Contents

**Definition of linearization formula**

When it comes to mathematics, the **linearization formula** is used to find a linear approximation to a function. The first-order Taylor expansion around the point of interest is a linear approximation of a function. This is a crucial approximation to many popular numerical techniques like Euler’s Method to approximate results to standard differential equations. The idea behind using linear approximations lies in the proximity of the tangent line to the graph of the function at a given point.

We can approximate intricate functions sometimes with the help of easier ones that provide the accuracy we are looking for in particular applications and are very easy to work with. Linearization is nothing but approximating the functions. They are completely based on tangents. We bring new variables dx and dy and explain them in such a way that gives a whole new meaning to the notation dy/dx. Dy is used in estimating the sensitivity to change and error in measurement.

## The** general formula for linearization**

The process of taking the gradient of a nonlinear function with all variables and constructing a linear representation at that given point. It is necessary for some types of analysis such as solution with a Laplace transform, stability analysis and to place the model into the form of linear state-space.

Linear approximation is a technique for estimating the value of a function, f(x), close to a point, x=a, with the help of the formula: y=f(a)+f’(a)(x-a). The formula which you can see is known as the linearization of f at x=a, but this formula is similar to the equation of the tangent line to f at x=a.y=f(a)+f’(a)(x-a).

This shows the way to find the linearization of a function and how to make use of it to create a linear approximation. This technique is often used in several fields of science, and it requires a little bit of knowledge of calculus.

**How does linearization work**

The idea of tangent planes to any multivariable function is generalized by local linearization. The main idea is to approximate a function closer to its inputs with a simple function that has a similar value at that input, and also the same values of partial derivative.

Allegedly, it looks clear that, in a plane, one and only one line can be tangent to a curve at a given point. But, in 3D space, several lines can be tangent. If these lines appear in the same plane, they decide that tangent plant at that particular point. Another way to think of a tangent plane is assuming the surface has no corners and is smooth. Then, there are no abrupt changes in slope in the tangent line to the surface in any direction because the direction changes without any interruption. So, a tangent plane touches the surface in tiny surroundings around that point.

**How to calculate the linearization formula**

Let us use how to find the linearization of a function f and a point x=a. If you know two things such as the slope of the line, m, and any single point that the line passes through (a,b), you can easily determine the equation of a line. We put these parts of information into the point-slope form, and this provides us with the equation y – b = m(x-a).

But, you will not be given any values for b or m in such problems. But instead, you will have to find them on your own. First of all, m=f’(a), because the slope is measured by the derivative, and second b=f(a), because y-values are measured by the original function.

In the graph, if y = x^2 and y=2x-1, the tangent to a curve y=f(x) stays close to the curve near the tangency point. The y-values along with the tangent line provide a decent approximation to the y-values on the curve for some time on either side. The more you zoom into the graph of a function closer to a point where the function is differentiable, the graph gets flatter and starts resembling its tangent.

The tangent line goes through the point (a, f(a)) in the graph of the function y=f(x). So, its equation for point sloe is y=f(a)+f ‘(a) (x-a). Therefore, the tangent is the graph of the function L function L(x) = f(a) + f ‘ (a) (x – a). L(x) gives a good approximation to f(x) as long as the line stays closer to the graph of f.

**Is the f formula worth it**

The f formula is worth it because it solves the equation. Linearization or linear approximation is a technique used to approximate the value of a function at a specific point. The reason it is worth it is that it can be hard to find the value of a function at a given point. Square roots are the best examples of this. Linearization is a process of finding a line’s equation that is the nearest estimate of a function for a value of x. Linearization is also called the tangent line approximation and helps in simplifying the formulas related to trigonometric functions.

## Linear Approximations and Their Application in Real Life

## Optics

Geometrical optics has a technique called Gaussian optics which explains the behavior of the light rays in optical systems by making use of paraxial approximation. In this technique, only the rays which make small angles with the optical axis of the system are taken into consideration. Trigonometric functions can be expressed as linear functions of the angles in this approximation. Gaussian optics works well with systems where all the optical surfaces are either flat or are parts of a sphere. A simple formula can be provided for the parameters of an imaging system like magnification, brightness, and focal distance in this case.

Linear approximation is useful in science. In science, it can be referred to as using a simple model or process when the actual model is not easy to use. Calculations can be made easier with the help of an approximate model or process. Approximations are also helpful if incomplete information is hindering the use of exact representations in some cases.