Diagonalisability problem and others

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In summary, for the first problem, the goal is to prove that AtA is diagonalisable for a real matrix A. To do this, we can show that AtA is symmetric by writing out its elements in terms of the elements of A and showing that they are equal. As for the second problem, the task is to calculate the maximum and minimum values of ||Ax|| on the sphere ||x|| = 1 for a given 3*3 matrix A. X must be a 3x1 vector for the product Ax to be meaningful.
  • #1
rainwyz0706
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Homework Statement


1.Prove that if A is a real matrix then At A is diagonalisable.

2. Given a known 3*3 matrix A, Calculate the maximum and minimum values of ||Ax|| on the sphere ||x|| = 1.


Homework Equations





The Attempt at a Solution


For the first problem, I'm thinking of proving that AtA is symmetric, but I'm not sure which properties to use.
For the second one, is X a 3*n matrix? Do I need to discuss n?
Any help is greatly appreciated!
 
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  • #2
rainwyz0706 said:
For the first problem, I'm thinking of proving that AtA is symmetric, but I'm not sure which properties to use.
If [itex]B=A^TA[/itex], write down what [itex]B_{ij}[/itex] and [itex]B_{ji}[/itex] are in terms of the elements of A and show that they're equal.
 
  • #3
Thanks a lot! I can't believe I missed it in the first place.
Could anyone give me some hints about problem 2?
 
  • #4
For the second one, yes, x must be such a vector that the product Ax is meaningful, i.e. a 3x1 vector (matrix).
 
  • #5
thx, I finished it!
 

FAQ: Diagonalisability problem and others

What is the diagonalisability problem?

The diagonalisability problem is a mathematical problem that involves determining whether a given matrix can be transformed into a diagonal matrix through a change of basis. This problem is important in linear algebra and has applications in fields such as quantum mechanics and differential equations.

How do you determine if a matrix is diagonalisable?

A matrix is diagonalisable if it can be transformed into a diagonal matrix through a change of basis. This can be determined by finding the eigenvalues and eigenvectors of the matrix and checking if the eigenvectors form a basis for the vector space. If they do, then the matrix is diagonalisable.

What are the applications of diagonalisability in science?

Diagonalisability has various applications in science, particularly in fields such as quantum mechanics and differential equations. In quantum mechanics, diagonalisability is used to find the energy levels of a system. In differential equations, diagonalisability is used to solve systems of linear differential equations.

What other problems are related to diagonalisability?

Other problems related to diagonalisability include finding the inverse of a matrix, computing powers of a matrix, and solving systems of linear equations. These problems are all linked to diagonalisability as they involve matrix transformations and the use of eigenvectors and eigenvalues.

How can diagonalisability be applied in real-world scenarios?

Diagonalisability has practical applications in fields such as engineering, physics, and computer science. For example, in engineering, diagonalisability is used in control systems to model and analyze the behavior of dynamic systems. In physics, it is used to understand the behavior of quantum systems. In computer science, it is used in data compression and signal processing.

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