Can someone explain this example ? (Gram-schmidt Orthago

In summary, the bits in red are calculated using Integration, specifically using the formula for finding the area under a curve. The first circle represents the integration of t times 1, from 0 to 1, which is equal to \frac{t^2}{2}|_0^1=\frac{1}{2}. The rest of the circles follow the same process, with the first term representing the denominator and the second term representing the numerator.
  • #1
sid9221
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http://dl.dropbox.com/u/33103477/4.png

Can someone explain to me how the bits in red are calculated with Integration, the examples are doing my head in and it would be really useful if a human could show me how they are getting the values cause mine are different.
 
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  • #2
For example, the first circle is the integration of t times 1, from 0 to 1, which is equal to [itex]\frac{t^2}{2}|_0^1=\frac{1}{2}[/itex]. The rest are the same
 
  • #3
Here's a bit more detail:
\begin{align*}
\langle 1, 1 \rangle &= \int_0^1 1\cdot 1 \, dt = t\big|_0^1 = 1 \\
\langle t, 1 \rangle &= \int_0^1 t\cdot 1 \, dt = \left. \frac{t^2}{2}\right|_0^1 = \frac{1}{2}
\end{align*} Those are, respectively, the denominator and numerator in the first term you circled.
 

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 signal processing to simplify calculations and make them more accurate.

2. How does the Gram-Schmidt Orthogonalization process work?

The process involves taking a set of linearly independent vectors and calculating a new set of orthogonal vectors by subtracting the projection of each vector onto the space spanned by the previously processed vectors. This results in a set of orthogonal vectors that are perpendicular to each other and have the same span as the original set.

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

Orthogonal vectors are perpendicular to each other, meaning their dot product is equal to zero. Orthonormal vectors are not only orthogonal, but they also have a magnitude of 1, making them unit vectors. The Gram-Schmidt Orthogonalization process transforms a set of linearly independent vectors into a set of orthogonal vectors, but to make them orthonormal, we need to divide each vector by its magnitude.

4. Why is the Gram-Schmidt process important?

The Gram-Schmidt process is important because it allows us to transform a set of linearly independent vectors into a set of orthogonal vectors, which simplifies many calculations in linear algebra and signal processing. It also helps improve the accuracy of these calculations since orthogonal vectors are easier to work with and can reduce round-off errors.

5. Are there any limitations to the Gram-Schmidt Orthogonalization process?

While the Gram-Schmidt process is a powerful tool, it does have some limitations. It can only be applied to a set of linearly independent vectors and may not work well with vectors that are close to being linearly dependent. It also has some numerical stability issues, which can be addressed by using modified versions of the algorithm.

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