Linear Algebra - Orthogonal Vectors

In summary: If you are trying to solve for the equation of a line that passes through the origin and one of the given vectors, you are essentially trying to solve for the equation of a line that passes through all three given vectors and the origin.However, if you are trying to find a vector that is perpendicular to two given vectors, then you are essentially trying to solve for the equation of a line that passes through the origin and one of the given vectors and perpendicular to the other given vector.In summary, In this problem, you need to find a nonzero vector that is orthogonal to all three given vectors.
  • #1
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I'm a bit confused, conceptually. This is the problem

Let v1=( 1, -1, 2) v2=( 2, 1, 3) v3=( 1, -4, 3)
Find a nonzero vector u that is orthogonal to all three vectors v1, v2, and v3. I know how to find the projection matrix, P, which I can solve with v1, v2, and v3.
The equation for that is simply p=A(AT A)^-1AT
(Where AT is A transposed)However, I'm not sure exactly what P is... I know it's the projection matrix, but if I solved this, would this give me a matrix that is orthogonal to A? (Assuming A is spanned by v1, v2, and v3). If so, would I just be able to take one of the column vectors from this matrix and assume that it is orthogonal to v1 v2 and v3??

If I'm going in the wrong direction, can someone tell me how to find a vector that is orthogonal to A?Thanks!
 
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  • #2
Two ways. Write the equations a.v1=0, a.v2=0 and a.v3=0 with 'a' unknown and solve the system for the components of a. Alternatively, row reduce the set of vectors to find the two that are linearly independent and then take the cross product. Your pick.
 
  • #3
Thanks for responding... The first recommendation with choosing an 'a' sounds familiar. However, I don't exactly understand how that determines a vector that is perpendicular to the subspace formed by the three vectors.

I'm a bit lost on the concept... Having a hard time visualizing the problem.

v1*u=0
v2*u=0
v3*u=0

where u is a vector, right?

Could someone briefly explain why that works? I apologize if this is painfully obvious...

Thanks again for your help
 
  • #4
If the dot product of two vectors is zero, they are orthogonal (practically by definition).

So if the dot product of a vector with all three of the v's is zero, then it must be orthogonal to all three
 
  • #5
u.v1=0 means ux-uy+2*uz=0. u.v2=0 means 2*ux+uy+3*uz=0. You do the third one. So just three equations in three unknowns. You won't get a unique nonzero solution, but you can still find one, yes?
 
  • #6
Thank you. I understand now what you did any why. However, now I'm having difficulty coming up with a non-zero vector through that 3x3 matrix. So I don't see how to find a unique nonzero solution-- I see why I can't just solve for one.

Any pointers?Thanks again. This is my matrix:

1 -1 2
2 1 3
1 -4 3
 
  • #7
You can just solve for one. I don't really see the problem. Can you post more specifically what equations you are trying to solve? You will find you can't 'solve' for all of the variables. Just set x=1 and determine y and z.
 
  • #8
Have you noticed that the three vectors given are not independent? In fact, it is IMPOSSIBLE to have, in 3 dimensions, a non-zero vector that is orthogonal to each of the independent vectors. Do you remember anything from Calculus about finding a vector that is orthogonal to two given vectors.
 

1. What is the definition of orthogonal vectors?

Orthogonal vectors are two vectors that are perpendicular to each other, meaning that they form a 90-degree angle. This means that their dot product is equal to 0.

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

To determine if two vectors are orthogonal, you can use the dot product formula (a · b = |a||b|cosθ). If the dot product is equal to 0, then the vectors are orthogonal.

3. Can more than two vectors be orthogonal to each other?

Yes, more than two vectors can be orthogonal to each other. For example, in three-dimensional space, three vectors can be orthogonal to each other if they form a right-handed coordinate system.

4. What is the significance of orthogonal vectors in linear algebra?

Orthogonal vectors are important in linear algebra because they form the basis for the concept of orthogonality, which is fundamental in many mathematical and engineering applications. Orthogonality is also used in various algorithms and techniques for solving linear equations and optimization problems.

5. How are orthogonal vectors used in real-world applications?

Orthogonal vectors have many real-world applications, including computer graphics, signal processing, and data compression. They are also used in physics and engineering, such as in the calculation of forces and moments in mechanics problems. In addition, orthogonality is a key concept in statistics and machine learning, where it is used for feature selection and dimension reduction.

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