Question about linear second order ODE solution coefficients

In summary, the author is discussing the fact that the C_1 and C_2 coefficients can be complex. They are not always defined, but are simply referred to as "arbitrary constants".
  • #1
meldraft
281
2
Hey all,

there is something that has always bugged me in linear second order ODEs. We say that the general solution is:

[tex]y=C_1e^{r_1x}+C_2e^{r_2x}[/tex]

where r_1 and r_2 are the solutions of the characteristic polynomial.

The cases where r1, r2 are real are pretty straightforward. If they are complex, however:

[tex]r_1=a+ib , r_2=a-ib[/tex]

after algebraic manipulation we come up with the following:

[tex]y=e^{ax}[(C_1+C_2)cos(bx)+i(C_1-C_2)sin(bx)]=e^{ax}[c_1 cos(bx)+c_2 sin(bx)][/tex]

We then say, that this is the general solution of the ODE, and if c_1, c_2 are real, the solutions are also real.

This would imply that the C_1 and C_2 coefficients are considered to be complex. However, I have not seen such an assumption in any book or description so far. In fact, they are sometimes not defined at all, simply referred to as "arbitrary constants". Does this assumption cover the possibility that they may be complex numbers? Am I missing something obvious?
 
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  • #2
Generally you look for real valued solutions. There is another way to come up with the general solution. It is straightforward to prove that if y=u+iv is a complex valued solution of this type of equation then both u and v are real valued solutions and they are linearly independent (try it). The two complex solutions immediately yield the same two real solutions and the general solution is a linear combination of u and v.
 
  • #3
Certainly, a similar thing happens with harmonic conjugates. However, unless I am mistaken, even though the solution is real, it is (or rather, it can be) the superposition of complex functions. Therefore, wouldn't it make sense to explicitly state that the C_1 and C_2 coefficients can be complex?
 
  • #4
I agree. From a strictly mathematical point of view, a complex solution is a solution. But if we are solving a classical physical boundary value problem then the solutions are real. I think that author's are sometimes sloppy in not properly motivating or explaining exactly what solutions we are interested in.

Sorry. To answer the original question, they can be complex as you assume.
 
  • #5
This clears things up! Thank you!
 

1. What is a linear second order ODE?

A linear second order ordinary differential equation (ODE) is a mathematical equation that involves a dependent variable, its first derivative, and its second derivative in a linear combination. It is written in the form of a0 y'' + a1 y' + a2 y = f(x), where y is the dependent variable, a0, a1, and a2 are constants, and f(x) is a function of the independent variable x.

2. What is the order of a linear second order ODE?

The order of an ODE refers to the highest derivative present in the equation. In the case of a linear second order ODE, the order is 2 because the equation contains the second derivative of the dependent variable.

3. How do you solve a linear second order ODE?

To solve a linear second order ODE, you need to find a general solution, which is a family of solutions that satisfies the equation. This can be done by using various techniques such as the method of undetermined coefficients, variation of parameters, or the Laplace transform.

4. What are the coefficients in a linear second order ODE solution?

The coefficients in a linear second order ODE solution refer to the constants a0, a1, and a2 in the general solution. These coefficients are determined by the initial conditions of the ODE, which are values of the dependent variable and its derivatives at a specific point.

5. What are the applications of linear second order ODEs?

Linear second order ODEs have various applications in fields such as physics, engineering, economics, and biology. They can be used to model various physical systems, such as oscillating systems, electrical circuits, and chemical reactions. They are also used in mathematical models for population growth, economic growth, and other phenomena.

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