Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
Math 4/896: Seminar in Mathematics
Topic: Inverse Theory
Instructor: Thomas Shores
Department of Mathematics
Lecture 23, April 6, 2006
AvH 10
Instructor: Thomas Shores Department of Mathematics
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Key Idea: Generalized SVD (GSVD)
Theorem
Let G be an m
m
×m
×n
matrix and L a p
orthogonal U , p
matrix X with m
×p
×n
matrix. Then there exist
orthogonal V and n
≥ n ≥ min {p , n} = q
×n
nonsingular
such that
T GX
=
T LX
V
diag {λ1 , λ2 , . . . , λn }
= Λ = Λm ,n
=
ΛT Λ + M T M
diag {µ1 , µ2 , . . . , µq }
= M = Mp ,n
=
1.
U
≤ λ1 ≤ λ2 · · · ≤ λn ≤ 1 and 1 ≥ µ1 ≥ µ2 · · · ≥ µq ≥ 0.
γi = λi /µi , i = 1, . . . , rank (L) ≡ r are called the
generalized singular values of G and L and 0 ≤ γ1 ≤ γ2 · · · ≤ γr .
Also
0
The numbers
Application to Higher Order Regularization
The minimization problem is equivalent to the problem
G
T G + α 2 LT L
m = GTd
which has solution forms
mα,L =
Filter factors: fj
p
X
j =1
=
γ2
j
γj2 + α2
γj2
γj2 + α2
UTj d
λj
Xj +
n X
j =p +1
UTj d Xj
, j = 1, . . . , p , fj = 1, j = p + 1, . . . , n.
Thus
mα,L =
n
X
j =1
fj
UTj d
λj
Xj .
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
TGSVD and GCV
Error Bounds
Vertical Seismic Proling Example
The Experiment:
Place sensors at vertical depths zj , j
= 1, . . . , n ,
in a borehole,
then:
Generate a seizmic wave at ground level, t
Measure arrival times dj
= 0.
= t (zj ), j = 1, . . . , n.
Now try to recover the slowness function s (z ), given
Z z
t
(z ) =
0
Z
s
(ξ) d ξ =
It should be easy: s (z )
0
∞
s
(ξ) H (z − ξ) d ξ
= t 0 (z ).
Hmmm.....or is it?
Do Example 5.4-5.5 from the CD.
Instructor: Thomas Shores Department of Mathematics
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
TGSVD and GCV
Error Bounds
Vertical Seismic Proling Example
The Experiment:
Place sensors at vertical depths zj , j
= 1, . . . , n ,
in a borehole,
then:
Generate a seizmic wave at ground level, t
Measure arrival times dj
= 0.
= t (zj ), j = 1, . . . , n.
Now try to recover the slowness function s (z ), given
Z z
t
(z ) =
0
Z
s
(ξ) d ξ =
It should be easy: s (z )
0
∞
s
(ξ) H (z − ξ) d ξ
= t 0 (z ).
Hmmm.....or is it?
Do Example 5.4-5.5 from the CD.
Instructor: Thomas Shores Department of Mathematics
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
TGSVD and GCV
Error Bounds
Vertical Seismic Proling Example
The Experiment:
Place sensors at vertical depths zj , j
= 1, . . . , n ,
in a borehole,
then:
Generate a seizmic wave at ground level, t
Measure arrival times dj
= 0.
= t (zj ), j = 1, . . . , n.
Now try to recover the slowness function s (z ), given
Z z
t
(z ) =
0
Z
s
(ξ) d ξ =
It should be easy: s (z )
0
∞
s
(ξ) H (z − ξ) d ξ
= t 0 (z ).
Hmmm.....or is it?
Do Example 5.4-5.5 from the CD.
Instructor: Thomas Shores Department of Mathematics
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
TGSVD and GCV
Error Bounds
Vertical Seismic Proling Example
The Experiment:
Place sensors at vertical depths zj , j
= 1, . . . , n ,
in a borehole,
then:
Generate a seizmic wave at ground level, t
Measure arrival times dj
= 0.
= t (zj ), j = 1, . . . , n.
Now try to recover the slowness function s (z ), given
Z z
t
(z ) =
0
Z
s
(ξ) d ξ =
It should be easy: s (z )
0
∞
s
(ξ) H (z − ξ) d ξ
= t 0 (z ).
Hmmm.....or is it?
Do Example 5.4-5.5 from the CD.
Instructor: Thomas Shores Department of Mathematics
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
TGSVD and GCV
Error Bounds
Vertical Seismic Proling Example
The Experiment:
Place sensors at vertical depths zj , j
= 1, . . . , n ,
in a borehole,
then:
Generate a seizmic wave at ground level, t
Measure arrival times dj
= 0.
= t (zj ), j = 1, . . . , n.
Now try to recover the slowness function s (z ), given
Z z
t
(z ) =
0
Z
s
(ξ) d ξ =
It should be easy: s (z )
0
∞
s
(ξ) H (z − ξ) d ξ
= t 0 (z ).
