Have you done PA=LU factorization?

In summary, The first permutation step is trivial (since the pivot element 10 is already the largest). The corresponding permutation matrix is the identity, and we need not write it down. The first elimination step is: in order to avoid dividing by small numbers, we do permutations in order to divide by a larger number instead. This is necessary because dividing by small numbers can lead to incorrect solutions. The LU decomposition is useful for solving systems of linear equations.
  • #1
LongApple
68
0
I'm trying to figure out what this quote means and what our strategy is just looking at the matrix what kind of permutations we need to do.

Quotes: "The first permutation step is trivial (since the pivot element 10 is already the largest). The corresponding permutation matrix is the identity, and we need not write it down. The first elimination step is:"

Why do we need to do permutations at all? How do we know by looking?

upload_2015-2-15_4-27-42.png


https://www.student.cs.uwaterloo.ca/~cs370/notes/LUExample2.pdf
 
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  • #2
So as a start, why do we care at all that 10 is the largest?
 
  • #3
Dividing by small numbers is bad. We do permutations so we may divide by a big number. In step 1 10 is already the biggest available. In step 2 5/2 is larger than 1/10 so we do a permutation so we can divide by 5/2 instead of 1/10.
 
  • #4
"... what kind of permutations we need to do."
Are you referring to what permutations have to do in this setting (I may be grossly misunderstanding the thrust of your question)? If so, they are referring to rearranging (permuting is rearranging) the rows in order to bring a more desirable value to the pivot location.

If that wasn't your question - apologies.
 
  • #5
lurflurf said:
Dividing by small numbers is bad. We do permutations so we may divide by a big number. In step 1 10 is already the biggest available. In step 2 5/2 is larger than 1/10 so we do a permutation so we can divide by 5/2 instead of 1/10.

1. Why is dividing by big numbers bad? Aside from inconvenient I don't see why big number necessarily necessitates row swaps

2. 10 is already the biggest available" <- available of _____ ? Of the numbers down the column

I'm a bit confused about P

Given a matrix A, does it always have a fixed P for LU factorization? Couldn't we have multiple different P's depending on how we RREF the problem ?
 
  • #6
"Why is dividing by big numbers bad?"
Read again - dividing by small numbers is to be avoided

"Of the numbers down the column" Yes

"Given a matrix A, does it always have a fixed P for LU factorization? Couldn't we have multiple different P's depending on how we RREF the problem"
Yes, you can: each time rows are swapped there is another P: take a look here:
http://staff.imsa.edu/~fogel/LinAlg/PDF/17 Permutations and Row Swaps.pdf
 
  • #7
statdad said:
"Why is dividing by big numbers bad?"
Read again - dividing by small numbers is to be avoided

"Of the numbers down the column" Yes

"Given a matrix A, does it always have a fixed P for LU factorization? Couldn't we have multiple different P's depending on how we RREF the problem"
Yes, you can: each time rows are swapped there is another P: take a look here:
http://staff.imsa.edu/~fogel/LinAlg/PDF/17 Permutations and Row Swaps.pdf

""Why is dividing by big numbers bad?"
Read again - dividing by small numbers is to be avoided"

Yup I read that the first time. Now - why do we want this or even if we want it why is it necessary? Maybe it is obvious to you

Aside from inconvenient I don't see why big number necessarily necessitates row swaps____________

upload_2015-2-15_20-38-44.png


1. Why does L have 1's down the diagonal? Couldn't it have anything down the diagonal?

2. Is P is not unique, then LU is not unique right. So how have linear algebra people been grading tests all these years? They can't enumerate all possible LU's can they or take the time to manually multiply every studetn's answer out can they?
 

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  • #9
""Why is dividing by big numbers bad?"
Read again - dividing by small numbers is to be avoided"

Yup I read that the first time. Now - why do we want this or even if we want it why is it necessary? Maybe it is obvious to you

not in wikipedia
 
  • #10
The wikipedia gives a classic example of the need for pivoting consider two systems
$$\left[ \begin{array}{cc|c}
0.00300 & 59.14 & 59.17 \\
5.291 & -6.130 & 46.78 \\
\end{array} \right]
\\
\left[ \begin{array}{cc|c}
5.291 & -6.130 & 46.78 \\
0.00300 & 59.14 & 59.17 \\
\end{array} \right]$$
If we work with 4 digits and no pivoting we get for the first system
{9873.3,4}
for the second
{10.00,1.000}
We see the two systems should have the same solution, but the first one came out wrong due to dividing by small numbers.
So we decide to divide by the largest number available.

Edit: I got something different when I checked the wikipedia result, I probably added wrong somewhere.
 
Last edited:
  • #11
LongApple said:
""Why is dividing by big numbers bad?"
Read again - dividing by small numbers is to be avoided"

Yup I read that the first time. Now - why do we want this or even if we want it why is it necessary? Maybe it is obvious to you

not in wikipedia
Why do we want the LU decomposition? As one example - look up LU and solving systems of linear equations
 

1. What is PA=LU factorization?

PA=LU factorization is a method used in linear algebra to decompose a matrix into the product of a permutation matrix (P), a lower triangular matrix (L), and an upper triangular matrix (U). It is used to simplify systems of linear equations and to find the inverse of a matrix.

2. Why is PA=LU factorization useful?

PA=LU factorization is useful because it allows for the efficient solving of linear systems of equations, as well as finding the inverse of a matrix. It also helps to reduce rounding errors in numerical calculations.

3. How is PA=LU factorization different from other matrix decomposition methods?

PA=LU factorization differs from other matrix decomposition methods, such as Cholesky decomposition or QR decomposition, in that it involves a permutation matrix (P). This allows for more flexibility in the decomposition and can help to reduce rounding errors.

4. What are the applications of PA=LU factorization?

PA=LU factorization has many applications in mathematics, engineering, and science. It is used in solving systems of linear equations, finding the inverse of a matrix, and in numerical analysis. It is also used in computer graphics and in solving differential equations.

5. How is PA=LU factorization performed?

The PA=LU factorization is performed by finding the permutation matrix (P) that rearranges the rows of the original matrix, then using Gaussian elimination to reduce the matrix to upper triangular form (U). Finally, the lower triangular matrix (L) is calculated using the pivot elements from the elimination process. The resulting factorization is PA=LU.

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