What is the null space of TΦ for Φ = x over the interval [0,1]?

In summary, the null space, also known as the kernel, of a matrix or linear transformation is the set of all vectors that are mapped to the zero vector. It can be thought of as the set of solutions to a homogeneous system of linear equations, and if it is empty, it means that the matrix or linear transformation is one-to-one. Additionally, the null space is always a subspace of the vector space in which it is defined and has various applications in fields such as data science, image processing, and engineering.
  • #1
tavi
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Let V denote the vectore space of continuously differentiable functions, ƒ, over the interval [0,1] such that ƒ(0)=0.
Suppose Φ is-contained C∞ [0,1] (set of infinitely differentiable functions over the interval [0,1]) and define the operator
TΦ:V→R:ƒ→∫ƒ'(x)Φ(x)dx 0,1
Describe the null space of TΦ if Φ = x (Hint: integration by parts)
 
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  • #2
Please dont' multiple post.
 

1. What is a null space?

The null space, also known as the kernel, of a matrix or linear transformation is the set of all vectors that are mapped to the zero vector.

2. How is the null space related to a linear system of equations?

The null space can be thought of as the set of solutions to a homogeneous system of linear equations, where all of the variables are set equal to zero.

3. What does it mean if the null space is empty?

If the null space is empty, it means that the matrix or linear transformation is one-to-one, meaning that each input has a unique output. In other words, the only vector that is mapped to the zero vector is the zero vector itself.

4. Can the null space be a subspace of the vector space?

Yes, the null space is always a subspace of the vector space in which it is defined. This means that it satisfies the three properties of a subspace: it contains the zero vector, is closed under addition, and is closed under scalar multiplication.

5. How can the null space be used in applications?

The null space has various applications in fields such as data science, image processing, and engineering. It can be used to find solutions to underdetermined systems of equations, reduce the dimensionality of data, and determine the stability of systems in control theory.

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