Numerical analysis | Fractals

Newton fractal

The Newton fractal is a boundary set in the complex plane which is characterized by Newton's method applied to a fixed polynomial p(Z) ∈ ℂ[Z] or transcendental function. It is the Julia set of the meromorphic function z ↦ z − p(z)/p′(z) which is given by Newton's method. When there are no attractive cycles (of order greater than 1), it divides the complex plane into regions Gk, each of which is associated with a root ζk of the polynomial, k = 1, …, deg(p). In this way the Newton fractal is similar to the Mandelbrot set, and like other fractals it exhibits an intricate appearance arising from a simple description. It is relevant to numerical analysis because it shows that (outside the region of quadratic convergence) the Newton method can be very sensitive to its choice of start point. Almost all points of the complex plane are associated with one of the deg(p) roots of a given polynomial in the following way: the point is used as starting value z0 for Newton's iteration zn + 1 := zn − p(zn)/p'(zn), yielding a sequence of points z1, z2, …, If the sequence converges to the root ζk, then z0 was an element of the region Gk. However, for every polynomial of degree at least 2 there are points for which the Newton iteration does not converge to any root: examples are the boundaries of the basins of attraction of the various roots. There are even polynomials for which open sets of starting points fail to converge to any root: a simple example is z3 − 2z + 2, where some points are attracted by the cycle 0, 1, 0, 1… rather than by a root. An open set for which the iterations converge towards a given root or cycle (that is not a fixed point), is a Fatou set for the iteration. The complementary set to the union of all these, is the Julia set. The Fatou sets have common boundary, namely the Julia set. Therefore, each point of the Julia set is a point of accumulation for each of the Fatou sets. It is this property that causes the fractal structure of the Julia set (when the degree of the polynomial is larger than 2). To plot images of the fractal, one may first choose a specified number d of complex points (ζ1, …, ζd) and compute the coefficients (p1, …, pd) of the polynomial . Then for a rectangular lattice of points in ℂ, one finds the index k(m,n) of the corresponding root ζk(m,n) and uses this to fill an M × N raster grid by assigning to each point (m,n) a color fk(m,n). Additionally or alternatively the colors may be dependent on the distance D(m,n), which is defined to be the first value D such that |zD − ζk(m,n)| < ε for some previously fixed small ε > 0. (Wikipedia).

Newton fractal
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The Newton Fractal Explained | Deep Dive Maths

A Newton fractal is obtained by iterating Newton's method to find the roots of a complex function. The iconic picture of this fractal is what I call The Newton Fractal, and is generated from the function f(z)=z^3-1, whose roots are the three cube roots of unity. What is the history of th

From playlist Deep Dive Maths

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Newton’s method, and the fractal it creates that Newton knew nothing about

Who knew root-finding could be so complicated? Next part: https://youtu.be/LqbZpur38nw Special thanks to the following supporters: https://3b1b.co/lessons/newtons-fractal#thanks An equally valuable form of support is to simply share the videos. ------------------ Interactive for this vid

From playlist Explainers

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Newton Fractals

Using Newton's Method to create Fractals by plotting convergence behavior on the complex plane. Functions used in this video include arctan(z), z^3-1, sin(z), z^8-15z^4+16. Example code and images available at https://github.com/osveliz/numerical-veliz Correction: The derivative of arctan

From playlist Root Finding

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Fractal Derivative

In this video, I define a neat concept called the fractal derivative (which shouldn't be confused with fractional derivatives). Then I provide a couple of examples, and finally I present an application of this concept to the study of anomalous diffusion in physics. Enjoy!

From playlist Calculus

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Fractals from Newton’s Method | Lecture 18 | Numerical Methods for Engineers

Determines the three complex cube roots of unity and discusses how to generate a Newton fractal using Newton's method of root finding. Join me on Coursera: https://www.coursera.org/learn/numerical-methods-engineers Lecture notes at http://www.math.ust.hk/~machas/numerical-methods-for-eng

From playlist Numerical Methods for Engineers

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Coding the Newton Fractal | Lecture 19 | Numerical Methods for Engineers

Demonstrates how to write a MATLAB code to generate a Newton fractal. Join me on Coursera: https://www.coursera.org/learn/numerical-methods-engineers Lecture notes at http://www.math.ust.hk/~machas/numerical-methods-for-engineers.pdf Subscribe to my channel: http://www.youtube.com/user/

From playlist Numerical Methods for Engineers

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Fractals are typically not self-similar

An explanation of fractal dimension. Help fund future projects: https://www.patreon.com/3blue1brown An equally valuable form of support is to simply share some of the videos. Special thanks to these supporters: https://3b1b.co/fractals-thanks And by Affirm: https://www.affirm.com/careers H

From playlist Explainers

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Global Newton's Method - It Always Converges

Globally convergent modification of Newton's Method that uses backtracking whenever a test point would not cause the function iterations to shrink in absolute value based on the Armijo's Search. Lesson also covers fractals using Global Newton Method as well as solving systems of nonlinear

From playlist Root Finding

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Sierpinski from Pascal

This is a recreation of a short clip from a long form video showing six different ways to construct the Sierpinski triangle: https://youtu.be/IZHiBJGcrqI In this short, we shade odd entries of the Halayuda/Pascal triangle to obtain the Sierpinski triangle. Can you explain why this works?

From playlist Fractals

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Newton's Method for Systems of Nonlinear Equations

Generalized Newton's method for systems of nonlinear equations. Lesson goes over numerically solving multivariable nonlinear equations step-by-step with visual examples and explanation of the Jacobian, the backslash operator, and the inverse Jacobian. Example code in MATLAB / GNU Octave on

From playlist Newton's Method

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Halley's Method

Halley's Method (the method of tangent hyperbolas) for finding roots including history, derivation, examples, and fractals. Also discusses Taylor's Theorem relating to Halley's Method as well as Halley's Comet. Sample code and images available on GitHub https://www.github.com/osveliz/numer

From playlist Root Finding

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Householder's Method

Householder's Method for finding roots of equations including history, derivation, examples, and fractals. Example code is available on GitHub https://github.com/osveliz/numerical-veliz Chapters 0:00 Intro 0:25 Derivation 1:58 History 2:34 Householder's Method 4:07 Householder's Method Ex

From playlist Root Finding

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Laguerre's Method

Laguerre's method for finding real and complex roots of polynomials. Includes history, derivation, examples, and discussion of the order of convergence as well as visualizations of convergence behavior. Example code available on github https://www.github.com/osveliz/numerical-veliz Chapte

From playlist Root Finding

Related pages

Complex plane | Polynomial | Numerical analysis | Julia set | Bounded set | Pseudocode | Transcendental function | Root of a function | Almost all | Newton's method | Mandelbrot set | Meromorphic function