but many publications which appeared before 1980 are also included. Many titles have been checked with the authors. The scope of the Bibliography is not restricted to physics and mathematics of chaos only. Applications of chaotic dynamics to other branches of natural and social sciences are also considered. Works related to chaotic dynamics, e.g., papers on turbulence dynamical systems theory and fractal geometry, are listed at the discretion of the author or the compiler. This Bibliography is expected to be an important reference book for libraries and individual researchers.

This book presents comprehensive state-of-the-art theoretical analysis of the fundamental Newtonian and Newtonian-related approaches to solving optimization and variational problems. A central focus is the relationship between the basic Newton scheme for a given problem and algorithms that also enjoy fast local convergence. The authors develop general perturbed Newtonian frameworks that preserve fast convergence and consider specific algorithms as particular cases within those frameworks, i.e., as perturbations of the associated basic Newton iterations. This approach yields a set of tools for the unified treatment of various algorithms, including some not of the Newton type per se. Among the new subjects addressed is the class of degenerate problems. In particular, the phenomenon of attraction of Newton iterates to critical Lagrange multipliers and its consequences as well as stabilized Newton methods for variational problems and stabilized sequential quadratic programming for optimization. This volume will be useful to researchers and graduate students in the fields of optimization and variational analysis.

Computational examples from nonlinear programming are given.

This book deals with the efficient numerical solution of challenging nonlinear problems in science and engineering, both in finite dimension (algebraic systems) and in infinite dimension (ordinary and partial differential equations). Its focus is on local and global Newton methods for direct problems or Gauss-Newton methods for inverse problems. The term 'affine invariance' means that the presented algorithms and their convergence analysis are invariant under one out of four subclasses of affine transformations of the problem to be solved. Compared to traditional textbooks, the distinguishing affine invariance approach leads to shorter theorems and proofs and permits the construction of fully adaptive algorithms. Lots of numerical illustrations, comparison tables, and exercises make the text useful in computational mathematics classes. At the same time, the book opens many directions for possible future research.

The intricately interwoven basins of attraction stemming from Newton's Method applied to a simple complex polynomial are a common sight in fractal, dynamical systems, and numerical analysis literature. In this work, the author investigates how this workhorse of root-finding algorithms works for complex polynomials, in addition to a variety of other settings, from the simple, one-dimensional real function with a simple root, to the infinite-dimension Banach space. The rapid, quadratic convergence of Newton's method to a simple root is well known, but this performance is not guaranteed for all roots and for all starting points. Damping is one modification to the Newton algorithm that can be used to overcome difficulties in global convergence. We explore computationally how this damping affects the fractal geometry of the Newton basins of attraction for a simple function.