eBook Computational Methods In Nonlinear Analysis: Efficient Algorithms, Fixed Point Theory And Applications, 1st Edition

  • Published By: World Scientific Publishing Company
  • ISBN-10: 9814405833
  • ISBN-13: 9789814405836
  • DDC: 515.355
  • Grade Level Range: College Freshman - College Senior
  • 592 Pages | eBook
  • Original Copyright 2013 | Published/Released January 2015
  • This publication's content originally published in print form: 2013
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The field of computational sciences has seen a considerable development in mathematics, engineering sciences, and economic equilibrium theory. Researchers in this field are faced with the problem of solving a variety of equations or variational inequalities. We note that in computational sciences, the practice of numerical analysis for finding such solutions is essentially connected to variants of Newtons method. The efficient computational methods for finding the solutions of fixed point problems, nonlinear equations and variational inclusions are the first goal of the present book. The second goal is the applications of these methods in nonlinear problems and the connection with fixed point theory.This book is intended for researchers in computational sciences, and as a reference book for an advanced computational methods in nonlinear analysis. We collect the recent results on the convergence analysis of numerical algorithms in both finite-dimensional and infinite-dimensional spaces, and present several applications and connections with fixed point theory. The book contains abundant and updated bibliography, and provides comparison between various investigations made in recent years in the field of computational nonlinear analysis.

Table of Contents

Front Cover.
Half Title Page.
Title Page.
Copyright Page.
1: Newton's Methods.
2: Special Conditions for Newton's Method.
3: Newton's Method on Special Spaces.
4: Secant Method.
5: Gauss–Newton Method.
6: Halley's Method.
7: Chebyshev's Method.
8: Broyden's Method.
9: Newton-Like Methods.
10: Newton–Tikhonov Method for Ill-Posed Problems.