Lecture

Fixed Point Theorem: Convergence of Newton's Method

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Description

This lecture discusses the fixed point theorem and the convergence of Newton's method. The instructor begins by reviewing the iteration function and its derivation from the starting function. They illustrate the method of Picard for finding fixed points and emphasize the importance of choosing an appropriate function for convergence. The lecture includes graphical representations of functions and their roots, demonstrating the convergence behavior of the method. The instructor explains the conditions under which the method converges, particularly focusing on the derivative's behavior. They introduce the concept of contraction mappings and how they relate to fixed points. The lecture culminates in a discussion of Newton's method as a specific case of fixed point iteration, highlighting its convergence properties and the significance of the derivative being less than one in absolute value. The instructor concludes by outlining future topics, including error analysis and additional computational tools for further exploration of these concepts.

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Official source

Ontological neighbourhood

Mathematics

Analysis: Numerical analysis, Differential anaysis

Related concepts (32)

Iterated function system

In mathematics, iterated function systems (IFSs) are a method of constructing fractals; the resulting fractals are often self-similar. IFS fractals are more related to set theory than fractal geometry. They were introduced in 1981. IFS fractals, as they are normally called, can be of any number of dimensions, but are commonly computed and drawn in 2D. The fractal is made up of the union of several copies of itself, each copy being transformed by a function (hence "function system").

Iterated function

In mathematics, an iterated function is a function X → X (that is, a function from some set X to itself) which is obtained by composing another function f : X → X with itself a certain number of times. The process of repeatedly applying the same function is called iteration. In this process, starting from some initial object, the result of applying a given function is fed again in the function as input, and this process is repeated. For example on the image on the right: with the circle‐shaped symbol of function composition.

Fixed-point iteration

In numerical analysis, fixed-point iteration is a method of computing fixed points of a function. More specifically, given a function defined on the real numbers with real values and given a point in the domain of , the fixed-point iteration is which gives rise to the sequence of iterated function applications which is hoped to converge to a point . If is continuous, then one can prove that the obtained is a fixed point of , i.e., More generally, the function can be defined on any metric space with values in that same space.

Iterative method

In computational mathematics, an iterative method is a mathematical procedure that uses an initial value to generate a sequence of improving approximate solutions for a class of problems, in which the n-th approximation is derived from the previous ones. A specific implementation with termination criteria for a given iterative method like gradient descent, hill climbing, Newton's method, or quasi-Newton methods like BFGS, is an algorithm of the iterative method.

Fixed-point theorem

In mathematics, a fixed-point theorem is a result saying that a function F will have at least one fixed point (a point x for which F(x) = x), under some conditions on F that can be stated in general terms. The Banach fixed-point theorem (1922) gives a general criterion guaranteeing that, if it is satisfied, the procedure of iterating a function yields a fixed point.

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