Recognizing linear ordinary differential equations

In summary, the conversation discusses whether the given equation is a linear ordinary differential equation and the reasoning behind it. The conclusion is that the equation is not linear due to the unknown function R.
  • #1
fishingspree2
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Homework Statement



Is the following equation a linear ODE?
[itex]\frac{d^{2}R}{dt^{2}}=-\frac{k}{R^{2}}[/itex] where k is a constant

Homework Equations


A linear ordinary differential equation can be written in the following form:
[itex]a_{n}\left ( x \right )\frac{{d}^{n}y}{{d}x^{n}}+a_{n-1}\left ( x \right )\frac{{d}^{n-1}y}{{d}x^{n-1}}+...+a_{1}\left ( x \right )\frac{{d}y}{{d}x}+a_{0}\left ( x \right )y=g\left ( x \right )[/itex]

The Attempt at a Solution


Well, the correct answer in the textbook is: not a linear ODE. But there is something I don't really understand. If we multiply both sides by [itex]R^{2}[/itex] then we have:
[itex]R^{2}\frac{d^{2}R}{dt^{2}}=-k[/itex]

The right hand side is of the form [itex]g\left ( x \right )=-k[/itex], so this is good. Also, the [itex]\frac{{d}^{2}R}{{d}t^{2}}[/itex] term is power of 1, which is also good. The problem relies in the [itex]a_{n}\left ( x \right )[/itex] term. The coefficient in front of the derivative must at most depend on the independant variable, in this case, t.

We have [itex]R^{2}[/itex]. Well, in my opinion, we can't know whether it is linear or not because we don't know how R is explicitely defined. R could be a function of t in the following form: [itex]R=F\left ( t \right )[/itex]. If this is the case, then [itex]R^{2}[/itex] could be substitued by [itex]\left (F\left ( t \right ) \right )^{2}[/itex]. Then we would clearly see that [itex]R^{2}[/itex] depend only on the variable t and we could conclude that the given equation is linear.

Given my arguments, I don't understand why we can already tell that the equation is non-linear. Thank you very much!
 
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  • #2
Well, even if [itex]R=F(t)[/itex], that would not make it linear. The problem is that F is an unknown function. That is: we don't know (a priori) the exact form of F.

The idea is that [itex]\alpha_i[/itex] are known functions that depend on t. But R is not a known function. Thus it is a non-linear equation.
 

FAQ: Recognizing linear ordinary differential equations

What is a linear ordinary differential equation (ODE)?

A linear ordinary differential equation is a mathematical equation that involves an unknown function and its derivatives. It is considered linear if the unknown function and its derivatives appear only in the first power and are not multiplied together. For example, y'' + 2xy' + 3y = 0 is a linear ODE, while y'' + sin(y') = 0 is not linear.

How do you recognize a linear ODE?

A linear ODE can be recognized by the fact that the unknown function and its derivatives appear in the equation only in the first power and are not multiplied together. Additionally, the coefficients of the unknown function and its derivatives should not depend on the function itself or its derivatives. For example, in the equation y'' + xy' + y = 0, x and 1 are constant coefficients.

What is the general form of a linear ODE?

The general form of a linear ODE is y(n) + an-1(x)y(n-1) + ... + a1(x)y' + a0(x)y = f(x), where y(n) is the nth derivative of the unknown function y, an-1(x) to a0(x) are coefficients that may depend on x, and f(x) is a function of x.

What are the steps for solving a linear ODE?

The general steps for solving a linear ODE are as follows:

  1. Identify the highest order derivative and its coefficient.
  2. Rewrite the equation in standard form, with the highest order derivative on the left and all other terms on the right.
  3. Find the general solution by solving the characteristic equation, which is obtained by setting the coefficients of the derivatives equal to zero.
  4. Apply initial or boundary conditions to find the particular solution.

What are some applications of linear ODEs?

Linear ODEs have many applications in various fields of science and engineering. Some common examples include modeling population growth, predicting the spread of diseases, analyzing chemical reactions, and studying the behavior of electrical circuits. They are also used in fields such as economics, physics, and biology to describe various phenomena and make predictions.

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