3 by 3 matrix with an orthogonality constraint

In summary: As a result, there are 3 (9-6) independent numbers in R.In summary, the conversation discusses the number of independent parameters in a 3x3 matrix. It is initially stated that a real 3x3 matrix has 9 entries, but the discussion then focuses on the orthogonality constraint. This constraint leads to 6 independent equations, leaving 3 independent numbers in the matrix.
  • #1
touqra
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This is a paragraph from a book, which I don't understand:
"How many independent parameters are there in a 3x3 matrix? A real 3x3 matrix has 9 entries but if we have the orthogonality constraint,
[tex]RR^T = 1 [/tex]
which corresponds to 6 independent equations because the product
[tex]RR^T[/tex] being the same as [tex]R^TR [/tex], is a symmetrical matrix with 6 independent entries.
As a result, there are 3 (9-6) independent numbers in R."
I can understand why a real 3x3 matrix has 9 entries. But the sentences after that...I don't understand.
 
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  • #2
touqra said:
This is a paragraph from a book, which I don't understand:
"How many independent parameters are there in a 3x3 matrix? A real 3x3 matrix has 9 entries but if we have the orthogonality constraint,
[tex]RR^T = 1 [/tex]
which corresponds to 6 independent equations because the product
[tex]RR^T[/tex] being the same as [tex]R^TR [/tex], is a symmetrical matrix with 6 independent entries.
As a result, there are 3 (9-6) independent numbers in R."
I can understand why a real 3x3 matrix has 9 entries. But the sentences after that...I don't understand.
The orginal statement is a bit peculiar. "How many independent parameters are there in a 3x3 matrix?" is answered by the first part of the next sentence. Since a real 3x3 matrix has 9 entries- 9 independent parameters. However, it then talks about the "orthogonality constraint" as if it were talking about orthogonal matrices all along.
Imagine writing out a 3x3 matrix and its transpose, then multiplying them. Since RRT= 1, that gives 9 equations. However, RTR must give the same thing so that not all of those equations are independent. There are 3 equations that say the quantities on the main diagonal are equal to 1 and 3 equations that say the quantities above the main diagonal are equal to 0. The 3 equations that say the quantities below the main diagonal are 0 do not give us anything new because of the symmetry. There are (no more than) 6 independent equations. You could choose 3 of the entries independently (as the parameters) and then solve the 6 equations for the remaining 6 numbers.
 

FAQ: 3 by 3 matrix with an orthogonality constraint

What is a 3 by 3 matrix with an orthogonality constraint?

A 3 by 3 matrix with an orthogonality constraint is a square matrix with dimensions of 3 rows and 3 columns, where the rows and columns are orthogonal or perpendicular to each other.

What does it mean for a matrix to be orthogonal?

A matrix is orthogonal if its rows and columns are perpendicular to each other, and the dot product of any two rows or columns is equal to zero.

How is the orthogonality constraint applied to a 3 by 3 matrix?

The orthogonality constraint is applied by ensuring that the dot product of any two rows or columns of the matrix is equal to zero. This means that the rows and columns of the matrix are perpendicular to each other.

What are the benefits of using a 3 by 3 matrix with an orthogonality constraint?

Using a 3 by 3 matrix with an orthogonality constraint can simplify calculations and make it easier to solve problems. It also has applications in fields such as computer graphics, signal processing, and physics.

Can a 3 by 3 matrix with an orthogonality constraint have any values for its elements?

No, a 3 by 3 matrix with an orthogonality constraint must have specific values for its elements. The rows and columns must be perpendicular to each other, which requires specific values for the elements to satisfy the orthogonality constraint.

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