Hmmm.....or is it?
Do Example 5.4-5.5 from the CD.
Instructor: Thomas Shores Department of Mathematics
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
TGSVD and GCV
Error Bounds
Vertical Seismic Proling Example
The Experiment:
Place sensors at vertical depths zj , j
= 1, . . . , n ,
in a borehole,
then:
Generate a seizmic wave at ground level, t
Measure arrival times dj
= 0.
= t (zj ), j = 1, . . . , n.
Now try to recover the slowness function s (z ), given
Z z
t
(z ) =
0
Z
s
(ξ) d ξ =
It should be easy: s (z )
0
∞
s
(ξ) H (z − ξ) d ξ
= t 0 (z ).
Hmmm.....or is it?
Do Example 5.4-5.5 from the CD.
Instructor: Thomas Shores Department of Mathematics
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
TGSVD and GCV
Error Bounds
Model Resolution
Model Resolution Matrix:
m
As usual, R ,α,L
= G \G .
We can show this is XFX
−1 .
Instructor: Thomas Shores Department of Mathematics
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
TGSVD and GCV
Error Bounds
Model Resolution
Model Resolution Matrix:
m
As usual, R ,α,L
= G \G .
We can show this is XFX
−1 .
Instructor: Thomas Shores Department of Mathematics
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
TGSVD and GCV
Error Bounds
Outline
Instructor: Thomas Shores Department of Mathematics
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
TGSVD and GCV
Error Bounds
TGSVD:
We have seen this idea before. Simply apply it to formula above,
remembering that the generalized singular values are reverse
ordered.
Formula becomes
mα,L =
p
X
j =k
γ2
j
γj2 + α2
UTj d
cj
Xj +
n X
j =p +1
UTj d Xj
Key question: where to start k .
Instructor: Thomas Shores Department of Mathematics
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
TGSVD and GCV
Error Bounds
GCV
Basic Idea:
Comes from statistical leave-one-out cross validation.
Leave out one data point and use model to predict it.
Sum these up and choose regularization parameter
α
that
minimizes the sum of the squares of the predictive errors
V0
(α) =
1
m
m X
[k ]
α,L
Gm
k =1
k
− dk
2
.
One can show a good approximation is
V0
(α) =
m
kG mα − dk2
Tr (I
2
− GG \ )
Example 5.6-7 gives a nice illustration of the ideas. Use the CD
Instructor:
Thomas
Department
of Mathematics
Math 4/896:
Seminar
script
to Shores
explore
it. Change
the startuple
path
to in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
TGSVD and GCV
Error Bounds
GCV
Basic Idea:
Comes from statistical leave-one-out cross validation.
Leave out one data point and use model to predict it.
Sum these up and choose regularization parameter
α
that
minimizes the sum of the squares of the predictive errors
V0
(α) =
1
m
m X
[k ]
α,L
Gm
k =1
k
− dk
2
.
One can show a good approximation is
V0
(α) =
m
kG mα − dk2
Tr (I
2
− GG \ )
Example 5.6-7 gives a nice illustration of the ideas. Use the CD
Instructor:
Thomas
Department
of Mathematics
Math 4/896:
Seminar
script
to Shores
explore
it. Change
the startuple
path
to in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
TGSVD and GCV
Error Bounds
GCV
Basic Idea:
Comes from statistical leave-one-out cross validation.
Leave out one data point and use model to predict it.
Sum these up and choose regularization parameter
α
that
minimizes the sum of the squares of the predictive errors
V0
(α) =
1
m
m X
[k ]
α,L
Gm
k =1
k
− dk
2
.
One can show a good approximation is
V0
(α) =
m
kG mα − dk2
Tr (I
2
− GG \ )
Example 5.6-7 gives a nice illustration of the ideas. Use the CD
Instructor:
Thomas
Department
of Mathematics
Math 4/896:
Seminar
script
to Shores
explore
it. Change
the startuple
path
to in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
TGSVD and GCV
Error Bounds
GCV
Basic Idea:
Comes from statistical leave-one-out cross validation.
Leave out one data point and use model to predict it.
Sum these up and choose regularization parameter
α
that
minimizes the sum of the squares of the predictive errors
V0
(α) =
1
m
m X
[k ]
α,L
Gm
k =1
k
− dk
2
.
One can show a good approximation is
V0
(α) =
m
kG mα − dk2
Tr (I
2
− GG \ )
Example 5.6-7 gives a nice illustration of the ideas. Use the CD
Instructor:
Thomas
Department
of Mathematics
Math 4/896:
Seminar
script
to Shores
explore
it. Change
the startuple
path
to in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
TGSVD and GCV
Error Bounds
Outline
Instructor: Thomas Shores Department of Mathematics
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
TGSVD and GCV
Error Bounds
Error Bounds
Error Estimates:
They exist, even in the hard cases where there is error in both G
and d .
