Are these functions necessarily linearly independent?

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In summary, linearly independent functions are a set of functions that cannot be expressed as a linear combination of each other. Determining if functions are linearly independent is important because it helps us understand the relationship between the functions and how they can be used in mathematical models. One way to test if functions are linearly independent is to use the Wronskian determinant. No, two linearly dependent functions cannot be used to describe a system because they are essentially the same function and do not provide any additional information. Not all linearly independent functions are orthogonal, as orthogonality only applies to vector spaces.
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Ackbach
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Here is this week's POTW:

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Let $x_1, x_2,\dots,x_n$ be differentiable (real-valued) functions of a single variable $t$ which satisfy
\begin{align*}
\d{x_1}{t}&=a_{11}x_1+a_{12}x_2+\cdots+a_{1n}x_n \\
\d{x_2}{t}&=a_{21}x_1+a_{22}x_2+\cdots+a_{2n}x_n \\
\vdots \\
\d{x_n}{t}&=a_{n1}x_1+a_{n2}x_2+\cdots+a_{nn}x_n
\end{align*}
for some constants $a_{ij}>0$. Suppose that for all $i, \; x_i(t)\to 0$ as $t\to\infty$. Are the functions $x_1,x_2,\dots,x_n$ necessarily linearly independent?

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Re: Problem Of The Week # 216 - May 17, 2016

This was Problem A-5 in the 1995 William Lowell Putnam Mathematical Competition.

No one answered this week's POTW. The solution, attributed to Kiran Kedlaya and his associates, follows:

It is known that the set of solutions of a system of
linear first-order differential equations with constant coefficients
is $n$-dimensional, with basis vectors of the form $f_{i}(t)
\vec{v}_{i}$ (i.e.\ a function times a constant vector), where the
$\vec{v}_{i}$ are linearly independent. In
particular, our solution $\vec{x}(t)$ can be written as $\sum_{i=1}^{n}
c_{i}f_{i}(t) \vec{v}_{1}$.

Choose a vector $\vec{w}$ orthogonal to $\vec{v}_{2}, \dots,
\vec{v}_{n}$ but not to $\vec{v}_1$. Since $\vec{x}(t) \to 0$ as $t
\to \infty$, the same is true of $\vec{w} \cdot \vec{x}$; but that is
simply $(\vec{w} \cdot \vec{v}_{1}) c_{1} f_{1}(t)$. In other words,
if $c_{i} \neq 0$, then $f_{i}(t)$ must also go to 0.

However, it is easy to exhibit a solution which does not go to 0. The
sum of the eigenvalues of the matrix $A = (a_{ij})$, also known as the
trace of $A$, being the sum of the diagonal entries of $A$, is
nonnegative, so $A$ has an eigenvalue $\lambda$ with nonnegative real
part, and a corresponding eigenvector $\vec{v}$. Then $e^{\lambda t}
\vec{v}$ is a solution that does not go to 0. (If $\lambda$ is not
real, add this solution to its complex conjugate to get a real
solution, which still doesn't go to 0.)

Hence one of the $c_{i}$, say $c_{1}$, is zero, in which case
$\vec{x}(t) \cdot \vec{w} = 0$ for all $t$.
 

FAQ: Are these functions necessarily linearly independent?

What is the definition of "linearly independent" functions?

Linearly independent functions are a set of functions that cannot be expressed as a linear combination of each other. In other words, none of the functions can be written as a multiple of another function.

Why is it important to determine if functions are linearly independent?

Determining if functions are linearly independent is important because it helps us understand the relationship between the functions and how they can be used in mathematical models. It also allows us to simplify complex systems and identify the minimum number of functions needed to describe a system.

How can we test if functions are linearly independent?

One way to test if functions are linearly independent is to use the Wronskian determinant. If the determinant is non-zero for all values of the independent variable, then the functions are linearly independent.

Can two linearly dependent functions be used to describe a system?

No, two linearly dependent functions cannot be used to describe a system because they are essentially the same function and do not provide any additional information. In order to fully describe a system, the functions must be linearly independent.

Are all linearly independent functions orthogonal?

No, not all linearly independent functions are orthogonal. Orthogonality refers to the property of being perpendicular, and this only applies to vector spaces. Functions can be linearly independent without being orthogonal.

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