Linearly independent functions with identically zero Wronskian

In summary, the concept of linearly independent functions with identically zero Wronskian addresses a paradox in differential equations and linear algebra. Typically, a zero Wronskian indicates linear dependence among functions. However, under certain conditions, such as when the functions are defined over an interval where the Wronskian is identically zero, they can still be linearly independent. This situation often arises in the context of differential equations, where specific solutions may exhibit this behavior, challenging the conventional understanding of linear independence.
  • #1
zenterix
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Homework Statement
Consider the two functions ##f(x)=x^3## and ##g(x)=x^2|x|## on the interval ##[-1,1]##.

(a) Show that their Wronskian ##W(g,f)## vanishes identically.

(b) Show that ##f## and ##g## are not linearly dependent.

(c) Do (a) and (b) contradict Lemma 2? If not, why not?
Relevant Equations
Here is Lemma 2

If ##y_1(x)## and ##y_2(x)## are two solutions of the homogeneous differential equation

$$y''+P(x)y'+Q(x)y=R(x)\tag{1}$$

on ##[a,b]##, then they are linearly dependent on this interval ##\iff## their Wronskian is identically zero.

(By the way, I think it is implicit that ##P## and ##Q## are continuous on ##[a,b]##, right?)
My question will be about item (c).

Part (a)


Note that for ##x\geq 0## we have ##f(x)=g(x)##.

For ##x<0## we have ##f(x)=-g(x)##.

Since ##f## is a constant times ##g## then one column of the matrix in the Wronskian is a constant times the other column. Thus, the Wronskian is zero, Note that there are two separate calculations, one for ##x\geq 0## and one for ##x<0##, but in each case the justification for the Wronskian being zero is the same.

Part (b)

There is no linear combination of ##f## and ##g## that is identically zero on the entire ##[-1,1]##. There are two different linear combinations that are zero on each of ##[-1,0]## and ##[0,1]##.

Thus, ##f## and ##g## are linearly independent.

Part (c)

I've looked at the proof of Lemma 2 a few times to see where it is required that ##y_1## and ##y_2## be solutions to some homogeneous differential equation.

It doesn't seem (according to my guess) to be the case that if we just pick two functions then they will be solutions to some differential equation.

However, in any case, I did not see this assumption being explicitly used in the proof. (see next post)

That being said, if ##P(x)## and ##Q(x)## continuous on ##[-1,1]## then there should be exactly one solution with a given value and slope at a point.

The fact that ##f## and ##g## do have the same values and slopes at any point ##x\geq 0## means that they should be the same solution on the entire interval. But they aren't for ##x<0##.

Therefore, can I conclude that these two functions cannot both be solutions to some equation like (1)?

If I can conclude this then it seems one of the assumptions of Lemma 2 is not satisfied (even though I can't really see yet how this assumption is used in the proof of that Lemma) and so it need not be the case that the zero Wronskian means the functions are linearly independent.
 
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  • #2
Here is the proof of Lemma 2 in the textbook I am reading (Simmons - Differential Equations)

1708879715331.png


Now I can see the use of the assumption that ##y_1## and ##y_2## are solutions to a homogeneous 2nd order linear differential equation. It is in the proof of the converse of the biconditional.

We have a subinterval ##[c,d]## of ##[a,b]## and ##y_1## that is non-zero on this subinterval.

Because the Wronskian is zero on ##[a,b]## we conclude that ##y_1=ky_2## on ##[c,d]## for some constant ##k## and all ##x## in ##[c,d]##.

Since ##y_1## and ##ky_2## are the same on ##[c,d]## then they have equal values and derivatives on points in this subinterval.

And here is where we use the assumption that ##y_1## and ##y_2## are solutions to ##y''+P(x)y'+Q(x)y=0## with continuous ##P## and ##Q##.

Since ##y_1## and ##ky_2## have same value and slope at a point (actually on an entire subinterval) then they must be the same solution on the entire interval ##[a,b]##.
 
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FAQ: Linearly independent functions with identically zero Wronskian

What is the Wronskian of a set of functions?

The Wronskian of a set of functions is a determinant used in the study of differential equations to determine whether a set of solutions is linearly independent. For functions \( f_1, f_2, \ldots, f_n \), the Wronskian is defined as the determinant of the matrix whose \( i \)-th row consists of the \( (i-1) \)-th derivatives of these functions.

Can linearly independent functions have an identically zero Wronskian?

Yes, linearly independent functions can have an identically zero Wronskian, but this typically occurs under special conditions, such as when the functions are defined on an interval where they do not satisfy the conditions required for the Wronskian to be a definitive test of linear independence.

What are the conditions for the Wronskian to be zero for linearly independent functions?

The Wronskian can be zero for linearly independent functions in cases where the functions are analytic and the interval of consideration is not simply connected, or if the functions do not belong to the same differential equation. This phenomenon is often related to the specific properties of the functions and the domain on which they are defined.

What is an example of linearly independent functions with an identically zero Wronskian?

An example is the functions \( f(x) = x \) and \( g(x) = x|x| \) on the interval \([-1, 1]\). These functions are linearly independent, but their Wronskian is identically zero on this interval. This occurs because the functions involve absolute value terms that create a situation where the determinant of the Wronskian matrix is zero.

How does the zero Wronskian affect the solutions of differential equations?

In the context of differential equations, a zero Wronskian does not necessarily imply that the solutions are linearly dependent. It means that the standard test for linear independence using the Wronskian fails, and other methods must be used to determine the linear independence of the solutions. This situation often requires a deeper analysis of the specific properties of the solutions and the differential equation.

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