Gram-Schmidt procedure to find orthonormal basis

In summary, the four functions form a basis for the vector space of polynomials of degree 3. The Gram-Schmidt procedure is used to find an orthonormal basis with respect to the inner product <f, g>. These four functions can be used to calculate the ui's from the equationui = vi - \sumi-1j <vi, uj>/||vj||2>. Additionally, the scalar multiplication and vector addition of the four functions are the same as usual for the polynomials.
  • #1
MellyC
6
0

Homework Statement



The four functions v0 = 1; v1 = t; v2 = t^2; v3 = t^3 form a basis for the vector space of
polynomials of degree 3. Apply the Gram-Schmidt procedure to find an orthonormal basis with
respect to the inner product: < f ; g >= (1/2)[itex]\int[/itex] 1-1 f(t)g(t) dt

Homework Equations



ui = vi - [itex]\sum[/itex]i-1j <vi, uj>/||vj||2> *vj

The Attempt at a Solution



I am not sure that the impact that given inner product integral gives to the question. I don`t even know how to approach this question as well, because I have typically been given vectors of the form (x1, y1, z1) to use gram-schmidt orthonormalization with, not of the given form.
 
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  • #2
MellyC said:

Homework Statement



The four functions v0 = 1; v1 = t; v2 = t^2; v3 = t^3 form a basis for the vector space of
polynomials of degree 3. Apply the Gram-Schmidt procedure to find an orthonormal basis with
respect to the inner product: < f ; g >= (1/2)[itex]\int[/itex] 1-1 f(t)g(t) dt


Homework Equations



ui = vi - [itex]\sum[/itex]i-1j <vi, uj>/||vj||2> *vj

The Attempt at a Solution



I am not sure that the impact that given inner product integral gives to the question. I don`t even know how to approach this question as well, because I have typically been given vectors of the form (x1, y1, z1) to use gram-schmidt orthonormalization with, not of the given form.

What is stopping you from using the formula in (2) with the inner product as defined in (1)?

RGV
 
  • #3
How would I go about doing that? I'm a little bit confused about what the relationship between f(t), g(t), vi and ui is in this case. I understand that I can put the integral from part 1 into the inner product for the gram-schmidt orthogonalization in part 2, but what would my f(t) and g(t) represent?
 
  • #4
MellyC said:
How would I go about doing that? I'm a little bit confused about what the relationship between f(t), g(t), vi and ui is in this case. I understand that I can put the integral from part 1 into the inner product for the gram-schmidt orthogonalization in part 2, but what would my f(t) and g(t) represent?

They would be whatever two functions whose inner product you want.

RGV
 
Last edited:
  • #5
Welcome to PF, MellyC! :smile:

Let's take the first two as examples:

[tex]u_0 = v_0[/tex]
[tex]u_1 = v_1 - \sum_{j=0}^{1-1} {<v_1, u_j> \over ||v_j||^2} \cdot v_j[/tex]


So:
[tex]u_0 = 1[/tex]
[tex]u_1 = t - {<t, 1> \over ||1||^2} \cdot 1[/tex]
with:
[tex]<t, 1> = {1 \over 2} \int_{-1}^1 t \cdot 1 dt[/tex]
[tex]||1||^2 = <1, 1> = {1 \over 2} \int_{-1}^1 1 \cdot 1 dt[/tex]


Can you calculate u1 from this?
And u2 and u3?
 
  • #6
MellyC said:
I am not sure that the impact that given inner product integral gives to the question. I don`t even know how to approach this question as well, because I have typically been given vectors of the form (x1, y1, z1) to use gram-schmidt orthonormalization with, not of the given form.
If you really want to, you can look at the polynomials like that. A polynomial f(t) is a linear combination of your given basis vectors:
[tex]f(t) = a_0 + a_1 t + a_2 t^2 + a_3 t^3 = a_0 \vec{v}_0 + a_1 \vec{v}_1 + a_2 \vec{v}_2 + a_3 \vec{v}_3[/tex]so f(t) corresponds to the coordinate vector (a0, a1, a2, a3) and vice versa. You could do your calculations using the 4-tuples (except the inner product since it involves integrating the functions) and then convert your answers back to polynomial form.

But you should be able to see that scalar multiplication of a 4-tuple is the same thing as the usual scalar multiplication of the polynomial. Similarly, vector addition of the 4-tuples is the same as adding the two polynomials the way you normally do. So there's really no reason to use the 4-tuples instead of the polynomials directly in the calculations. They're just different ways of writing the same thing.
 

What is the Gram-Schmidt procedure?

The Gram-Schmidt procedure is a mathematical technique used to transform a set of linearly independent vectors into a set of orthonormal vectors. This procedure is commonly used in linear algebra and is named after its inventors, Jørgen Pedersen Gram and Erhard Schmidt.

Why is the Gram-Schmidt procedure important?

The Gram-Schmidt procedure is important because it allows us to find an orthonormal basis for a vector space. This is useful in many areas of mathematics, including solving systems of linear equations, optimization problems, and finding eigenvalues and eigenvectors.

What is an orthonormal basis?

An orthonormal basis is a set of vectors that are both orthogonal (perpendicular) and normalized (unit length). This means that each vector in the basis is perpendicular to all other vectors and has a length of 1. An orthonormal basis is important because it simplifies many mathematical calculations and allows us to easily represent vectors and perform operations on them.

Can any set of linearly independent vectors be transformed into an orthonormal basis using the Gram-Schmidt procedure?

Yes, any set of linearly independent vectors can be transformed into an orthonormal basis using the Gram-Schmidt procedure. However, it should be noted that this procedure may result in rounding errors, so the resulting orthonormal basis may not be perfectly orthogonal or normalized. In practical applications, it is important to consider the precision of the calculations and adjust accordingly.

Are there any limitations to using the Gram-Schmidt procedure?

One limitation of the Gram-Schmidt procedure is that it can only be used for finite-dimensional vector spaces. Additionally, it may not work for certain types of matrices, such as those with complex eigenvalues. In these cases, alternative methods such as the QR decomposition can be used to find an orthonormal basis.

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