Numerical mathematics, Newton's method

In summary, the goal of this task is to check understanding of the derivation of Newton's method in the lecture. This involves considering smooth functions G and F defined from \mathbb{R}^N to \mathbb{R}^N, and finding a matrix H such that if r is a root of F, then r is also a fixed point of G. The Jacobian of G at the point r must also be equal to 0 for this to hold, and the natural choice for H is -[F'(r)]^{-1}. Additionally, the use of Richardson extrapolation on the formula \phi(h)=\frac{f(x_0 +h) - f(x_0)}{h} can result
  • #1
Wingeer
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Homework Statement


"The goal of this task is to check that you understood the derivation of Newton’s method in the lecture.
1. Consider a smooth function G defined from [tex]\mathbb{R}^N[/tex] to [tex]\mathbb{R}^N[/tex]. Suppose it admits a fixed point [tex]r \in \mathbb{R}^N[/tex]. Write down the Taylor development of this function at the point r.
2. Consider now a function F also defined from [tex]\mathbb{R}^N[/tex] to [tex]\mathbb{R}^N[/tex]. Define a function G as:
[tex]G(x) = x + H(x)\cdot F(x)[/tex] (1)
where [tex]H(x)[/tex] is now a matrix depending on x, and [tex]\cdot[/tex] denotes the matrix vector multiplication. Check that if r is a root of F (i.e., F(r) = 0), then r is also a fixed point of G.
3. G'(r) denotes the Jacobian of G at the point r. Suppose that r is a root of F, and that G takes the form (1). Write down the condition G'(r) = 0 and deduce a formula for H(r).
4. What is a natural choice of the matrix H that leads to the vector Newton method?"

Homework Equations




The Attempt at a Solution


I was not attending the lecture, so I am not sure how this was derivated.
The Taylor series is: [tex]G(x_1, x_2, \cdots , x_n) \equiv G(\mathbf{x}) = G(r) + \sum_{j=1}^{n} r_j \frac{\partial G(r)}{\partial x_j} + O(r^2)[/tex]
b) Trivial by insertion. I do however not see the relationship between the function given in b, and the one in a?
c) I suppose [tex]G'(r)=0=1 + H'(r)F(r) + H(r)F'(r) = 1 + H(r)F'(r)[/tex] from which:
[tex]H(r)=-[F'(r)]^{-1}[/tex].
d) I am really not sure. I cannot see the whole picture here. And what's the point of the fixed point?

Thanks in advance.
 
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  • #2
Partly as a bump, an partly another question related to numerical mathematics I post in my own thread.
"Consider the formula [tex] \phi(h)=\frac{f(x_0 +h) - f(x_0)}{h}[/tex] Use Richardson extrapolation on the points h and h/2 in order to find a new formula. What should be its new order?"
What is meant by the order? And how do I use Richardson extrapolation? Am I not dependant on some error terms to use the method?
 

FAQ: Numerical mathematics, Newton's method

1. What is numerical mathematics?

Numerical mathematics is a branch of mathematics that deals with the development and implementation of numerical algorithms and methods to solve mathematical problems. It involves using computational techniques to approximate solutions to mathematical equations.

2. What is Newton's method?

Newton's method is an iterative algorithm used to find the roots of a function. It involves using the tangent line at a current guess to approximate the root, and then repeating the process until a satisfactory approximation is achieved.

3. How does Newton's method work?

Newton's method starts with an initial guess for the root of a function. It then uses the tangent line at that point to find a better estimate for the root. This process is repeated until the root is approximated with the desired level of accuracy.

4. What are the advantages of using Newton's method?

Newton's method is fast and efficient, as it can converge to the root of a function in a relatively small number of iterations. It also allows for the estimation of multiple roots of a function at once.

5. What are the limitations of Newton's method?

Newton's method can fail to converge if the initial guess is not close enough to the root, or if the function has a discontinuity or singularity near the root. It also requires the calculation of the derivative of the function, which can be challenging for some functions.

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