How to Linearize a Function without Using Logarithms

In summary, to linearize the model y=\alpha*x*e^{\beta*x}, you can take the natural logarithm of both sides to get ln(y) = ln(alpha) + ln(x) + beta*x. Then you can use linear least squares fit to extract the values of ln(alpha) and beta as the intercept and slope, respectively, and obtain a linear model in the form of y = mx + b.
  • #1
mrwest09
4
0

Homework Statement


Linearize the following model:
y=[tex]\alpha*x*e^{\beta*x}[/tex]

Homework Equations


The only relevant equations I can think of are the laws of natural logarithms.

The Attempt at a Solution


I have tried to taking the ln of both sides however that leaves me with an equation that has two term with x in it.

ln(y)=ln(a)+ln(x)+Bx

I'm sure there has to be a simple solution but I can't visualize anything without running into the same problems.
 
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  • #2
mrwest09 said:

Homework Statement


Linearize the following model:
y=[tex]\alpha*x*e^{\beta*x}[/tex]


Homework Equations


The only relevant equations I can think of are the laws of natural logarithms.


The Attempt at a Solution


I have tried to taking the ln of both sides however that leaves me with an equation that has two term with x in it.

ln(y)=ln(a)+ln(x)+Bx

I'm sure there has to be a simple solution but I can't visualize anything without running into the same problems.
It's possible that you're supposed to do this using a Maclaurin series representation for your function, and discard the x2 and higher degree terms.

The Maclaurin series for e[itex]\beta[/itex]x is
[tex]e^{\beta x} = 1 + \frac{\beta x}{1!} + \frac{(\beta x)^2}{2!} + ... + \frac{(\beta x)^n}{n!} + ...[/tex]

Multiply the terms in this series by [itex]\alpha[/itex]x and then discard all terms in x2 or higher.

EDIT:
On second thought, there's a simpler formula that is related to the above.

If x is "close to" 0, then f(x) [itex]\approx[/itex] f(0) + x*f'(0). This gives you a first degree polynomial approximation to your function.
 
Last edited:
  • #3
mrwest09 said:

Homework Statement


Linearize the following model:
y=[tex]\alpha*x*e^{\beta*x}[/tex]


Homework Equations


The only relevant equations I can think of are the laws of natural logarithms.

Correct:

[tex]
\ln{y} = \ln{\alpha} + \ln{x} + \beta \, x
[/tex]

So, [itex]\ln{y} - ln{x}[/itex] is a linear model relative to the function [itex]x[/itex] and you can use linear least squares fit to extract the value of the coefficients [itex]\ln{\alpha}[/itex] (the intercept) and [itex]\beta[/itex] (the slope).
 
  • #4
Okay that makes some sense but if you were to fit that into your standard y=mx+b format for linear lines wouldn't your 'y' value depend on two variables? In this case wouldn't it not be linear?
This is how I am picturing the final equation:

[tex]

\ln{y} - \ln{x} = \beta \, x + \ln{\alpha}

[/tex]

y = m x + b
 
Last edited:
  • #5
Yes. In:

[tex]
\tilde{y} = m \tilde{x} + b
[/tex]

we need to calculate:

[tex]
\tilde{y} = \ln{y} - \ln{x}
[/tex]

[tex]
\tilde{x} = x
[/tex]

and then:

[tex]
m = \beta, \; b = \ln{\alpha}
[/tex]
 

1. What is linearization of a function?

Linearization of a function is the process of approximating a nonlinear function with a linear function in a small interval around a specific point. This is done by finding the tangent line of the function at that point.

2. Why is linearization of a function useful?

Linearization is useful because it simplifies complex functions and makes them easier to analyze and understand. It also allows for easier calculation of values and estimation of the function in a specific interval.

3. How is linearization of a function different from linear regression?

Linearization of a function is a mathematical process used to approximate a nonlinear function with a linear function, while linear regression is a statistical method used to find the best linear fit for a set of data points. In other words, linearization is a mathematical technique, while linear regression is a statistical technique.

4. What are the applications of linearization of a function?

Linearization of a function is commonly used in physics, engineering, and economics to approximate complex nonlinear relationships and make them easier to analyze. It is also used in numerical methods for solving differential equations, optimization problems, and curve fitting.

5. Can any function be linearized?

No, not all functions can be linearized. Functions that are already linear or have a constant rate of change cannot be linearized. Additionally, some complex functions may not have a linear approximation that is accurate enough for practical use.

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