Inner product orthogonal vectors

In summary, the problem asks for finding two unit vectors that are orthogonal to three given vectors in R4 with the Euclidean inner product. The attempt at a solution involved setting up three sets of linear equations, but the resulting solution was not a unit vector. After correcting an error and solving again, the correct solution should have components with 11's in the denominators.
  • #1
derryck1234
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0

Homework Statement



Let R4 have the Euclidean inner product. Find two unit vectors that are orthogonal to the three vectors

u = (2, 1, -4, 0) ; v = (-1, -1, 2, 2) ; w = (3, 2, 5, 4)

Homework Equations



<u, v> = u1v1 + u2v2 + u3v3 + u4v4 = 0 {orthogonal}

The Attempt at a Solution



There is no example in the textbook for this kind of problem.

What I thought of doing was making three sets of linear equations. By letting a orthogonal vector be = (x, y, z, w), therefore:

2x + y - 4z = 0
-x -y + 2z + 2w = 0
3x + 2y + 5z + 4w = 0

The general solution to which I found to be:

t(-310/3, 4/3, -154/3, 1)

This does not agree with the back of the textbook?
 
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  • #2
That might be because what you wrote is not a unit vector (which is what the problem asks for). [That is to say, t is not completely arbitrary...]

Now that I check, you'll find that your components do not solve the second and third of your linear equations (especially not the third one!).

[Small hint: you should be getting 11's in your denominators.]
 
Last edited:

Related to Inner product orthogonal vectors

1. What is an inner product?

An inner product is a mathematical operation that takes two vectors as input and produces a scalar as output. It is used to measure the angle between two vectors and is an important concept in linear algebra and functional analysis.

2. What does it mean for two vectors to be orthogonal?

Two vectors are orthogonal if they are perpendicular to each other, meaning that the angle between them is 90 degrees. This is equivalent to the dot product (or inner product) of the two vectors being equal to 0.

3. How do you calculate the inner product of two vectors?

The inner product of two vectors, denoted as a · b, is calculated by multiplying the corresponding components of the two vectors and then summing the products. In other words, a · b = a1b1 + a2b2 + ... + anbn, where a and b are n-dimensional vectors.

4. Why are orthogonal vectors important?

Orthogonal vectors are important because they provide a convenient way to express and analyze complex vectors. They also have many practical applications, such as in signal processing, data compression, and pattern recognition.

5. Can a set of vectors be both orthogonal and linearly independent?

Yes, a set of vectors can be both orthogonal and linearly independent. In fact, an orthogonal set of vectors is always linearly independent, meaning that none of the vectors can be expressed as a linear combination of the others. This property makes orthogonal vectors useful for solving systems of linear equations.

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