Orthagonal vectors/vector spaces

  • Thread starter Dell
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In summary, we found that the group W in R4 is defined as the set of all vectors that are orthogonal to the vectors v1(1 0 1 0) and v2(1 2 1 1). To find a basis and dimension of W, we set up equations using the dot product and found that W can be spanned by the vectors (1 0 -1 0) and (0 -2 0 1), and the dimension of W is 2. Then, to find a vector in W that is orthogonal to (1 -2 1 3), we solved for the parameters and found that any vector of the form (t 0 -t 0) would fit
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Dell
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givem,-W is the group of all the vectors in R4 which are orthagonal to the vectors v1(1 0 1 0) and also v2(1 2 1 1)
a) find a basis and the dimention of W
b) find, in W, a vector which is orthagonal to the vector (1 -2 1 3)

a)

i call this vector w=(a b c d) and say
v1(dot)w=0
v2(dot)w=0

therefore

a+0b+c+0d=0 a=-c
a+2b+c+d=0 d=-2b

i will have 2 free parameters here, 4 unknows -rank2=2
a=t
d=u

so w=(t, -2u, -t, u)

W=sp{(1 0 -1 0), (0 -2 0 1)}
dim(w)=2
----------------------------
b)

(t, -2u, -t, u) dot (1 -2 1 3)=0

t+4u-t+3u=0
7u=0
u=0

therefore any vector that fits
(t 0 -t 0) is orthagonal to (1 -2 1 3) and a part of W


is this all correct
 
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Looks good.
 

1. What are orthogonal vectors?

Orthogonal vectors are two or more vectors that are perpendicular to each other, meaning they form a 90 degree angle when plotted on a graph. This also 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: v1 · v2 = v1x * v2x + v1y * v2y + v1z * v2z, where v1 and v2 are the two vectors. If the dot product is equal to 0, then the vectors are orthogonal.

3. What is an orthogonal basis?

An orthogonal basis is a set of vectors that are orthogonal to each other and can be used to form any vector in the vector space. This means that the vectors are linearly independent and can be used as a basis to represent any vector in the space.

4. How is orthogonality used in vector spaces?

Orthogonality is used in vector spaces to simplify calculations and solve problems involving multiple vectors. It allows for easier visualization and manipulation of vectors, making it a useful tool in various fields such as physics, engineering, and mathematics.

5. What are some real-world applications of orthogonality in science?

Orthogonality has many applications in science, including image and signal processing, data compression, and quantum mechanics. It is also used in computer graphics to create three-dimensional objects and in machine learning algorithms to classify data. Additionally, orthogonal vectors are used in physics to represent forces and motion in multiple dimensions.

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