Skip to main content

2.5 LU decomposition (3) Why do we do LU decomposition?

Finally I can conclude LU decomposition. If you are not interested in linear algebra, unfortunately this is not for you.





Until the last two blog entries, we say LU decomposition is just an elimination, but keeping the elimination matrices as the inverse of them. As I understand, LU decomposition has two advantages.

The first point is, L and U are triangler matrix. A triangler matrix is the easiest matrix to be solved by back substitution. For example, if we want to solve the following triangler matrix system,
this is simple. I rewrite this as simultaneous equations:

Equation 12 shows directory x=5. Equation 13 is x + y = 2, we know x=5, therefore, y=-3. Equation 14 is x + 2y + z = 2, therefore, 5 - 6 + z = 2, then z = 3. We don't need any elimination process. Actually, we have done elimination to get this triangler matrix, therefore, we don't need to wonder. It is so easy to solve a triangler matrix.

The second point is LU decomposition needs only matrices. Recall the elimination needs augmented matrix to get a solution. LU decomposition doesn't have that. When we want to solve the system with many right hand sides, LU decomposition needs only one decomposition as long as the system stays the same, we could repeat back substitution for each right hand side, that is simple.

As a conclusion, the reason of doing LU decomposition is: L and U are both triangler matrix and easy to solve, for each system, we need only one decomposition and back substitution. If only the right hand side changes, we don't need perform decomposition again. LU decomposition has these two advantages compare to pure elimination.

Now we solve the system by LU decomposition. We use the following observations:

This is an example system.







LU decomposition has been done by elimination. We just solve two systems and both are triangler matrices. We have already seen how simple to solve the triangler system. This is why LU decomposition has an advantage.

Comments

Popular posts from this blog

Geometric Multiplicity: eignvectors (2)

If eigenvectors of a matrix A are independent, it is a happy property. Because the matrix A can be diagonalized with a matrix S that column vectors are eigenvectors of A . For example, Why this is a happy property of A? Because I can find A's power easily. A^{10} is not a big deal. Because Λ is a diagonal matrix and power of a diagonal matrix is quite simple. A^{10} = SΛ^{10} S^{-1} Then, why if I want to compute power of A ? That is the same reason to find eigenvectors. Eigenvectors are a basis of a matrix. A matrix can be represented by a single scalar. I repeat this again. This is the happy point, a matrix becomes a scalar. What can be simpler than a scalar value. But, this is only possible when the matrix S's columns are independent. Because S^{-1} must be exist. Now I come back to my first question. Is the λ's multiplicity related with the number of eigenvectors? This time I found this has the name. Geometric multiplicity (GM): the number of in...

Gauss's quote for positive, negative, and imaginary number

Recently I watched the following great videos about imaginary numbers by Welch Labs. https://youtu.be/T647CGsuOVU?list=PLiaHhY2iBX9g6KIvZ_703G3KJXapKkNaF I like this article about naming of math by Kalid Azad. https://betterexplained.com/articles/learning-tip-idea-name/ Both articles mentioned about Gauss, who suggested to use other names of positive, negative, and imaginary numbers. Gauss wrote these names are wrong and that is one of the reason people didn't get why negative times negative is positive, or, pure positive imaginary times pure positive imaginary is negative real number. I made a few videos about explaining why -1 * -1 = +1, too. Explanation: why -1 * -1 = +1 by pattern https://youtu.be/uD7JRdAzKP8 Explanation: why -1 * -1 = +1 by climbing a mountain https://youtu.be/uD7JRdAzKP8 But actually Gauss's insight is much powerful. The original is in the Gauß, Werke, Bd. 2, S. 178 . Hätte man +1, -1, √-1) nicht positiv, negative, imaginäre (oder gar um...

Tezuka Osamu's Black Jack, "Shrinking"

I like several novel authors. My first favorite author is probably Teduka, Osamu. I still love him. The list grows by adding Hoshi, Shinichi, Agatha Christie, Hermann Hesse, and so forth. My first favorite article of Tezuka was Atom as most of the (boy's) Tezuka fans did. But my favorite is Black Jack. I try to summarize one story, it is still quite vivid in my memory. I first read this story when I was 13 - 15 years old. I re-read it at least several times since Black Jack is composed of many short episodes. The title should be "ちぢむ (SHRINKING)" or it might be "縮む(Shrinking)". (It is not so convenient to translate this to English, since English does not have a system to say the exact same word in several ways. So I just simulate it with capital letters.) Black Jack is a genius surgeon, but he does not have the license. In short, his medical activity is illegal. His skill is top level in the world, but, the fee is also out-of-law expensive. In the story ...