'Givens' Matrix rotations and QR Factorisation

In summary, the conversation discusses the concept of Givens rotations and how they can be used to calculate the QR factorization of a matrix. The first part explains that by using Givens rotations, the matrix can be made upper-triangular with at most \frac{1}{2}n(n-1)-\sum k(i) rotations, where k(i) is the number of zero elements in the i-th row of the matrix. The second part discusses the specific case of an upper-triangular matrix with nonzero elements in its first column and suggests using a specific order of Givens rotations to shift the elements in the first column below the diagonal. However, it is uncertain whether this approach will work and further clarification is
  • #1
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Hey there all!

I'm a little confused by the concept of Givens rotations and was hoping someone could help elaborate a little bit with the following problem for me - if i could get some help understanding how to approach the problem it would be a really great help to me.

'Let A be an n x n matrix, and for i = 1, 2, ..., n let k(i) be the number of zero elements in the i-th row of A that come before all nonzero elements in this row and before the diagonal element [tex]a_{ii}[/tex]. Show that the QR factorization of A can be calculated by using at most [tex]\frac{1}{2}n(n-1)-\sum k(i)[/tex] Givens rotations. Hence show that, if A is an upper triangular matrix except that there are nonzero elements in its first column, i.e. [tex]a_{ij} = 0[/tex] when [tex]2 \leq k < i \leq n[/tex], then its QR factorisation can be calculated using only 2n-3 Givens rotations: you should try finding the order of the first (n-2) rotations that brings your matrix to the form considered above.'

Now the first part seems almost too obvious for me to be understanding it properly; I know that there always exists a Givens rotation to zero out any element we choose in A, and so essentially the problem comes down to making the matrix upper-triangular by removing all the non-zero elements below the diagonal; there are [tex]n(n-1)/2[/tex] elements down there in total, and at least [tex]\sum k(i)[/tex] of those are zero, so at most [tex]\frac{1}{2}n(n-1)-\sum k(i)[/tex] non-zero elements below the diagonal, so then at most that many rotations, right? I'm not sure if perhaps the problem lies in showing that by zeroing out one element, we don't make a previously zeroed element nonzero (or is that not necessarily true?), or if I'm missing something else about the problem. I don't feel like I've actually done anything mathematical rather than simply heuristic so far with the problem, which does concern me.

For the second part, it seems logical that we want to use our Givens rotations to maximize the k(i) in each row, in order to minimize the overall sum - in which case presumably we want each of our n-1 nonzero elements below the diagonal to be '''immediately''' below the diagonal (as one of the elements in the column already is), so is it simply a case of shifting the other n-2 elements in the first column so they're below the diagonal by using some specific order of Givens rotations? In which case, I suspect the rotations I need to use are the ones which simply switch two rows (since we're left multiplying by the rotation matrices; the matrices I'm referring to are the ones corresponding to the elementary row operation of row-switching). My thoughts are to shift every row down one position, which effectively shifts our diagonal one place lower, except for the very top-row which is now empty except for the first and last elements; still, that doesn't really seem to solve my problem, because no matter what way I permute the rows, there will always be an element in the left-hand and right-hand column, as is the case for every row. If I could switch columns around, then I think this approach would work, but that would correspond to right-multiplication by the Givens rotation matrices, and I'm fairly sure that wouldn't give you a 'QR' factorisation - I'm very confused!

I'd hugely appreciate any light you could shed on the problem - I'm not a huge fan of numerical analysis and I just want to get this work done and get on to something like complex analysis, far more enjoyable! :) Thanks to all very much in advance, and I'll try and respond to everything ASAP if I can get to a computer.
 
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  • #2
Any thoughts, anyone? :)
 

Related to 'Givens' Matrix rotations and QR Factorisation

1. What is a 'Givens' matrix rotation?

A 'Givens' matrix rotation is a mathematical operation used to rotate a vector or a matrix in a specified direction. It is used in linear algebra for various purposes, such as changing the orientation of a coordinate system or performing a QR factorization.

2. What is the purpose of QR factorization?

QR factorization is a matrix decomposition technique that breaks down a matrix into two components: an orthogonal matrix (Q) and an upper triangular matrix (R). This is useful for solving systems of linear equations, calculating eigenvalues and eigenvectors, and many other applications in mathematics and science.

3. How are 'Givens' matrix rotations and QR factorization related?

'Givens' matrix rotations are often used as a part of the QR factorization process. The rotations are applied to the matrix in order to eliminate certain elements and create a triangular matrix, which can then be easily factored into Q and R components.

4. What are the benefits of using QR factorization?

QR factorization has several benefits, including making it easier to solve systems of linear equations, providing a more stable method for calculating eigenvalues and eigenvectors, and reducing the computational complexity of certain matrix operations.

5. Are there any limitations to using 'Givens' matrix rotations and QR factorization?

One limitation of using 'Givens' matrix rotations is that they can be computationally expensive for larger matrices. Additionally, QR factorization can only be applied to square matrices, so it may not be useful for certain types of data in science and engineering applications.

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