PA = LU Factorization with Pivoting |
PA = LU Factorization with Pivoting
Background
Definition ( LU-Factorization ). The
nonsingular matrix A has an LU-factorization if it can be expressed as
the product of a lower-triangular matrix L and an upper triangular matrix
U:
.
When this is possible we say that A has an LU-decomposition. It
turns out that this factorization (when it exists) is not unique. If L
has 1's on it's diagonal, then it is called a Doolittle factorization. If U
has 1's on its diagonal, then it is called a Crout factorization. When ,
it is called a
Cholesky decomposition. In this module we will
develop an algorithm that produces a Doolittle factorization.
Theorem ( LU
Factorization with NO
pivoting). If row interchanges
arenot needed to solve the linear system AX
= B, then A has the LU factorization (illustrated with 4�4
matrices).
=
.
Remark 1. This is not a linear system.
Remark 2. The easy solution uses row
vectors and is a modification of limited Gauss Jordan elimination.
Remark 3. A sufficient condition for the
factorization to exist is that all principal minors of A are nonsingular.
Mathematica Subroutine (Limited Gauss-Jordan
Elimination).
Mathematica Subroutine (LandU).
Theorem (A =
LU; Factorization with
NO Pivoting). Assume
that A has a Doolittle, Crout or Cholesky factorization. The
solution X to the linear system ,
is found in three steps:
1. Construct
the matrices ,
if possible.
2. Solve for using
forward substitution.
3. Solve for using
back substitution.
The above theorem assumes that there are no row interchanges. We have
seen in Example 3 an example of a nonsingular matrix A could not be
directly factored as A = LU. If row interchanges are permitted
then a factorization of a "permuted matrix" will be obtained. A permutation of
the first n positive integers . is
an arrangement of
these integers in a definite order. For example, is
a permutation of the five integers . The
standard base vectors for are
used in the next definition.
Definition ( Permutation
Matrix). An
n�n permutation matrix P is a matrix with precisely one entry whose value
is "1" in each column and row, and all of whose other entries are "0." The rows
of P are a permutation of the rows of the identity matrix and P can
be written as
.
The elements of have
the form
Theorem. Suppose
that is
a permutation matrix. The product PA is a new matrix whose rows
consists of the rows of A rearranged in the new order .
For example, the permutation matrix will
interchange rows 1 and 2 and also interchange rows 3 and 4.
Theorem. If A is
a nonsingular matrix, then there exists a permutation matrix P so
that PA has an LU-factorization
PA = LU.
Theorem (PA =
LU; Factorization with Pivoting). Given
that A is nonsingular. The solution X to the linear system ,
is found in four steps:
1. Construct
the matrices .
2. Compute
the column vector .
3. Solve for using
forward substitution.
4. Solve for using
back substitution.
Mathematica Subroutine (LUfactor).
Remark. The matrix A is
a global variable and elements are changed when
the LUfactor subroutine is
executed. Hence, it is important to save a copy of A in the variable A0.
Use one of the following two versions of the LUfactor subroutine
and the SolveLU subroutine. The
first version of LUfactor uses
parallel programming and "row operations" instead of loops.
This is the second version of LUfactor and
it uses more loops and traditional programming.
Mathematica Subroutine (LUfactor).
Use the subroutine SolveLU
which is similar to the forward substitution and back substitution subroutines.
Mathematica Subroutine (SolveLU).
Remark. Everything has been carefully
set up so that L, U, and P can all be studied.
Application to Polynomial Curve Fitting
Theorem ( Least-Squares
Polynomial Curve Fitting ).
Given the data
points , the
least squares polynomial of degree m of the
form
that fits the n data points is obtained by
solving the following linear system
for the m+1 coefficients
. These
equations are referred to as the "normal equations".
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