On an eigenvector of matrix

In summary, the conversation is discussing whether a given matrix A with certain properties (binary with zero diagonal and a specific formula for element values) will have the same eigenvector as its square matrix B (formed by multiplying A with itself) when the eigenvector for B is the vector of all ones. The speaker is questioning if this is always true and presents a counterexample for 2x2 matrices. They are asking for ideas on how to investigate this further for matrices larger than 2x2.
  • #1
A_Studen_349q
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0

Homework Statement


A=||A(i,j)|| (i,j=1,…,n) (n>2) is a binary matrix with zero diagonal and A(i,j)=1-A(j,i) for i≠j. W=(1,1,…,1)’ is an eigenvector for matrix B=A*A. Will W be an eigenvector for matrix A too? Why?

2. The attempt at a solution
Let have a look at these two statements:
"a". A=||A(i,j)|| (i,j=1,…,n) (n>2) is a binary matrix with zero diagonal and A(i,j)=1-A(j,i) (for i≠j) AND W=(1,1,…,1)’ is an eigenvector for matrix A.
"b". B=A*A (matrix A is of a form mentioned in "a") AND W=(1,1,…,1)’ is an eigenvector for matrix B.
It is easy to see that "a"=>"b", BUT how to investigate the implication "b"=>"a"? Is it always true? The first one ("a"=>"b") tells that the second ("b"=>"a") sometimes may be true (when both "a" and "b" occur) but will (or not?) the second always be true? I tried to investigate possible forms of matrix B but failed (no common form of matrix B could be found).
 
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  • #2
Any ideas?
 
  • #3
[edit] I found an immediate 2x2 counterexample but then noticed the n > 2 constraint.
 
  • #4
Dear brainmonsters and superbrains, have you got any new ideas?
 

1. What is an eigenvector of a matrix?

An eigenvector of a matrix is a vector that, when multiplied by the matrix, results in a scalar multiple of itself. In other words, the direction of the eigenvector does not change when multiplied by the matrix.

2. How is an eigenvector different from a regular vector?

Unlike a regular vector, an eigenvector is not affected by matrix multiplication, except for a scalar multiple. Additionally, an eigenvector is always associated with a specific eigenvalue.

3. What is the significance of an eigenvector in linear algebra?

Eigenvectors are important in linear algebra because they represent the directions in which a linear transformation only changes in magnitude, not direction. This allows for simplification of matrix operations and analysis of systems in terms of their eigenvalues and eigenvectors.

4. Can a matrix have multiple eigenvectors?

Yes, a matrix can have multiple eigenvectors. In fact, most matrices have multiple eigenvectors associated with different eigenvalues. However, some matrices may have repeated eigenvalues, leading to fewer distinct eigenvectors.

5. How are eigenvectors used in real-world applications?

Eigenvectors have numerous applications in various fields, including physics, engineering, computer science, and data analysis. They are used to understand the behavior of complex systems, perform dimensionality reduction in data analysis, and solve differential equations, among other applications.

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