Eigenvalue Vector x: Question 1 and 2

In summary: The matrix I is the identity matrix, which has all diagonal entries 1 and everything else 0. So the last part should be factored as Ix=1x, where the 1 is a scalar, not the matrix I.
  • #1
Miike012
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Question one: in regards to two segments underlined in blue.

If (a,x) is an eigenvalue and vector of A, that means

Ax = ax, where a is a real number.

My question is, is Amx = amx, where m in an integer greater than 1?


Question 2: in regards to two segments underlined in red.

I know that I*x = x, where I is the nxn identity matrix and x is a vector in Rn.
But the last part where the vector x is factored, shouldn't the 1 be an I?
 

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  • #2
Miike012 said:
Question one: in regards to two segments underlined in blue.

If (a,x) is an eigenvalue and vector of A, that means

Ax = ax, where a is a real number.

My question is, is Amx = amx, where m in an integer greater than 1?


Question 2: in regards to two segments underlined in red.

I know that I*x = x, where I is the nxn identity matrix and x is a vector in Rn.
But the last part where the vector x is factored, shouldn't the 1 be an I?

Yes, ##A^mx=a^mx##. You should try to prove that if you aren't sure. For the second question what they factored out are scalars. Since Ix=1x what you factor out should be the scalar 1, not the matrix I.
 
  • #3
Miike012 said:
Question one: in regards to two segments underlined in blue.

If (a,x) is an eigenvalue and vector of A, that means

Ax = ax, where a is a real number.

My question is, is Amx = amx, where m in an integer greater than 1?


Question 2: in regards to two segments underlined in red.

I know that I*x = x, where I is the nxn identity matrix and x is a vector in Rn.
But the last part where the vector x is factored, shouldn't the 1 be an I?

Note: do NOT assume that the eigenvalue is real: it may not be. Some matrices have only real eigenvalues, others have some that are complex.
 

Related to Eigenvalue Vector x: Question 1 and 2

Question 1: What is an eigenvalue?

An eigenvalue is a scalar value that represents the magnitude of a particular eigenvector. It is often denoted by the Greek letter lambda (λ) and is an important concept in linear algebra.

Question 2: What is an eigenvector?

An eigenvector is a vector that, when multiplied by a matrix, results in a scalar multiple of itself. In other words, it is a special vector that remains in the same direction after being transformed by a matrix.

Question 3: How are eigenvalues and eigenvectors related?

Eigenvalues and eigenvectors are related in that an eigenvector is associated with a specific eigenvalue. The eigenvalue represents the magnitude of the eigenvector, and the eigenvector represents the direction in which the matrix transformation occurs.

Question 4: What are the practical applications of eigenvalues and eigenvectors?

Eigenvalues and eigenvectors have many practical applications in fields such as physics, engineering, and computer science. They are used in image and signal processing, data compression, and in solving differential equations.

Question 5: How are eigenvalues and eigenvectors calculated?

Eigenvalues and eigenvectors can be calculated using various methods, such as the characteristic polynomial or the power method. These methods involve finding the roots of a polynomial or repeatedly multiplying a vector by a matrix until it converges to an eigenvector.

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