Gram-Schmidt Orthogonalization Process

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In summary, the correct procedure for finding an orthogonal basis for ##\operatorname{span} (S)## is to first define ##v_1 = 1##, then calculate ##v_2 = \frac{u_2}{\| u_2 \|}## where ##u_2 = x - \langle x, v_1 \rangle v_1##, and finally calculate ##v_3 = \frac{u_3}{\| u_3 \|}## where ##u_3 = x^2 - \langle x^2, v_1 \rangle v_1 - \langle x^2, v_2 \rangle v_2##. This results in an orthogonal
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Mr Davis 97
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Homework Statement


Find an orthogonal basis for ##\operatorname{span} (S)## if ##S= \{1,x,x^2 \}##, and ##\langle f,g \rangle = \int_0^1 f(x) g(x) \, dx##

Homework Equations

The Attempt at a Solution


So we start by the normal procedure.

Let ##v_1 = 1##. Then ##\displaystyle v_2 = x - \frac{\langle x,1 \rangle}{\| 1 \|^2}(1) = x - \frac{1}{2}##.
Then ##\displaystyle v_3 = x^2 - \frac{\langle x^2,1 \rangle}{\| 1 \|^2}(1) - \frac{\langle x^2,x \rangle}{\| x \|^2}(x) = x^2 - \frac{1}{3} - \frac{3}{4}x##.

But this is not correct, because if I calculate ##\displaystyle \langle 1, x^2 - \frac{1}{3} - \frac{3}{4}x\rangle = \int_0^1 x^2 - \frac{1}{3} - \frac{3}{4}x \, dx = -\frac{3}{2} \ne 0##.

What am I doing wrong?
 
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Shouldn't the v3 definition use v2 = x-1/2 in the third term instead of x?
 
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Mr Davis 97 said:

Homework Statement


Find an orthogonal basis for ##\operatorname{span} (S)## if ##S= \{1,x,x^2 \}##, and ##\langle f,g \rangle = \int_0^1 f(x) g(x) \, dx##

Homework Equations

The Attempt at a Solution


So we start by the normal procedure.

Let ##v_1 = 1##. Then ##\displaystyle v_2 = x - \frac{\langle x,1 \rangle}{\| 1 \|^2}(1) = x - \frac{1}{2}##.
Then ##\displaystyle v_3 = x^2 - \frac{\langle x^2,1 \rangle}{\| 1 \|^2}(1) - \frac{\langle x^2,x \rangle}{\| x \|^2}(x) = x^2 - \frac{1}{3} - \frac{3}{4}x##.

But this is not correct, because if I calculate ##\displaystyle \langle 1, x^2 - \frac{1}{3} - \frac{3}{4}x\rangle = \int_0^1 x^2 - \frac{1}{3} - \frac{3}{4}x \, dx = -\frac{3}{2} \ne 0##.
_
What am I doing wrong?
Wrong normalization.
$$v_2=\frac{u_2}{|| u_2 ||}, \\
u_2 = x - <x, v_1> v_1 $$
 
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1. What is the Gram-Schmidt Orthogonalization Process?

The Gram-Schmidt Orthogonalization Process is a mathematical method used to transform a set of linearly independent vectors into a set of orthogonal vectors. This process is commonly used in linear algebra and is named after mathematicians Jørgen Pedersen Gram and Erhard Schmidt.

2. Why is the Gram-Schmidt Orthogonalization Process important?

The Gram-Schmidt Orthogonalization Process is important because it allows for the simplification of complex vector spaces and makes them easier to work with. It also has many applications in fields such as physics, engineering, and data science.

3. What is the difference between orthogonal and orthonormal vectors?

Orthogonal vectors are those that are perpendicular to each other, meaning their dot product is equal to 0. Orthonormal vectors are not only orthogonal, but they also have a magnitude of 1, making them unit vectors. The Gram-Schmidt Orthogonalization Process produces orthonormal vectors.

4. Are there any limitations to the Gram-Schmidt Orthogonalization Process?

Yes, there are limitations to the Gram-Schmidt Orthogonalization Process. It only works for linearly independent vectors, and it can be numerically unstable for highly correlated vectors. In these cases, other methods such as Householder reflections or Givens rotations may be more appropriate.

5. How is the Gram-Schmidt Orthogonalization Process related to the QR decomposition?

The Gram-Schmidt Orthogonalization Process is used to obtain the QR decomposition, which is a factorization of a matrix into an orthogonal matrix and an upper triangular matrix. The process involves applying Gram-Schmidt to the columns of the original matrix to obtain an orthonormal basis, which is then used to construct the orthogonal matrix in the QR decomposition.

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