Differentiable Linear Transformation

In summary, the conversation discusses the linear space V of all real functions differentiable on (0,1) and the operation T, which takes a function f in V and produces a new function g by multiplying f'(t) by t for all t in (0,1). The task is to prove that every real number λ is an eigenvalue for T and to determine the corresponding eigenfunctions. An eigenfunction of T with eigenvalue λ means that T applied to the function results in a scalar multiple of the original function, where the scalar is λ. The definition of T is used to further explain this relationship.
  • #1
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Homework Statement



Let V be the linear space of all real functions Differentiable on (0,1). If f is in V define g=T(f) to mean that g(t)=tf'(t) for all t in (0,1). Prove that every real λ is an eigenvalue for T, and determine the eigenfunctions corresponding to λ.

Homework Equations


The Attempt at a Solution



All I know is that f'=λf and T(f)=λf in general. I tried substituting the variables, and I ended up with only t, which doesn't make sense.
 
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  • #2
Can you use the definitions of "eigenvalue", "eigenfunction" and T to explain what "f is an eigenfunction of T with eigenvalue λ" means?

Edit: You have already given a partial answer for that by saying that Tf=λf. This is the part that follows from the definitions of eigenvalue and eigenfunction. So now you need to use the definition of T to explain what Tf=λf means.
 
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FAQ: Differentiable Linear Transformation

What is a differentiable linear transformation?

A differentiable linear transformation is a mathematical function that maps one vector space to another, while preserving the properties of linearity and differentiability. This means that the transformation can be described by a linear equation and its derivative exists at every point in its domain.

What are the properties of a differentiable linear transformation?

A differentiable linear transformation has two key properties: linearity and differentiability. Linearity means that the transformation preserves scalar multiplication and vector addition, while differentiability means that the transformation has a well-defined derivative at every point in its domain.

How is a differentiable linear transformation represented?

A differentiable linear transformation can be represented using a matrix. The matrix representation of a linear transformation is a set of numbers arranged in a rectangular array, where each number represents the coefficient of a variable in the linear equation.

What is the importance of differentiable linear transformations in science?

Differentiable linear transformations are important in science because they allow us to model and understand complex systems, such as physical processes and natural phenomena, using simple and elegant mathematical equations. They also provide a powerful framework for solving optimization problems and making predictions.

How are differentiable linear transformations used in machine learning?

In machine learning, differentiable linear transformations are used to model relationships between input features and output variables. They are an essential component of many popular machine learning algorithms, such as linear regression and logistic regression, and are also used in more complex models such as neural networks.

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