Linear Dependence of Matrix Vectors

In summary, to prove that the set {I, A, A^2,..., A^n} is linear dependent, one can either show the linear combinations of matrices to get I and relate it to the matrices A, or use the Cayley-Hamilton theorem and the characteristic polynomial of degree n to show linear dependence. Both methods lead to the conclusion that the set is linear dependent.
  • #1
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I need to prove that the set {I, A, A^2,..., A^n} is linear dependent where A is any nxn matrix. The vector space is the set of nxn matrix, considered as a nxn dimensional vector space.

Does anybody have an idea how to prove it?
Thank you very much.
 
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  • #2
well I maybe wrong but to get you started:

I is in the nxn vector space and all the other matrices A, A^2, ..A^n

You might start by showing the linear combinations of matrices to get I and then relate that to the matrices A, of which matrices' can be written as nxn A^n=PA(^n)P^(-1)
 
  • #3
Well, I tried to write A=P^(-1) J P where J is a Jordan matrix. Even more, I also tried to put J=N + D where N is nilpotent such that N^n=0 and D diagonal but I could not prove that the set is linear dependent. Thank you for your help. I f you have any further details or ideas I would be pleased.
 
  • #4
At the end I arrived at the right solution. (A-lambda I)^k must be zero for some k<n. It comes from the Jordan canonical matrix, where lambda belongs to its spectrum. Logically then, the set {I, A, A^2,..., A^n} is linear dependent.

Thank you
 
  • #5
An alternative method, which gives you the exact dependence right off the bat, is to use the Cayley-Hamilton theorem. The characteristic polynomial is of degree at most n, and is satisfied by the matrix.
 
  • #6
Great! Thats certainly a much better, simpler and more elegant solution.
Thank you very much, Henry.
 

Related to Linear Dependence of Matrix Vectors

1. What is linear dependence of matrix vectors?

Linear dependence of matrix vectors refers to the relationship between two or more vectors in a matrix. It is a mathematical concept that describes how one vector can be expressed as a linear combination of other vectors in the matrix.

2. How do you determine if matrix vectors are linearly dependent?

To determine if matrix vectors are linearly dependent, you can use the Gaussian elimination method or the determinant method. In Gaussian elimination, you reduce the matrix to its row echelon form and check for any all-zero rows. If there is at least one all-zero row, the vectors are linearly dependent. In the determinant method, you calculate the determinant of the matrix. If the determinant is equal to 0, the vectors are linearly dependent.

3. What is the significance of linear dependence of matrix vectors?

Understanding the linear dependence of matrix vectors is important in various fields of science and engineering, such as linear algebra, physics, and computer graphics. It helps in solving systems of linear equations and identifying the basis of a vector space. Additionally, it is used in data analysis and modeling to identify relationships between variables.

4. Can two vectors be linearly dependent if they are not scalar multiples of each other?

Yes, two vectors can be linearly dependent even if they are not scalar multiples of each other. This is because linear dependence does not only refer to scalar multiples, but also to any linear combination of vectors. For example, if vector A = [1, 2, 3] and vector B = [2, 4, 6], they are linearly dependent because B = 2A.

5. How does linear dependence affect the invertibility of a matrix?

If a matrix has linearly dependent vectors, it is not invertible. This is because the determinant of an invertible matrix is non-zero, and if the determinant is equal to 0, the matrix is not invertible. Additionally, linear dependence can lead to redundant information in a matrix, making it difficult to solve for unique solutions.

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