Linear Algebra- Kernel and images of a matrix

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SUMMARY

The discussion focuses on the relationships between the kernel and image of a square matrix A and its powers, specifically ker(A), ker(A^2), im(A), and im(A^2). It is established that if A is an invertible matrix, then ker(A) is the zero vector, and im(A) spans R^n. The discussion confirms that ker(A) is a subset of ker(A^2) and that im(A) contains im(A^2), though they are not necessarily equal. This pattern extends to higher powers of A, indicating a consistent relationship across ker(A^n) and im(A^n).

PREREQUISITES
  • Understanding of linear algebra concepts, specifically kernels and images of matrices.
  • Familiarity with matrix operations and properties of invertible matrices.
  • Knowledge of vector spaces and their dimensions.
  • Basic proficiency in mathematical notation and terminology related to linear transformations.
NEXT STEPS
  • Study the properties of kernel and image in linear transformations.
  • Explore the implications of the Rank-Nullity Theorem in relation to ker(A) and im(A).
  • Investigate the behavior of kernels and images for non-invertible matrices.
  • Learn about the relationship between eigenvalues, eigenvectors, and the kernel/image of a matrix.
USEFUL FOR

Students and professionals in mathematics, particularly those studying linear algebra, as well as educators seeking to deepen their understanding of matrix theory and its applications.

KyleS4562
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Homework Statement



Consider a square matrix A:

a. What is the relationship between ker(A) and ker(A^2)? Are they necessarily equal? Is one of them necessarily contained in the other? More generally, What can you say about ker(A), ker(A^2), ker(A^3), ker(A^4),...?

b. What can you say about im(A), im(A^2), im(A^3), im(A^4),...?


2. The attempt at a solution

So i believe if A is invertible nxn matrix, than ker(A)={<0,0,0>} and so will ker(A^2) and so on. And the image of A if A is invertible is im(A)=R^n and so will the im(A^2) and so on, but I am not sure what it would be for other conditions of A at least that's what I think this question wants.
 
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a. If x \in \ker(A), what can you say about A^2 x? What does that tell you about \ker(A^2)?

b. For any x, A^2x is in \operatorname{im}(A^2). If you rewrite A^2x as A(Ax)=Ay, what can you say about \operatorname{im}(A)?
 
for A: A^2 * x would equal the zero vector which means the ker(A^2) contains x but is not necessarily equal because it may contain another vector

B. Is that saying the im(A) contains the im(A^2) but is not necessarily equal to the im(A^2) because it will span more vectors?
 
KyleS4562 said:
for A: A^2 * x would equal the zero vector which means the ker(A^2) contains x but is not necessarily equal because it may contain another vector

In other words, we have \ker(A)\subset \ker(A^2).

KyleS4562 said:
B. Is that saying the im(A) contains the im(A^2) but is not necessarily equal to the im(A^2) because it will span more vectors?

Yes. You could also write : \operatorname{im}(A^2)\subset \operatorname{im}(A)

Now, how can you apply this to \ker(A^3), \ker(A^4)... and \operatorname{im}(A^3), \operatorname{im}(A^4)...?
 
alright, thank you very much for your help
 

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