Least Squares Data Fitting
Existence, Uniqueness, and Conditioning
Solving Linear Least Squares Problems
Scientific Computing: An Introductory Survey
Chapter 3 – Linear Least Squares
Prof. Michael T. Heath
Department of Computer Science
University of Illinois at UrbanaChampaign
Copyright c 2002. Reproduction permitted
for noncommercial, educational use only.
Michael T. Heath
Scientific Computing
1 / 61
Least Squares Data Fitting
Existence, Uniqueness, and Conditioning
Solving Linear Least Squares Problems
Outline
1
Least Squares Data Fitting
2
Existence, Uniqueness, and Conditioning
3
Solving Linear Least Squares Problems
Michael T. Heath
Scientific Computing
2 / 61
Least Squares Data Fitting
Existence, Uniqueness, and Conditioning
Solving Linear Least Squares Problems
Least Squares
Data Fitting
Method of Least Squares
Measurement errors are inevitable in observational and
experimental sciences
Errors can be smoothed out by averaging over many
cases, i.e., taking more measurements than are strictly
necessary to determine parameters of system
Resulting system is
overdetermined
, so usually there is no
exact solution
In effect, higher dimensional data are projected into lower
dimensional space to suppress irrelevant detail
Such projection is most conveniently accomplished by
method of
least squares
Michael T. Heath
Scientific Computing
3 / 61
Least Squares Data Fitting
Existence, Uniqueness, and Conditioning
Solving Linear Least Squares Problems
Least Squares
Data Fitting
Linear Least Squares
For linear problems, we obtain
overdetermined
linear
system
Ax
=
b
, with
m
×
n
matrix
A
,
m > n
System is better written
Ax
∼
=
b
, since equality is usually
not exactly satisfiable when
m > n
Least squares solution
x
minimizes squared Euclidean
norm of residual vector
r
=
b

Ax
,
min
x
r
2
2
= min
x
b

Ax
2
2
Michael T. Heath
Scientific Computing
4 / 61
Least Squares Data Fitting
Existence, Uniqueness, and Conditioning
Solving Linear Least Squares Problems
Least Squares
Data Fitting
Data Fitting
Given
m
data points
(
t
i
, y
i
)
, find
n
vector
x
of parameters
that gives “best fit” to model function
f
(
t,
x
)
,
min
x
m
i
=1
(
y
i

f
(
t
i
,
x
))
2
Problem is
linear
if function
f
is linear in components of
x
,
f
(
t,
x
) =
x
1
φ
1
(
t
) +
x
2
φ
2
(
t
) +
· · ·
+
x
n
φ
n
(
t
)
where functions
φ
j
depend only on
t
Problem can be written in matrix form as
Ax
∼
=
b
, with
a
ij
=
φ
j
(
t
i
)
and
b
i
=
y
i
Michael T. Heath
Scientific Computing
5 / 61
Least Squares Data Fitting
Existence, Uniqueness, and Conditioning
Solving Linear Least Squares Problems
Least Squares
Data Fitting
Data Fitting
Polynomial fitting
f
(
t,
x
) =
x
1
+
x
2
t
+
x
3
t
2
+
· · ·
+
x
n
t
n

1
is linear, since polynomial linear in coefficients, though
nonlinear in independent variable
t
Fitting sum of exponentials
f
(
t,
x
) =
x
1
e
x
2
t
+
· · ·
+
x
n

1
e
x
n
t
is example of nonlinear problem
For now, we will consider only linear least squares
problems
Michael T. Heath
Scientific Computing
6 / 61
Least Squares Data Fitting
Existence, Uniqueness, and Conditioning
Solving Linear Least Squares Problems
Least Squares
Data Fitting
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 Fall '08
 Layton,A
 Least Squares, Singular value decomposition, Linear least squares, Michael T. Heath, Data Fitting Existence

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