Are ||b||a + ||a||b and ||b||a - ||a||b always orthogonal?

In summary, it has been shown that ||b||a + ||a||b and ||b||a - ||a||b are orthogonal vectors due to the fact that they are formed by multiplying the magnitudes of the vectors and the unit vectors in the parentheses are the diagonals of a unit square, making them perpendicular.
  • #1
1MileCrash
1,342
41

Homework Statement



Show that

||b||a + ||a||b and ||b||a - ||a||b are orthogonal vectors.

Homework Equations





The Attempt at a Solution



After analyzing it and trying to prove it to no avail, I don't even think it's a true statement.
 
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  • #2
1MileCrash said:

Homework Statement



Show that

||b||a + ||a||b and ||b||a - ||a||b are orthogonal vectors.

Homework Equations





The Attempt at a Solution



After analyzing it and trying to prove it to no avail, I don't even think it's a true statement.
If two vectors are othogonal, their dot product will be zero. What do you get if you dot the two vectors in this problem?
 
  • #3
Nevermind, careless error.
 
  • #4
Though I was able to prove it, I do find it difficult to wrap my head around the fact that what makes it work is the scalar multiplication..
 
  • #5
That's probably the most important use of the dot product - determining whether two vectors are orthogonal. Less important, IMO, is the ability to find the angle between two vectors.
 
  • #6
I mean the scalar multiplied by vector multiplication. The dot product is not always 0 for (a+b) * (a-b). I find it hard to grasp that the magnitudes are what made them orthogonal.
 
  • #7
1MileCrash said:
I mean the scalar multiplied by vector multiplication. The dot product is not always 0 for (a+b) * (a-b).
True. If you form a parallelogram with a and b as two adjacent sides, then a + b will be a diagonal, and a - b will be the other diagonal. Most of the time these diagonals won't be perpendicular, so the dot product (a + b)##\cdot##(a - b) won't be zero.
1MileCrash said:
I find it hard to grasp that the magnitudes are what made them orthogonal.
That's because you're essentially working with unit vectors, and the parallelogram is actually a square. In that case, the diagonals are perpendicular.

|b|a + |a|b = |a| * |b| * (a/|a| + b/|b|)

|b|a - |a|b = |a| * |b| * (a/|a| - b/|b|)

The vectors in the parentheses on the right sides are unit vectors, with a/|a| + b/|b| being one diagonal of a unit square, and a/|a| - b/|b| being the other diagonal.

(Here a/|a| means 1/|a| * a, of course. I'm also using just a single pair of | vertical bars to denote vector magnitude.)
 

Related to Are ||b||a + ||a||b and ||b||a - ||a||b always orthogonal?

1. What is vector orthogonality?

Vector orthogonality is a concept in linear algebra where two vectors are said to be orthogonal if they are perpendicular to each other. This means that their dot product is equal to 0.

2. How is vector orthogonality represented mathematically?

Vector orthogonality is represented using the dot product formula: a · b = ||a|| ||b|| cosθ, where a and b are two vectors, ||a|| and ||b|| are their magnitudes, and θ is the angle between them. If the dot product is equal to 0, then the vectors are orthogonal.

3. What is the process for proving vector orthogonality?

To prove vector orthogonality, you need to show that the dot product of the two vectors is equal to 0. This can be done by finding the magnitudes of the vectors and the angle between them, plugging them into the dot product formula, and solving for the dot product. If the dot product is equal to 0, then the vectors are orthogonal.

4. Can two non-zero vectors be orthogonal?

Yes, two non-zero vectors can be orthogonal. As long as their dot product is equal to 0, they are considered orthogonal. This means that their magnitudes and angle between them must satisfy the dot product formula.

5. Why is vector orthogonality important?

Vector orthogonality is important because it allows us to easily calculate the angle between two vectors without needing to use trigonometric functions. It also has many applications in mathematics, physics, and engineering, such as in vector projections and finding perpendicular components of a vector.

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