QR factorization of a n x 1 matrix

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SUMMARY

The discussion focuses on the QR factorization of an n x 1 matrix, specifically addressing the reduced QR factorization of a vector a. The matrices \(\hat{Q}\) and \(\hat{R}\) are explicitly defined, with \(\hat{Q} = \left[\frac{\mathbf{a_1}}{\left\|\mathbf{a_1}\right\|}\right]\) and \(\hat{R} = \left[\left\|\mathbf{a_1}\right\|\right]\). The solution to the linear least squares problem \(ax \approx b\) is also discussed, emphasizing the simplicity of the case due to the matrix's dimensions. The participant expresses confusion due to a lack of recent experience with linear algebra concepts.

PREREQUISITES
  • Understanding of QR factorization
  • Familiarity with the Gram-Schmidt process
  • Knowledge of linear algebra concepts, particularly least squares
  • Basic proficiency in matrix operations
NEXT STEPS
  • Study the Gram-Schmidt process in detail
  • Learn about the implications of QR factorization in solving linear least squares problems
  • Explore applications of QR factorization in numerical methods
  • Review linear algebra fundamentals, focusing on matrix norms and operations
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Students preparing for linear algebra exams, educators teaching QR factorization, and anyone seeking to understand the application of QR factorization in solving linear least squares problems.

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Homework Statement


Consider the vector a as an n × 1 matrix.

A) Write out its reduced QR factorization, showing the matrices [itex]\hat{Q}[/itex] and [itex]\hat{R}[/itex] explicitly.

B) What is the solution to the linear least squares problem ax ≃ b where b is a given n-vector.


Homework Equations


I was using the equation from 1.1 (http://www.math.ucla.edu/~yanovsky/Teaching/Math151B/handouts/GramSchmidt.pdf) to help me solve this problem.

The Attempt at a Solution


I haven't taken linear algebra for about 2 years and this is kind of hazy. I'm really confused here, and I really don't know where to start. I know that I'm supposed to come to this website with some sort of progress, but I'm really confused here.
 
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So, the first step in the Gram-Schmidt process is to think of the matrix as being a row of column vectors. Since your matrix is n x 1, it's like having a matrix of only one column vector. Thus [itex]A = [\mathbf{a_1}][/itex] in this case. So, going by the pdf you provided, let [itex]\mathbf{u_1} = \mathbf{a_1}[/itex] and then
[itex]\mathbf{e_1} = \frac{\mathbf{u_1}}{\left\| \mathbf{u_1} \right\|}[/itex] [itex]= \frac{\mathbf{a_1}}{\sqrt{(a_{11})^2+(a_{21})^2+...+(a_{n1}^2)}}[/itex].

In this case, [itex]Q = [\frac{ \mathbf{a_1} }{\left\| \mathbf{a_1} \right\|}][/itex] and R is the 1x1 matrix
[itex][ \mathbf{a_1} \bullet (\frac{ \mathbf{a_1} }{\left\| \mathbf{a_1} \right\|}) ][/itex] = [itex][ \frac{ \mathbf{a_1} \bullet \mathbf{a_1} }{ \left\| \mathbf{a_1} \right\| } ][/itex] = [itex][\frac{(\left\| \mathbf{a_1} \right\|)^2}{\left\| \mathbf{a_1} \right\|}] = [\left\|\mathbf{a_1}\right\|][/itex].

Then, [itex]QR = [ \frac{\mathbf{a_1}}{\left\|\mathbf{a_1}\right\|} \left\|\mathbf{a_1}\right\| ] = [\mathbf{a_1}][/itex].

In this case, the result isn't very interesting, because it's an nx1 matrix. But I think that's also to (supposedly) make it easier for you. I can see how in this case it made it even more confusing.
 
Thank you so much for your help. I have an exam in this class in one week, so I will be referring back to this when studying.
 

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