Diagonalization of linear transformations
Hello everybody!!
My questions are about diagonalization of linear transformations:
1. What are the motivations for diagonalize linear transformations?
2. How does diagonalizing a linear transformation help in theoretical mathematics and applied mathematics?
For example, my way of interpreting or motivating the idea of eigenvalues and eigenvectors is as follows: Linear transformations[$T$ ] (for example, in $\mathbb{R}^{3} $ to fix ideas) are those applications that "transform" certain vector spaces into others; but it is not just any transformation, but those that preserve the algebraic structure of said vector spaces. Now, in this action of "transforming" it happens that the vectors, elements of the vector spaces, change direction; but other vectors maintain the same direction, but have been lengthened or shortened or worse still changed direction (but still maintain their direction). For example: let be a nonzero vector $v\in \mathbb{R} ^{3} $ be a vector before applying the linear transformation $T$ and $ u\in \mathbb{R} ^{3} $ the resulting vector after the transformation; in other words, $T(v)=u=\lambda v, \lambda \in \mathbb{R} $ .
Now, these types of vectors that do not change direction are of great importance in both theoretical and applied mathematics.
But, I don't have a motivation to study the diagonalization of linear transformations.
My questions are about diagonalization of linear transformations:
1. What are the motivations for diagonalize linear transformations?
2. How does diagonalizing a linear transformation help in theoretical mathematics and applied mathematics?
For example, my way of interpreting or motivating the idea of eigenvalues and eigenvectors is as follows: Linear transformations[$T$ ] (for example, in $\mathbb{R}^{3} $ to fix ideas) are those applications that "transform" certain vector spaces into others; but it is not just any transformation, but those that preserve the algebraic structure of said vector spaces. Now, in this action of "transforming" it happens that the vectors, elements of the vector spaces, change direction; but other vectors maintain the same direction, but have been lengthened or shortened or worse still changed direction (but still maintain their direction). For example: let be a nonzero vector $v\in \mathbb{R} ^{3} $ be a vector before applying the linear transformation $T$ and $ u\in \mathbb{R} ^{3} $ the resulting vector after the transformation; in other words, $T(v)=u=\lambda v, \lambda \in \mathbb{R} $ .
Now, these types of vectors that do not change direction are of great importance in both theoretical and applied mathematics.
But, I don't have a motivation to study the diagonalization of linear transformations.
Homeomorfo Fle
121
Answer
Answers can only be viewed under the following conditions:
- The questioner was satisfied with and accepted the answer, or
- The answer was evaluated as being 100% correct by the judge.
Erdos
4.8K
-
From everything you wrote I get the impression, correct me if I'm wrong, that instead of asking why diagonalize a linear transformation; The first thing to ask yourself is why diagonalize a matrix? (I am right?). Could you please put the third link back? (Every time I try to open I get: "The file could not be accessed").
-
I added a note at the end of my solution that answers your question above. The link seems to work for me. Copy/Past the link instead of clicking on it.
The answer is accepted.
Join Matchmaticians Affiliate Marketing
Program to earn up to a 50% commission on every question that your affiliated users ask or answer.
- answered
- 1603 views
- $15.00
Related Questions
- Linear Algebra - Matrices (Multiple Choice Question) (1st Year College)
- Find the general solution of the system of ODE $X'=\begin{bmatrix} 1 & 3 \\ -3 & 1 \end{bmatrix} X$
- Consider the function, prove that it's bilinear, symmetric, and positive definite
- Find eigenvalues and eigenvectors of $\begin{pmatrix} -3 & 0 & 2 \\ 1 &-1 &0\\ -2 & -1& 0 \end{pmatrix} $
- Questions about using matrices for finding best straight line by linear regression
- How to filter data with the appearance of a Sine wave to 'flattern' the peaks
- Determinant and Inverse of a predefined matrix. The question and bounty are updated.
- Consider the plane in R^4 , calculate an orthonormal basis
