How to find orthogonal vectors?

In summary, this person is trying to find a vector that has its dot product with another vector that has the same origin vanishes. There are multiple ways to do this, depending on what information is available.
  • #1
meteorologist1
100
0
Hi, this might be very easy, but I forgot how to do the following: I have a vector in R^6: (x1, x2, x3, x4, x5, x6). How do I find a vector such that their dot product vanishes? I know how to do it for the two dimensional case: (x1, x2), so the vector that is perpendicular to it is c(-x2, x1) where c is a scalar. Could someone show me how to do it for the vector in R^6 case?
 
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  • #2
Take the cross product of your vector and any other vector that has the same origin (any unit vector usually fits the bill and is easiest since only one axis is non-zero).
 
  • #3
Ok I see. I'm not sure how to do a cross product for a six-component vector though. Could you please show me?
 
  • #4
The only condition on the vector is that its dot product with x vanishes?

Just solve

[tex]\vec{x}\cdot\vec{y} = 0[/tex]

--J
 
  • #5
Given a vector in n-dimensions, there is a whole (n-1)-dimensional hyperplane of vectors that are orthogonal to that vector.

Let me do the 4D-case. The 6D-case is easily obtained by generalizing.
Given V=(x1,x2,x3,x4), choose W=(w1,w2,w3,w4) such that
0=(V dot W)=x1w1+x2w2+x3w3+x4w4.

One choice is w1=-x2, w2=x1, w3=0, w4=0. (This is a generalization of your 2D example).

Basically, you can choose values for three components of W. The fourth is determined by the requirement that 0=(V dot W).
 
  • #6
since someone has already posted the answer... I will show you another alternative method for this problem

let's say you have vector x in R^n, and want to find a vector y orthogonal to it
we know the dot product of these two vector must be zero

[tex] \vec{x} \cdot \vec{y} = 0 [/tex]

[tex] \Rightarrow x_{1} y_{1} + x_{2} y_{2} + x_{3} y_{3} + ...+ x_{n-1} y_{n-1} + x_{n} y_{n} = 0 [/tex]

[tex]\Rightarrow x_{1} y_{1}+ x_{2} y_{2} + x_{3} y_{3} + ...+ x_{n-1} y_{n-1} = - x_{n} y_{n} [/tex]

[tex]\Rightarrow y_{n} = -(x_{1} y_{1} + x_{2} y_{2} + x_{3} y_{3} +...+ x_{n-1} y_{n-1}) / x_{n} [/tex]

now, you have more freedom to vary [itex] y_{1}.y_{2}...y_{n-1} [/itex] , as long as you the [itex] y_{n} [/itex] follows the formulas above :biggrin:
 
  • #7
If you know how to do it with 2 dim vectors, just take a vector with all except two components 0 and fix the other 2 to give a 0 dot product!

For example, to find a vector orthogonal to <1, -3, 4, 1 , -1, 2>, take all except the first two components 0, take the first two 3 and 1 respectively: <3, 1, 0, 0, 0, 0>.

Of course, there are many independent vectors orthogonal to a given six dimensional vector (not true in 2 dimensions). You could also choose to make all except the first and last components 0: <2, 0, 0, 0, 0, -1>.

OR <0, 4, 3, 0, 0, 0>

OR ...
 
  • #8
All right thanks all.
 

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

To determine if two vectors are orthogonal, you can use the dot product formula. If the dot product of the two vectors is equal to 0, then they are orthogonal. Alternatively, you can also check if the angle between the two vectors is 90 degrees.

2. How do you find an orthogonal basis for a subspace?

To find an orthogonal basis for a subspace, you can use the Gram-Schmidt process. This involves taking a set of linearly independent vectors from the subspace and applying the Gram-Schmidt algorithm to orthogonalize them. The resulting set of vectors will form an orthogonal basis for the subspace.

3. Can two vectors in a three-dimensional space be orthogonal?

Yes, two vectors in a three-dimensional space can be orthogonal. In fact, there can be an infinite number of orthogonal vectors in a three-dimensional space. The only requirement is that the dot product of the two vectors is equal to 0.

4. How many orthogonal vectors can exist in a n-dimensional space?

In an n-dimensional space, there can be a maximum of n orthogonal vectors. This is because any additional vectors would be linearly dependent on the existing n vectors, making them non-orthogonal.

5. Can orthogonal vectors be used to create a right-handed coordinate system?

Yes, orthogonal vectors can be used to create a right-handed coordinate system. In fact, in a three-dimensional space, three orthogonal vectors can be used to define the x, y, and z axes, respectively, and create a right-handed coordinate system.

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