Define Matrix A w/ Orthogonal Vectors | R3

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In summary, A is a nonsingular (invertible) matrix with a diagonal matrix equal to A'A. This can be calculated using the given orthogonal vectors. The relation between orthogonality and linear independence shows that the matrix is non-singular, and therefore regular.
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dlevanchuk
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Homework Statement



Let S = { u1, u2, u3} be an orthogonal set of nonzero vectors in R3. Define (3 x 3) matrix A by A = [u1, u2, u3]. Show that A is nonsingular and A'A (' is transpose) is a diagonal matrix. Calculate the diagonal matrix using the given orthogonal vectors: u1 = [1 1 1]'; u2 = [-1 0 1]'; u3 = [-1 2 -1]'

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The Attempt at a Solution



I think my IQ drops by the minute, thereof a stupid question... what does it mean to "define" the matrix? Do I just create the matrix from the given set of vectors??
 
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  • #2
dlevanchuk said:
I think my IQ drops by the minute, thereof a stupid question... what does it mean to "define" the matrix? Do I just create the matrix from the given set of vectors??

Yes, exactly.

Now, what is the relation between orthogonality and linear independence of vectors? Further on, how does this relate to the matrix rank? And how does the rank relate to regularity/non-singularity?
 
  • #3
could i just say that since the vectors are orthogonal, that means they are linearly independent, and that makes the matrix non singular.. sounds pretty logical to me :)
 

1. What is a matrix?

A matrix is a rectangular array of numbers or symbols arranged in rows and columns. It is often used to represent and manipulate data in mathematics, science, and engineering.

2. What are orthogonal vectors?

Orthogonal vectors are two or more vectors that are perpendicular to each other, meaning they form a 90 degree angle. In other words, the dot product of orthogonal vectors is equal to 0.

3. How do you define matrix A with orthogonal vectors in R3?

To define matrix A with orthogonal vectors in R3, we first choose two vectors that are orthogonal to each other. Then, we place the first vector as the first column of A and the second vector as the second column of A. Finally, we fill in the remaining column with a third vector that is also orthogonal to the first two vectors.

4. Why is it important to use orthogonal vectors in a matrix?

Using orthogonal vectors in a matrix can simplify calculations and make them more efficient. It also allows for easier manipulation and analysis of data, as orthogonal vectors have distinct properties that can be utilized in various mathematical operations.

5. In what areas of science is the use of matrices with orthogonal vectors common?

The use of matrices with orthogonal vectors is common in fields such as physics, engineering, and computer science. It is often used in applications such as image processing, signal processing, and data analysis. It is also commonly used in linear algebra, a branch of mathematics that deals with vector spaces and linear transformations.

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