Are the Row Vectors of a Matrix with Orthonormal Columns Also Orthonormal?

In summary, the conversation is discussing the orthonormality of a matrix A, which is defined as having columns that are orthonormal vectors. This implies that A is also an orthogonal matrix and its inverse is equal to its transpose. The question is then raised on how to show that the rows of A are also orthonormal. One suggestion is to use the fact that if A^(-1) = A^T, then the rows of A will also be orthonormal. Another approach is to prove that if the columns of A are orthonormal, then the rows are as well.
  • #1
mathboy20
30
0
Hi

Given A an n x n matrix where the columns are the set of standard vectors S = {e1, e2,...e_n} in R^n

These vectors are orthonormal according to the definition since

<e_i, e_j> = 0, i \neq j

<e_i,e_i> = 1

Since the columns of A then they are orthonormal according to the definition, and therefore the matrix A is orthogonal matrix which implies that

A^T A = I <---> A^ (-1) = A^T, and so on.


I need to show that the row vectors of of A are orthonormal too.

Any idears on how?

My own idear is that

Since

A^ (-1) = A^T, then the columns in invertible A become the rows in transposed A, and the these orthonormal according to the definition?

Sincerely Yours
Mathboy20
 
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  • #2
I don't know what your reasoning is, but with A^(-1) = A^T then you also know that (A^T)^(-1) = A. A^T*A = A*A^T. It follows from there.
 
  • #3
Actually, the way you stated the problem, its trivial. If the columns of the matrix are just the standard basis vectors e1, etc. then the rows are those basis vectors too! More likely you want to prove that if columns are any set of orthonormal vectors, then the rows are too. Orthodontist's hint is good.
 

Related to Are the Row Vectors of a Matrix with Orthonormal Columns Also Orthonormal?

1. What are orthonormal row vectors?

Orthonormal row vectors are a set of vectors that are both orthogonal (perpendicular) and normalized (unit length). This means that each vector in the set is perpendicular to every other vector and has a length of 1.

2. How are orthonormal row vectors used in mathematics?

Orthonormal row vectors are commonly used in linear algebra to represent an orthonormal basis for a vector space. They are also used in matrix multiplication and transformations, as well as in solving systems of linear equations.

3. How are orthonormal row vectors created?

Orthonormal row vectors can be created by starting with a set of linearly independent vectors and applying the Gram-Schmidt process. This process involves orthogonalizing the vectors and then normalizing them to have a length of 1.

4. What is the significance of orthonormal row vectors in data analysis?

In data analysis, orthonormal row vectors are often used to represent the features or variables of a dataset. This allows for easier computation and interpretation of the data, as well as reducing the effects of multicollinearity.

5. How are orthonormal row vectors related to orthonormal matrices?

Orthonormal row vectors can be used to create an orthonormal matrix, where the vectors are arranged as rows. Similarly, an orthonormal matrix can be used to create a set of orthonormal row vectors by taking the rows of the matrix.

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