Vectors Question (Orthogonality)

In summary, using the Gram-Schmidt process on u1, u2, and u3, we can obtain orthogonal vectors v1 = [2 -2 2], v2 = [-1 1 2], and v3 = [1, 2/3, 4/3]. However, upon checking the orthogonality of v3, it appears that an error may have been made in the calculation. Further examination of the process used may be necessary to determine the source of the error.
  • #1
haux
5
0

Homework Statement



Let u1 = [2 -2 2 ] u2 = [-2 2 1], u3 = [0 1 2]Use the Gram-Schmidt process to u1, u2, u3, in this order. The resulting vectors are:

v1 = [___ ___ ___], v2 = [___ ___ ___], v3 = [___ ___ ___]

And ß = {v1, v2, v3} is an orthongal basis for R3.

Homework Equations



v1 = u1

v2 = u2 - u2 . v1 v1
------------ v1 . v1
v3= u3 - u3 . v1 v1 - u3 . v2 v2
---------------v1 . v1 v2 . v2

The Attempt at a Solution



Well, the first one is easy.

v1 = [2 -2 2]

I solved v2 and got [-1, 1, 2] which was also right.

I tried v3 based on the equation and got [1, 2/3, 4/3] which was apparently incorrect.
 
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  • #2
Why do you say "apparently" incorrect? You should have checked yourself that <2, -2, 2].[1, 2/3, 4/3]= 2- 4/3+ 8/3= 2+ 4/3 and [-1, 1, 2].[1, 2/3, 4/3]= -1+ 2/3+ 8/3= -1+ 10/3, neither of which is 0.

If you show what you did, that is show use exactly how you used that formula, we might be able to point out an error.
 
  • #3


To find v3, we can use the same process as before, but this time using v2 instead of v1.

v3 = u3 - u3 . v2 v2
---------------v2 . v2

Substituting in the given values, we get:

v3 = [0 1 2] - [0 1 2] . [-1 1 2] [-1 1 2]
-------------------------- [-1 1 2] . [-1 1 2]

Simplifying, we get:

v3 = [0 1 2] - [-3] [-1 1 2]
-------------------------- [-6]

v3 = [0 1 2] + [3 3 -6]
------------------- [-6]

v3 = [3 4 -4]
--------- [-6]

v3 = [-1 -2/3 2/3]

Therefore, the resulting vectors are:

v1 = [2 -2 2]
v2 = [-1 1 2]
v3 = [-1 -2/3 2/3]

And ß = {v1, v2, v3} is an orthogonal basis for R3.
 

1. What is the definition of orthogonality in vectors?

Orthogonality in vectors refers to the property of two vectors being perpendicular or at a 90 degree angle to each other. This means that the dot product of the two vectors is equal to 0, indicating no overlap or similarity in direction.

2. How do you determine if two vectors are orthogonal?

To determine if two vectors are orthogonal, you can take the dot product of the two vectors and if the result is equal to 0, then the vectors are orthogonal. Another way is to graph the vectors and see if they form a 90 degree angle.

3. What is the significance of orthogonality in vector operations?

Orthogonality plays a crucial role in vector operations such as addition, subtraction, and projection. It allows us to break down a vector into its components and simplify calculations, making it easier to solve complex problems.

4. Can a vector be orthogonal to itself?

No, a vector cannot be orthogonal to itself. An orthogonal vector must have a dot product of 0 with another vector. Since the dot product of a vector with itself is equal to the magnitude squared, it is not possible for a vector to be orthogonal to itself.

5. How is orthogonality used in real-life applications?

Orthogonality is used in various fields such as physics, engineering, and computer science. In physics, it is used to calculate forces and motion in different directions. In engineering, it is used in structural analysis and design. In computer science, it is used in graphics and image processing to transform and manipulate vectors.

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