Analysis in Mechanical Engineering |
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© Leon van Dommelen |
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12.4 LU Theorem
If you put minus the multipliers in a Gaussian elimination without
partial pivoting in a lower triangular matrix , with values 1 on
the main diagonal, then
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(12.1) |
That is the theorem.
After and have been found through Gaussian elimination, you
can solve for any given right hand side
vector as follows: The system to solve is according to the
theorem . Temporarily call .
Solve using simple forward substitution.
With now known, can be solved using back
substitution as before:
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(12.2) |
In case partial pivoting was needed, remember the row order
interchanges you did and do the same interchanges with the
coefficients of before solving as above. You also need to
do the row exchanges on the forming matrix while doing the GE.
will then be the matrix , equal to matrix except with
its rows in the final order produced by all the row interchanges.
For large matrices the number of computer
operations
(defined as 1 multiplication and 1
addition) to find and is approximately . After
that, to find a given a takes only about
operations.
Some warnings. Normally speaking:
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1.
- Never ever use Cramer's rule for anything but the tiniest of
matrices. Multiplying out a determinant takes about
operations, which is gigantically larger than for
everything except the tiniest . Not to mention the possible
round-off error growth with so many operations. And the risk of
overflow and underflow (i.e. numbers getting outside the range that
they can be stored on a computer.)
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2.
- If you find the inverse matrix , you can simply find any
as . But that is normally a bad idea. One
reason is that it takes operations to find , three
times more than to find and . And to evaluate
still takes operations. Also the additional operations tend
to increase round-off error, [2].
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3.
- A band matrix is a matrix in which the nonzero elements
are restricted to a relatively narrow band around the main diagonal.
Never ever use an LU decomposition library routine designed for a
full matrix to solve a system with a band matrix. The waste in
storage and computer effort to store all these zeros, and compute
with them, would be gigantic. Use an LU decomposition subroutine
designed for a band matrix.
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4.
- Never ever find the inverse of a band matrix if you can help it.
The inverse will be a full matrix. However, and will still
be band matrices. (Partial pivoting may increase a bit to
outside the band of , by up to the width of .)