QR Fatoration (Linear Algebra) (FIXED)

In summary, if A is an matrix with linearly independent columns, it can be factored as A=QR, where Q is an matrix with orthonormal columns and R is an invertible upper triangular matrix. The elements of the diagonal of R are always positive because of the property that if i<j, then Uj and Xi are orthogonal. This leads to a triangular form for A, where the products (x1.u1) and (xk.uk) are always positive.
  • #1
leoh
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0

Homework Statement



Suppose that A is an matrix with linearly independent columns then A can be factored as,
where Q is an matrix with orthonormal columns and R is an invertible upper triangular matrix.

Show why it is Upper triangular matrix, and why the elements of the diagonal are Positive.

Homework Equations



A=QR
A= [x1 x2 ... xk]
R= [r1 r2 ... rk]

The Attempt at a Solution



(Sorry for my bad English, I am from Brazil)x1= (x1.u1)u1+(x1.u2)u2+...+(x1.uk)uk
x2= (x2.u1)u1+(x2.u2)u2+...+(x2.uk)uk
.
.
.
xn= (xn.u1)u1+(xn.u2)u2+...+(xn.uk)uk

now there is an argument, given Xi and Uj, if i<j , Uj and Xi are orthogonals. Which I do not know how to prove it.
So, if this is valid:

x1= (x1.u1)u1
x2= (x2.u1)u1+(x2.u2)u2
.
.
.
xk= (xk.u1)u1+(xk.u2)u2+...+(xk.uk)ukwhich is triangular. Now I do not now why the product (x1.u1), (xk.uk) is always positive.
 
Last edited:
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nobody?
 

1. What is QR factorization?

QR factorization is a matrix decomposition method in linear algebra that decomposes a matrix into two components: an orthogonal matrix (Q) and an upper triangular matrix (R). It is commonly used for solving linear systems of equations, least squares problems, and eigenvalue problems.

2. Why is QR factorization useful?

QR factorization is useful because it simplifies the process of solving linear systems of equations and other matrix problems. It can also improve the numerical stability of calculations, as it reduces the number of floating-point operations needed.

3. How is QR factorization different from LU factorization?

QR factorization is different from LU factorization in that it uses orthogonal matrices instead of lower and upper triangular matrices. Additionally, QR factorization can be applied to non-square matrices, while LU factorization can only be applied to square matrices.

4. What are the applications of QR factorization?

QR factorization has various applications in fields such as engineering, physics, and computer science. Some common applications include solving linear systems of equations, least squares problems, and eigenvalue problems. It is also used in data compression and signal processing.

5. How is QR factorization calculated?

There are several methods for calculating QR factorization, including Gram-Schmidt process, Householder transformation, and Givens rotation. These methods involve a series of matrix operations that ultimately produce the Q and R matrices. Software packages such as MATLAB and Python's NumPy have built-in functions for calculating QR factorization.

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