**Numerical Solutions Applied To Heat Transfer And Fluid
Mechanics Problems**

Whether you know it or not you've used Gauss elimination to solve systems of linear equations. What you probably never considered is that the method can be approached in a very systematic way, permitting implementation in a computer program. Also of importance is the fact that with very minimal additional effort, the program for Gauss elimination can be enhanced to perform Lower-Upper matrix factorization (write any non-singular matrix as a product of a lower triangular and an upper triangular matrix).

We wrote a set of linear equations with the following notation for coefficients

In the matrix notation of linear algebra, these equations can be written as:

Comparing the left side of the above matrix equation with the preceding set of linear equations gives a definition of the product of a matrix with a vector. When performing Gauss Elimination a further shorthand is used. We introduce the augmented matrix:

Let's lay out a specific set of equations in this format.

The augmented matrix form for this is:

For a standard Gauss elimination, the first question that we ask is:
By what factor must I multiply the first equation so that I can **subtract**
it from the second equation and eliminate the coefficient in column 1.
The answer is 4, and after I do this operation, the augmented matrix becomes:

or

Rather than leave the 0 in row 2 column 1 of the augmented matrix, I'm going to use that position to do some bookkeeping. I'll store the value of the multiplier that I just applied in the process of simplifying the equations.

Next I find that I need to multiply the first equation by 2 so that subtraction eliminates the coefficient in row 3 column 1. The result of this elimination including bookkeeping is:

Now I need to eliminate the coefficient in row 3 column 2. This can be accomplished by multiplying the equation in row 2 by 2/5 and subtracting it from the equation in row 3.

At this point we have completed the Gauss Elimination and by back substitution find that

x_{3} = 3/3 = 1

x_{2 }= (5+5x_{3})/5 = 2

x_{1} = 2 - 2x_{2} + x_{3} = -1

Now, let's look at the reason for those little numbers where the zeros would normally sit in the final form of the matrix. I can define a lower triangular matrix L by combining these numbers with 1's along the matrix diagonal to get:

and I can also write an upper triangular matrix from the basic results of the Gauss elimination

I claim that the matrix product LU is equal to the original coefficient
matrix for my equations.

Now I want to remind you of why we bother with L U decomposition. For
n equations with n unknowns Gauss elimination, or determining L and U takes
something proportional to n^{3} computer operations (multiplies
and adds). However solving the LUx=b system only takes of order n^{2
}operations.
(If you don't believe my counting solve 3, 4, and 5 equation systems and
make a careful count of the number of adds and multiplies that you do).
You won't see this for a while, but in many, perhaps most interesting problems
for a given matrix A you don't just solve one system Ax=b, but many related
systems Ax=b, Ax=c, Ax=d, .... You use LU decomposition to do most of the
work up front then additional equations are relatively cheap.

Let's check my claim that the product of L and U is equal to the original
coefficient matrix for the linear equations, and at the same time clearly
define matrix multiplication. I'm going to name the product A and its individual
coefficients a_{i j} . The coefficients of L are l_{i j}
and those of U are u_{i j} . The general definition for the matrix
product is:

where n is the number of rows in u (number of unknowns). Here n=3. In plainer terms the product can be written as a combination of matrix vector products. The first column of A is the result of multiplying L by the first column in U:

The second column of A is the product of L and the second column of U:

The third column of A is the product of L and the third column of U:

Combining the columns gives:

which is the original coefficient matrix. The bookkeeping exercise really did give us an LU decomposition. For those of you not totally up to speed on linear algebra you should got through the above exercise to calculate the product UL. You will discover that UL is not equal to LU. Matrix multiplication is not commutative.

In Fortran, the above matrix product can be accomplished with the following subroutine.

subroutine matmult(a,b,n,c) dimension a(n,n),b(n,n),c(n,n) do 100 i=1,n do 100 j=1,n c(i,j)=0. do 100 k=1,n 100 c(i,j)=c(i,j)+a(i,k)*b(k,j) return endUsing this type subroutine or loop structure is not a good idea when Fortran 90 is available, because you have a matrix multiply available that almost always will be implemented more efficiently than any compilation of your Fortran. For arrays established by a "REAL A(3,3), L(3,3), U(3,3)" you do the multiply with the following line:

A = MATMUL(L,U)

Time savings associated with things like matrix multiplies can be a
very big deal in many applications. If you look at the Fortran matrix multiply
above you will see that the operation takes n^{3} multiplies and
the same number of adds. You don't have to get to very large matrices or
very frequent use of the matrix multiply before you are looking at significant
savings in computer time by using a optimally coded function like MATMUL.

For more information on this, I recommend that you take a look at Chapter 19 of "Advanced Engineering Mathematics," by Erwin Kreyszig, or browse the Numerical Methods and Linear Algebra books in the library until something looks clear to you.