Finding an orthogonal complement without an explicitly defined inner product

In summary, the Gram Schmidt process gives us a set of orthonormalized vectors, and the orthogonal complement of a subspace is the set of all vectors orthogonal to that subspace.
  • #1
Idioteqnician
3
0

Homework Statement



P5 is an inner product space with an inner product. We applied the Gram Schmidt process to the basis {1,x,x^2,x^3,x^4} and obtained the following result. {f1,f2,f3,f4,x^4+2}

What is the orthogonal complement of P3 in P5 with respect to this inner product?

Homework Equations



http://tutorial.math.lamar.edu/Classes/LinAlg/OrthonormalBasis.aspx

has everything on the gram schmidt process

Definition of orthogonal complement:
Suppose that W is a subspace of an inner product space V. We say that a vector u from V is orthogonal to W if it is orthogonal to every vector in W. The set of all vectors that are orthogonal to W is called the orthogonal complement of W.

The Attempt at a Solution



I'm not really sure. I feel like I need a defined inner product to actually find which vectors are orthogonal. I feel like I need to do something with the x^4+2, but honestly I am entirely lost.
 
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  • #2
Of course you need to know what the inner product is to say what is orthogonal. They must have told you at some point.
 
  • #3
Nope, I copied this question straight from my homework page. We are given the result of the Gram Schmidt process, which is a set of orthonormalized vectors. Considering the orthogonal complement is the set of all vectors orthogonal to some subspace W, does that mean my answer is just a linear combination of the given result of the gram schmidt process? They are orthogonal vectors, and the only way I could think to get every orthogonal vector from that information would be to linearly combine them.
 
  • #4
Ok, maybe I'm beginning to see what the question is. So f1 is a constant term, f2 has the form a+bx, f3 is a quadratic, d+e*x+f*x^2 etc. Does that give you a hint?
 
  • #5
I think so. Thank you
 

FAQ: Finding an orthogonal complement without an explicitly defined inner product

1. What is an orthogonal complement?

An orthogonal complement is a subspace that is perpendicular to another subspace. This means that every vector in the orthogonal complement is orthogonal (perpendicular) to every vector in the original subspace.

2. How is an orthogonal complement related to an inner product?

An inner product is a mathematical operation that takes two vectors as inputs and produces a scalar as an output. The concept of an orthogonal complement is closely related to the inner product because the orthogonal complement of a subspace is the set of all vectors that have an inner product of 0 with every vector in the original subspace.

3. Can an orthogonal complement be found without an explicitly defined inner product?

Yes, an orthogonal complement can be found without an explicitly defined inner product. This can be done by using the Gram-Schmidt process, which is a method for finding an orthogonal basis for a given subspace. The vectors in this orthogonal basis can then be used to construct the orthogonal complement.

4. What are some applications of finding an orthogonal complement?

Finding an orthogonal complement has many applications in mathematics and science. For example, in linear algebra, the concept of orthogonal complements is used to solve systems of linear equations and find the best fit for data sets. In quantum mechanics, orthogonal complements are used to describe the physical states of particles. They are also used in signal processing and image compression techniques.

5. Are there any limitations to finding an orthogonal complement without an explicitly defined inner product?

One limitation of finding an orthogonal complement without an explicitly defined inner product is that it may not be unique. This means that there may be multiple sets of vectors that can be used to construct the orthogonal complement, making it difficult to determine which one is the "correct" complement. Additionally, the Gram-Schmidt process may be computationally intensive for larger vector spaces, making it less practical for certain applications.

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