How Does the Gram-Schmidt Procedure Orthonormalize a Complex Vector Basis?

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In summary, the Gram-Schmidt procedure can be used to orthonormalize a basis in three-space. The process involves normalizing the first vector, finding the projection of the second vector onto the first and subtracting it, then finding the projection of the third vector onto the first and second and subtracting those, and finally normalizing the resulting vectors to obtain an orthonormal basis. It is important to explain each step and double check calculations for accuracy.
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Homework Statement



Use the Gram-Schmidt procedure to orthonormalize the three-space basis [itex]\left|e_{1}\right\rangle = (1 + i) \widehat{i} + (1) \widehat{j} + (i) \widehat{k}, \left|e_{2}\right\rangle = (i) \widehat{i} + (3) \widehat{j} + (1) \widehat{k}, \left|e_{3}\right\rangle = (0) \widehat{i} + (28) \widehat{j} + (0) \widehat{k}.[/itex]

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The Attempt at a Solution



1. To obtain the first orthonormal basis vector, normalise [itex]\left|e_{1}\right\rangle[/itex]. Therefore, [itex] \left|e^{'}_{1}\right\rangle = (1/2 + i/2) \widehat{i} + (1/2) \widehat{j} + (i/2) \widehat{k}[/itex].

2. The projection of [itex]\left|e_{2}\right\rangle[/itex] along [itex]\left|e^{'}_{1}\right\rangle[/itex] = the scalar multiplication of [itex]\left|e^{'}_{1}\right\rangle[/itex] with the inner product of [itex]\left|e^{'}_{1}\right\rangle[/itex] and [itex]\left|e_{2}\right\rangle[/itex] = [itex] [(1/2 - i/2)(i) + (1/2)(3) + (-i/2)(1)] [(1/2 + i/2) \widehat{i} + (1/2) \widehat{j} + (i/2) \widehat{k}] = (1 + i) \widehat{i} + (1) \widehat{j} + (i) \widehat{k}[/itex].

Subtract the projection from [itex]\left|e_{2}\right\rangle[/itex] and normalise to obtain [itex] \left|e^{'}_{2}\right\rangle = (-1/\sqrt{7}) \widehat{i} + (2/\sqrt{7}) \widehat{j} + ( (1 - i)/\sqrt{7} ) \widehat{k}[/itex]

3. The projection of [itex]\left|e_{3}\right\rangle[/itex] along [itex]\left|e^{'}_{1}\right\rangle[/itex] = the scalar multiplication of [itex]\left|e^{'}_{1}\right\rangle[/itex] with the inner product of [itex]\left|e^{'}_{1}\right\rangle[/itex] and [itex]\left|e_{3}\right\rangle[/itex] = [itex] [0 + 14 + 0] [(1/2 + i/2) \widehat{i} + (1/2) \widehat{j} + (i/2) \widehat{k}] = (7 + 7i) \widehat{i} + (7) \widehat{j} + (7i) \widehat{k}[/itex].

The projection of [itex]\left|e_{3}\right\rangle[/itex] along [itex]\left|e^{'}_{2}\right\rangle[/itex] = the scalar multiplication of [itex]\left|e^{'}_{2}\right\rangle[/itex] with the inner product of [itex]\left|e^{'}_{2}\right\rangle[/itex] and [itex]\left|e_{3}\right\rangle[/itex] = [itex] [0 + 56/\sqrt{7} + 0] [(-1/\sqrt{7}) \widehat{i} + (2/\sqrt{7}) \widehat{j} + ( (1 - i)/\sqrt{7} ) \widehat{k}] = (-8) \widehat{i} + (16) \widehat{j} + (8 - 8i) \widehat{k}[/itex].

Subtract the projections from [itex]\left|e_{3}\right\rangle[/itex] and normalise to obtain [itex] \left|e^{'}_{3}\right\rangle = ( (1 - 7i)/\sqrt{130}) \widehat{i} + (5/\sqrt{130}) \widehat{j} + ( (i - 8)/\sqrt{130} ) \widehat{k}[/itex].

I would be grateful if you could please provide comments.
 
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Your solution looks correct! It is clear that you have a good understanding of the Gram-Schmidt procedure and how to apply it to orthonormalize a basis. One suggestion I have is to include a brief explanation of each step in your solution, as this can help others who may not be as familiar with the process. Also, make sure to double check your calculations to ensure they are accurate. Overall, great job on your solution!
 

FAQ: How Does the Gram-Schmidt Procedure Orthonormalize a Complex Vector Basis?

What is the Gram-Schmidt procedure?

The Gram-Schmidt procedure is a mathematical algorithm used to convert a set of linearly independent vectors into an orthonormal set of vectors. It is commonly used in linear algebra and signal processing.

What is the purpose of the Gram-Schmidt procedure?

The purpose of the Gram-Schmidt procedure is to create an orthonormal basis for a vector space. This is useful in many applications, including solving systems of linear equations, calculating projections, and finding eigenvalues and eigenvectors.

How does the Gram-Schmidt procedure work?

The Gram-Schmidt procedure involves a series of steps that transform a set of linearly independent vectors into an orthonormal set. This is done by finding the orthogonal projection of each vector onto the subspace spanned by the previously transformed vectors and then normalizing the resulting vector.

What are the applications of the Gram-Schmidt procedure?

The Gram-Schmidt procedure has numerous applications in mathematics, engineering, and other fields. It is commonly used in signal processing, image processing, data compression, and solving systems of linear equations. It is also used in computer graphics, finite element analysis, and machine learning.

What are the limitations of the Gram-Schmidt procedure?

The Gram-Schmidt procedure can be numerically unstable, meaning that small errors in the input vectors can lead to large errors in the output. It also does not work for sets of linearly dependent vectors, as they cannot be transformed into an orthonormal set. Additionally, it is not efficient for large matrices, as it involves multiple computations for each vector in the set.

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