QR factorization of a n x 1 matrix

In summary, the conversation discusses finding the reduced QR factorization for a vector as an n x 1 matrix and the solution to the linear least squares problem using the Gram-Schmidt process. The result for the reduced QR factorization is an nx1 matrix, making it simpler to solve.
  • #1
abajaj2280
2
0

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.
 
Physics news on Phys.org
  • #2
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.
 
  • #3
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.
 

Related to QR factorization of a n x 1 matrix

What is QR factorization of a n x 1 matrix?

QR factorization is a method used to decompose a matrix into an orthogonal matrix (Q) and an upper triangular matrix (R). In the case of a n x 1 matrix, the matrix is decomposed into a 1 x 1 orthogonal matrix and a 1 x 1 upper triangular matrix.

Why is QR factorization useful?

QR factorization is useful in many applications, such as solving systems of linear equations, least squares problems, and eigenvalue problems. It is also used in data compression and signal processing.

What is the process for performing QR factorization?

The process for performing QR factorization involves finding the QR decomposition of the matrix, which can be done using the Gram-Schmidt process or the Householder transformation. Once the orthogonal matrix (Q) and upper triangular matrix (R) are found, they can be multiplied together to reconstruct the original matrix.

What are the benefits of QR factorization over other methods?

QR factorization is numerically stable and can handle ill-conditioned matrices, making it a more reliable method for solving systems of equations. It also allows for efficient computation of eigenvalues and eigenvectors.

Are there any limitations to QR factorization?

QR factorization can be computationally expensive for large matrices, so other methods may be preferred in those cases. It also requires the matrix to be square, which may not always be the case in certain applications.

Similar threads

  • Calculus and Beyond Homework Help
Replies
4
Views
2K
  • Calculus and Beyond Homework Help
Replies
14
Views
612
  • Calculus and Beyond Homework Help
Replies
3
Views
580
  • Calculus and Beyond Homework Help
Replies
2
Views
3K
  • Calculus and Beyond Homework Help
Replies
10
Views
1K
  • Calculus and Beyond Homework Help
Replies
1
Views
1K
  • Calculus and Beyond Homework Help
Replies
15
Views
2K
  • Calculus and Beyond Homework Help
Replies
3
Views
568
  • Calculus and Beyond Homework Help
Replies
1
Views
3K
  • Calculus and Beyond Homework Help
Replies
12
Views
2K
Back
Top