Is a nth order ODE considered linear if n=2?

  • Thread starter Saladsamurai
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In summary: Yes, that is correct. The right-hand side of an equation can be equal to 0 or a function of the independent variable.
  • #1
Saladsamurai
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I am reading through my Diff Eqs Text and I follow most of the lingo. However I am just a tad confused by the statement:

An nth order ODE is said to be linear if F is linear in y,y',...y^(n)

Then it gives the example:

[tex]a_n(x)\frac{d^ny}{dx^n}+a_{n-1}(x)..+a_0(x)y=g(x)[/tex]

It then says: 'On the left-hand side of the above equation the dependent variable y and all of its derivatives, y,y',y'',...y[itex]^n[/itex] are of the first degree.

Clearly I missed something in Calc. If n=2, I have: [tex]\frac{d^2y}{dx^2}[/tex]

Why is this linear if n=2?

Thanks,
Casey
 
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  • #2
You're confusing the *order* of the derivative with its *degree* in the equation.

y'' + y = 0

is a second-order linear equation. Second-order because the highest order derivative in the equation is a second dervative. Linear because the equation itself is linear in both y'' and y.

In contrast:

(y'')^2 + ay^3 = 0

is a second-order NON-linear D.E. Second-order for the same reason. NON-linear because it is QUADRATIC in y'' and CUBIC in y. I hope this clears things up.
 
Last edited:
  • #3
cepheid said:
You're confusing the *order* of the derivative with its *degree* in the equation.

y'' + y = 0

is a second-order linear equation. Second-order because the highest order derivative in the equation is a second dervative. Linear because the equation itself is linear in both y'' and y.

In contrast:

(y'')^2 + ay^3 = 0

is a second-order NON-linear D.E. Second-order for the same reason. NON-linear because it is QUADRATIC in y'' and CUBIC in y. I hope this clears things up.

I think it does. y' just means "the 1st derivative" and similarly for y" however if either one y' or y" or y for that matter were raised to any power above 1, the DE would no longer be linear.

Thanks!
 
  • #4
Yes, that is correct. Also note that other "non-linear" functions of the dependent variable, y or its derivatives, such as sin(y) or exp(y"), would make the equation non-linear.
 
  • #5
HallsofIvy said:
Yes, that is correct. Also note that other "non-linear" functions of the dependent variable, y or its derivatives, such as sin(y) or exp(y"), would make the equation non-linear.

Great, thanks Halls and cepheid. Hey also, I know that the right-hand side can be equal to 0 or a function of the independent variable; what about a constant?

Like [itex]\frac{d^2y}{dx^2}-\frac{dy}{dx}+6y=7[/itex] ?
 

What is linearity?

Linearity refers to the relationship between two variables where a change in one variable results in a proportional change in the other variable. In other words, the relationship between the two variables can be represented by a straight line on a graph.

Why is linearity important in science?

Linearity is important in science because it allows us to make predictions and draw conclusions based on mathematical relationships between variables. It also allows us to accurately measure and compare the effects of different variables on an outcome.

What are the assumptions of linearity?

The assumptions of linearity include a linear relationship between variables, constant variance of the data, and normally distributed residuals. Additionally, there should be no outliers or influential points that significantly affect the relationship between the variables.

What are some common methods for testing linearity?

There are several methods for testing linearity, including scatter plots, correlation coefficients, and linear regression analysis. These methods can help determine the strength and direction of the linear relationship between variables and identify any potential outliers or influential points.

What are some examples of non-linear relationships?

Non-linear relationships are common in science and can take many forms, such as exponential growth or decay, quadratic relationships, or logarithmic relationships. For example, the relationship between bacterial growth and time may follow an exponential pattern, rather than a linear one.

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