Linearly independence of vector function

In summary: That is, there are no such coefficients that work for all values of ##t##. So, while the linear dependence holds for each fixed ##t##, it does not hold for all ##t## at the same time.In summary, x and y are linearly dependent at each point in the interval [0, 1], but they are linearly independent on [0, 1]. This is not a contradiction.
  • #1
HAMJOOP
32
0
Given two vectors
x(t) = (e^t te^t)^T

y(t) = (1 t)^T


a) Show that x and y are linearly dependent at each point in the interval [0, 1]

b) Show that x and y are linearly independent on [0, 1]


I compute det([x y]) = 0, so they are linearly dependent
how about part b. Isn't a) and b) are contradictory


The above problem comes from Elementary Differential Equations and Boundary Value Problems 9th ed.




Another question
given two vectors depends on t, v and w each has two components

det([v w]) = 0 at some points only
Can I say v and w are linearly dependent at those points ??
 
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  • #2
HAMJOOP said:
Given two vectors
x(t) = (e^t te^t)^T

y(t) = (1 t)^T


a) Show that x and y are linearly dependent at each point in the interval [0, 1]

b) Show that x and y are linearly independent on [0, 1]


I compute det([x y]) = 0, so they are linearly dependent
how about part b. Isn't a) and b) are contradictory
No, there is no contradiction.

In part (a), you are fixing a value of ##t##, call it ##t = t_0##, so the elements of the vectors are simply numbers. The linear dependence means that there exist coefficients ##a## and ##b## such that ##a x(t_0) + b y(t_0) = 0##. But the coefficients will vary with ##t_0##.

Part (b) is asking you to show that there are no coefficients ##a## and ##b## for which ##ax(t) + by(t) = 0## is true simultaneously for all ##t \in [0,1]##.
 

1. What is meant by linearly independent vector functions?

Linearly independent vector functions are a set of functions in which no function can be expressed as a linear combination of the others. In other words, the functions are not dependent on each other and cannot be formed by scaling or adding other functions in the set.

2. How do you determine if a set of vector functions is linearly independent?

To determine if a set of vector functions is linearly independent, you can use the Wronskian determinant method. This involves calculating the determinant of a matrix formed by the functions and their derivatives. If the determinant is non-zero, the functions are linearly independent. If the determinant is zero, the functions are linearly dependent.

3. Can a set of linearly independent vector functions also be linearly dependent?

No, a set of linearly independent vector functions cannot also be linearly dependent. If a set of functions is linearly independent, it means that each function is unique and cannot be formed by combining other functions. Therefore, there cannot be any linear combinations of the functions that result in a zero vector.

4. How does linear independence relate to the dimension of a vector space?

The dimension of a vector space is equal to the number of linearly independent vectors or functions that span the space. This means that the more linearly independent functions a vector space has, the higher its dimension will be. In other words, linear independence is a key factor in determining the size and complexity of a vector space.

5. Can a set of linearly dependent vector functions still be useful in practical applications?

Yes, a set of linearly dependent vector functions can still be useful in practical applications. While they may not be considered linearly independent, they can still provide valuable information and insights into a system or phenomenon. In some cases, linearly dependent functions can also be transformed into linearly independent ones, making them more useful in certain contexts.

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