How do you find the coordinates of a polynomial in terms of an orthogonal basis?

In summary: Then use the change of basis matrix to map these coefficients to the new basis ##S'##. This will give you the coordinates of ##1 + x + x^2## in terms of the orthogonal basis ##S'##. In summary, to find the coordinates of ##x^2 + x + 1## with respect to the orthogonal set of given set ##S = \{1, x, x^2\}##, use the change of basis matrix to map the coefficients of ##x^2 + x + 1## in terms of ##S## to the new basis ##S'##. This will give you the coordinates of ##x^2 + x + 1## in terms of the orthogonal basis ##
  • #1
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Homework Statement


Given ##S = \{1, x, x^2\}##, find the coordinates of ##x^2 + x + 1## with respect to the orthogonal set of S.

Homework Equations


Inner product on polynomial space:
##<f,g> = \int_{0}^{1} fg \textrm{ } dx##

The Attempt at a Solution


I used Gram-Schmidt to make ##S## orthogonal and got ##S' = \{1, x - \frac{1}{2}, x^2 - x + \frac{1}{6}\}##.

So the change of basis matrix I got was $$ \left( \begin{array}{ccc}
1 & \frac{1}{2} & \frac{1}{3} \\
0 & 1 & 1 \\
0 & 0 & 1 \end{array} \right)$$

But ##x^2 + x + 1## looks exactly like ##S##, so it would seem like it's the identity matrix so then it wouldn't change anything, which is where I'm stuck.
 
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  • #2
The problem says "with respect to the orthogonal set of S",and because S is not an orthogonal set,so you should find the components in S' basis.So forget about S and calculate the components w.r.t. S' directly!
Also,[itex] x^2+x+1 [/itex] doesn't look exactly like S,only its coordinates w.r.t. S is (1,1,1).
 
  • #3
Think about what the change of basis matrix does for you. Consider, for example, the polynomial ##x##. Expressed in terms of the original basis ##S##, we can write ##x = 0(1) + 1(x) + 0(x^2)##, so its coefficient vector in terms of the original basis is ##(\begin{array}{ccc}0& 1& 0\end{array})^T##. To express it in terms of the new basis, we simply multiply the matrix by this vector:
$$\text{new coefficient vector} = \left( \begin{array}{ccc}
1 & \frac{1}{2} & \frac{1}{3} \\
0 & 1 & 1 \\
0 & 0 & 1 \end{array} \right)
\left( \begin{array}{c} 0 \\ 1 \\ 0 \end{array}\right) = \left( \begin{array}{c} \frac{1}{2}\\ 1 \\0\end{array} \right)$$
This means that our polynomial ##x## can be expressed in terms of the orthogonal basis using the new coefficients:
$$x = \frac{1}{2}(1) + 1\left(x-\frac{1}{2}\right) + 0\left(x^2 - x + \frac{1}{6}\right)$$
and we can easily see that the left hand side does indeed equal the right hand side.

You can use exactly the same technique for any other polynomial. Start by finding the coefficients of ##1 + x + x^2## in terms of the original basis ##S##.
 

FAQ: How do you find the coordinates of a polynomial in terms of an orthogonal basis?

What is an inner product change of basis?

An inner product change of basis is a mathematical concept that involves changing the basis of a vector space while preserving the inner product between vectors. This can be thought of as a change in perspective or coordinate system without altering the relationships between vectors.

Why is inner product change of basis important?

Inner product change of basis is important because it allows us to simplify calculations and understand geometric relationships between vectors in different coordinate systems. It also helps with solving problems in areas such as linear algebra, quantum mechanics, and signal processing.

What is the formula for inner product change of basis?

The formula for inner product change of basis is <u, v> = [u]ATB[v], where u and v are vectors in the original basis, A is the change of basis matrix from the original basis to the new basis, and B is the change of basis matrix from the new basis to the original basis.

How do you perform inner product change of basis?

To perform inner product change of basis, you need to first determine the change of basis matrices from the original basis to the new basis and from the new basis to the original basis. Then, you can use the formula <u, v> = [u]ATB[v] to calculate the inner product between two vectors in different coordinate systems.

What are some real-world applications of inner product change of basis?

Inner product change of basis has many real-world applications, such as image and signal processing, where it is used to transform data into different coordinate systems for easier analysis. It is also used in quantum mechanics to study the behavior of particles in different states and in machine learning to classify and analyze data in different feature spaces.

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