Linear combination and orthogonality

In summary, the conversation discusses the proof of a claim that there is a non-zero linear combination of two non-zero vectors in ℝ3 that is orthogonal to a third non-zero vector. It is stated that the vectors must be linearly independent for this claim to hold. However, a counterexample is provided to show that the claim is not always true.
  • #1
ThomMathz
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Given the non-zero vectors u, v and w in ℝ3
Show that there is a non-zero linear combination of u and v that is orthogonal to w.
u and v must be linearly independant.

I am not really sure at all. But I have done this:
This is a screenshot of what I have done. Basicly, I assumed in the end that u and v are not orthogonal, and then I chose some suitable substitution for u and v, and ended up with zero when dotting them with w. Evverything is much appreciated and especially if you have another solution that is better or correct, because I think mine is not that good though.

https://gyazo.com/707e7c168a1c1a15166fd71be4ae7a81
 
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  • #2
I'm pretty sure the claim is not correct. Consider the vectors
u=(5,0,0)
v=(5,1,0)
z=(5,0,1)

The set is linearly independent, but there is no linear combination of u and v that is orthogonal to w.
 
  • #3
andrewkirk said:
I'm pretty sure the claim is not correct. Consider the vectors
u=(5,0,0)
v=(5,1,0)
z=(5,0,1)

The set is linearly independent, but there is no linear combination of u and v that is orthogonal to w.

If by z you mean w, then the statement above is false: the linear combination c*u-c*v = (0,-c,0) is orthogonal to w = (5,0,1).

The result is true, but I will not look at the OP's screenshot (only at a typed version).
 

1. What is a linear combination?

A linear combination is a mathematical operation in which two or more vectors are multiplied by different constants and then added together. The result is a new vector that is a combination of the original vectors.

2. How is orthogonality defined?

Orthogonality is a property of two vectors that have a dot product of zero, meaning they are perpendicular to each other. In other words, the angle between the two vectors is 90 degrees.

3. Can two vectors be both orthogonal and linearly independent?

Yes, two vectors can be both orthogonal and linearly independent. Orthogonality only refers to the angle between two vectors, while linear independence refers to the fact that one vector cannot be expressed as a scalar multiple of the other.

4. How is orthogonality used in linear algebra?

Orthogonality is an important concept in linear algebra and is used in many applications, such as solving systems of linear equations, finding the best fit line for a set of data, and performing transformations on vectors.

5. Can a linear combination of orthogonal vectors be non-orthogonal?

Yes, a linear combination of orthogonal vectors can be non-orthogonal. This can happen if the coefficients used in the linear combination are not chosen carefully. However, if the coefficients are chosen correctly, the result will still be orthogonal.

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