Linear Algrebra- Orthogonal Vectors

In summary, the conversation discusses the following questions: (a) Whether any set of orthogonal vectors in Rn is linearly independent (b) Whether the equation ||cv|| = c||v|| is true for any vector v in Rn and scalar c in R (c) Whether the equation ||u+v||^2 + ||u-v||^2 = 2 ||u||^2 + 2||v||^2 holds for any vectors u, v in Rn Part a is believed to be true, but the proof is still unknown. Part b can be proven by using the definitions of the norm notation and comparing it with the properties of a norm. For part c, the relationship between the norm and inner
  • #1
dondraper5
5
0
I am having trouble with these questions-

Explain/prove whether:
(a) Any set {v1,v2,...vk} of orthogonal vectors in Rn is linearly independent.
(b) If there is a vector v in Rn and scalar c in R, we have ||cv|| = c||v||
(c) for any vectors u, v in Rn, ||u+v||^2 + ||u-v||^2 = 2 ||u||^2 + 2||v||^2

I think part a is true, but can't get around a way to prove it. Need help with b and c.
 
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  • #2
To show that vectors, [itex]\{v_1, v_2, v_3, \cdot\cdot\cdot, v_m\}[/itex] are independent, you must show that if [itex]a_1v_1+ a_2v_2+ a_3v_3+ \cdot\cdot\cdot+ a_mv_m[/itex] then [itex]a_1= a_2= a_3= \cdot\cdot\cdot= a_m= 0[/itex].

To show that take the dot product of [itex]a_1v_1+ a_2v_2+ a_3v_3+ \cdot\cdot\cdot+ a_mv_m[/itex] with each of [itex]v_1, v_2, v_3, \cdot\cdot\cdot, v_m[/itex] in turn.
 
  • #3
^ thank you, makes sense now.
 
  • #4
any ideas for b?
 
  • #5
dondraper5 said:
any ideas for b?
Use the definitions of the [itex]\|x\|[/itex] notation on both sides. If you're allowed to use that the map [itex]x\mapsto\|x\|[/itex] is a norm, you can just compare the equality you've been given with the properties of a norm. I recommend you do it both ways. (They're both very easy).

For c, you should look at the terms on the left, one at at a time:
[tex]
\begin{align}
&\|u+v\|^2=\cdots\\
&\|u-v\|^2=\cdots
\end{align}
[/tex] Think about the relationship between the norm and the inner product. Once you have used that, the rest will be easy.
 
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  • #6
^ thanks a lot
 
  • #7
Umm... (b) is not true...
 
  • #8
Right, but he said "explain/prove whether...", so he probably wasn't assuming that they were all true.
 

FAQ: Linear Algrebra- Orthogonal Vectors

What is the definition of orthogonal vectors?

Orthogonal vectors are defined as two vectors that are perpendicular to each other, meaning they form a 90 degree angle when plotted on a graph. This also means that the dot product of the two vectors is equal to zero.

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) and solve for the angle θ. If the angle between the two vectors is 90 degrees, then they are orthogonal. Alternatively, you can also check if the dot product of the two vectors is equal to zero.

Can orthogonal vectors be in different dimensions?

Yes, orthogonal vectors can exist in different dimensions. For example, in a 2-dimensional space, two vectors with components (3,0) and (0,4) are orthogonal. In a 3-dimensional space, two vectors with components (1,0,0) and (0,1,0) are also orthogonal.

How do orthogonal vectors relate to linear independence?

Orthogonal vectors are always linearly independent, meaning they cannot be expressed as a linear combination of each other. This is because orthogonal vectors have no shared components and are pointing in different directions, making it impossible to create one vector using the other.

What are some real-life applications of orthogonal vectors?

Orthogonal vectors have numerous applications in fields such as physics, engineering, and computer graphics. One example is the use of orthogonal vectors in 3-dimensional graphics to determine the direction of lighting and shading on objects. They are also used in signal processing to extract specific components from a signal. In physics, orthogonal vectors are used to represent forces acting on an object in different directions.

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