relaxation_method.md

Relaxation Method

The relaxation method, commonly referred to as the fixed-point iteration method, is an iterative approach used to find solutions (roots) to nonlinear equations of the form $f(x) = 0$. Instead of directly solving for the root, the method involves rewriting the original equation in the form:

$$x = g(x),$$

where $g(x)$ is a suitable function defined such that a fixed point of $g(x)$ corresponds to a root of $f(x)$. That is, if $\alpha$ is a root of $f(x)$, then $g(\alpha) = \alpha$.

This approach transforms a root-finding problem into a fixed-point problem. If the iteration defined by:

$$x_{n+1} = g(x_n)$$

converges, it will do so to the fixed point $\alpha$, which is also the root of the original equation. Relaxation methods can sometimes converge faster than bracket-based methods (like bisection), but they do not come with guaranteed convergence from any starting point. Convergence depends on the properties of the chosen $g(x)$ and the initial guess $x_0$.

Conceptual Illustration:

Imagine the line $y = x$ and the curve $y = g(x)$. A fixed point $\alpha$ is a point where the two curves intersect:

The iteration takes a guess $x_n$, and then maps it to $x_{n+1}=g(x_n)$. If these iterations hone in on the intersection, the sequence converges to the root $\alpha$.

Mathematical Formulation

Starting Point:

Given a nonlinear equation:

$$f(x) = 0$$ we want to find $\alpha$ such that $f(\alpha)=0$.

Rewriting the Equation:

We rearrange the equation into a fixed-point form:

$$x = g(x)$$

There can be infinitely many ways to rewrite $f(x)=0$ as $x=g(x)$. A suitable $g(x)$ must be chosen to ensure convergence.

Once we have:

$$x_{n+1} = g(x_n)$$

if the iteration converges, it must converge to $\alpha$ satisfying $\alpha = g(\alpha)$.

Convergence Conditions:

A key sufficient condition for convergence is that:

$$|g'(x)| < 1 \quad \text{in the neighborhood of } \alpha.$$

If the derivative of $g(x)$ in the region around the fixed point is less than 1 in absolute value, the iteration will typically converge.

Derivation

I. From Root to Fixed Point Problem:

Suppose we have $f(x)=0$. We isolate $x$ on one side:

$$x = g(x)$$

If $\alpha$ solves $f(\alpha)=0$, then $g(\alpha)=\alpha$.

II. Fixed-Point Iteration:

Define a sequence ${x_n}$ by choosing an initial guess $x_0$ and applying:

$$x_{n+1} = g(x_n).$$

III. Convergence Insight:

Consider a point $\alpha$ such that $g(\alpha)=\alpha$. If we expand $g$ near $\alpha$:

$$g(\alpha + h) \approx g(\alpha) + g'(\alpha)h = \alpha + g'(\alpha)h$$

If $|g'(\alpha)|&lt;1$, then iterating this process will reduce the error $|h|$ at each step, leading to convergence of $x_n$ to $\alpha$.

In essence, the choice of $g(x)$ greatly influences the convergence behavior. A poorly chosen $g(x)$ may lead to divergence or very slow convergence.

Algorithm Steps

Input:

• A function $f(x)$ and a corresponding $g(x)$ such that $f(x)=0$ is equivalent to $x=g(x)$.
• An initial guess $x_0$.
• A tolerance $\epsilon &gt;0$ or a maximum number of iterations $n_{\max}$.

Initialization:

Set iteration counter $n=0$.

Iteration:

I. Compute the next approximation:

$$x_{n+1} = g(x_n)$$

II. Check convergence:

• If $|x_{n+1}-x_n|&lt; \epsilon$ or $|f(x_{n+1})|&lt; \epsilon$, stop.
• If $n&gt;n_{\max}$, stop.

III. Otherwise, set $n = n+1$ and repeat from step I.

Output:

• Approximate root: $x_{n+1}$.
• Number of iterations performed.

Example

Given Equation:

$$x^2 - 3x + 2 = 0$$

This equation factors as $(x-1)(x-2)=0$, so the roots are $x=1$ and $x=2$.

Choose a Fixed-Point Form:

We can rearrange the equation to:

$$x = \frac{x^2 + 2}{3}$$

Thus:

$$g(x) = \frac{x^2 + 2}{3}$$

Initial Guess:

Let $x_0=0$.

Iteration 1:

$$x_1 = g(x_0) = g(0) = \frac{0^2 + 2}{3} = \frac{2}{3}\approx0.6667$$

Iteration 2:

$$x_2 = g(x_1) = g(0.6667) = \frac{(0.6667)^2 + 2}{3} = \frac{0.4444 + 2}{3}\approx\frac{2.4444}{3}=0.8148$$

Iteration 3:

$$x_3 = g(x_2) = g(0.8148)=\frac{(0.8148)^2 +2}{3}=\frac{0.6639+2}{3}\approx\frac{2.6639}{3}=0.8880$$

Repeating this process, we observe $x_n$ approaching one of the roots (in this case, it will move closer to $x=1$, depending on the behavior of $g(x)$ near that root).

Advantages

1. Flexible formulation allows the relaxation method to work by simply rewriting the equation in fixed-point form $x = g(x)$ and iterating, making it conceptually straightforward.
2. Potentially faster than bisection, the method can converge more quickly when $g(x)$ is well-chosen, particularly if $|g'(x)| &lt; 1$ near the root.
3. Broad applicability makes the method suitable for nonlinear equations that do not require derivatives, although convergence often depends on the properties of $g(x)$.
4. Straightforward implementation is achieved by directly iterating $x_{n+1} = g(x_n)$, provided an appropriate $g(x)$ is identified.

Limitations

1. No guaranteed convergence means the method may fail if $|g'(x)| \geq 1$ in the vicinity of the root, unlike bracketing methods that ensure convergence under certain conditions.
2. Sensitive to the choice of $g(x)$, as some transformations of $f(x) = 0$ into $x = g(x)$ promote convergence while others lead to divergence.
3. Initial guess importance highlights that a poor starting point can result in divergence or very slow convergence, making the method less robust in such cases.
4. Dependent on continuity and differentiability, with standard convergence theory requiring $g(x)$ to be continuous and differentiable, limiting its applicability for problems that do not meet these conditions.