In the simpler case, G known exactly, they take the form
e
d − d
e α k2
kmα − m
2
≤ κα
kmα k2
kG mα k2
where
κα
is inversely proportional to
Instructor: Thomas Shores Department of Mathematics
α.
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
TGSVD and GCV
Error Bounds
Error Bounds
Error Estimates:
They exist, even in the hard cases where there is error in both G
and d .
In the simpler case, G known exactly, they take the form
e
d − d
e α k2
kmα − m
2
≤ κα
kmα k2
kG mα k2
where
κα
is inversely proportional to
Instructor: Thomas Shores Department of Mathematics
α.
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
TGSVD and GCV
Error Bounds
Error Bounds
More Estimates:
mtrue is smooth in the sense
w such that (p = 1) mtrue = G T w or
= G T G w. Let ∆ = δ/ kwk and γ = 1 if p = 1
Suppose that the true model
that there exists vector
(p
= 2) mtrue
γ = 4 if p = 2.
and
Then the choice
α
b = (∆/γ)1/(p+1)
is
optimal in the sense that we have the error bound
p \ p = O ∆ p +1 .
m
−
G
d
=
γ
(
p
+
1
)
α
b
true
2
This is about the best we can do. Its signicance: the best we
can hope for is about 1/2 or 2/3 of the signicant digits in the
data.
Instructor: Thomas Shores Department of Mathematics
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
TGSVD and GCV
Error Bounds
Error Bounds
More Estimates:
mtrue is smooth in the sense
w such that (p = 1) mtrue = G T w or
= G T G w. Let ∆ = δ/ kwk and γ = 1 if p = 1
Suppose that the true model
that there exists vector
(p
= 2) mtrue
γ = 4 if p = 2.
and
Then the choice
α
b = (∆/γ)1/(p+1)
is
optimal in the sense that we have the error bound
p \ p = O ∆ p +1 .
m
−
G
d
=
γ
(
p
+
1
)
α
b
true
2
This is about the best we can do. Its signicance: the best we
can hope for is about 1/2 or 2/3 of the signicant digits in the
data.
Instructor: Thomas Shores Department of Mathematics
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
TGSVD and GCV
Error Bounds
Error Bounds
More Estimates:
mtrue is smooth in the sense
w such that (p = 1) mtrue = G T w or
= G T G w. Let ∆ = δ/ kwk and γ = 1 if p = 1
Suppose that the true model
that there exists vector
(p
= 2) mtrue
γ = 4 if p = 2.
and
Then the choice
α
b = (∆/γ)1/(p+1)
is
optimal in the sense that we have the error bound
p \ p = O ∆ p +1 .
m
−
G
d
=
γ
(
p
+
1
)
α
b
true
2
This is about the best we can do. Its signicance: the best we
can hope for is about 1/2 or 2/3 of the signicant digits in the
data.
Instructor: Thomas Shores Department of Mathematics
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
Image Recovery
Problem:
An image is blurred and we want to sharpen it. Let intensity
function Itrue (x , y )dene the true image and Iblurred
(x , y )
dene
the blurred image.
A typical model results from convolving true image with
Gaussian point spread function
Z
Iblurred
where
∞
Z
∞
(x , y ) =
−∞
Ψ (u , v ) = e −(u
−∞
Itrue
(x − u , y − v ) Ψ (u , v )
2 +v 2 / 2σ 2
)( )
du dv
.
Think about discretizing this over an SVGA image
(1024
× 768).
But the discretized matrix should be sparse!
Instructor: Thomas Shores Department of Mathematics
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
Image Recovery
Problem:
An image is blurred and we want to sharpen it. Let intensity
function Itrue (x , y )dene the true image and Iblurred
(x , y )
dene
the blurred image.
A typical model results from convolving true image with
Gaussian point spread function
Z
Iblurred
where
∞
Z
∞
(x , y ) =
−∞
Ψ (u , v ) = e −(u
−∞
Itrue
(x − u , y − v ) Ψ (u , v )
2 +v 2 / 2σ 2
)( )
du dv
.
Think about discretizing this over an SVGA image
(1024
× 768).
But the discretized matrix should be sparse!
Instructor: Thomas Shores Department of Mathematics
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
Image Recovery
Problem:
An image is blurred and we want to sharpen it. Let intensity
function Itrue (x , y )dene the true image and Iblurred
(x , y )
dene
the blurred image.
A typical model results from convolving true image with
Gaussian point spread function
Z
Iblurred
where
∞
Z
∞
(x , y ) =
−∞
Ψ (u , v ) = e −(u
−∞
Itrue
(x − u , y − v ) Ψ (u , v )
2 +v 2 / 2σ 2
)( )
du dv
.
Think about discretizing this over an SVGA image
(1024
× 768).
But the discretized matrix should be sparse!
Instructor: Thomas Shores Department of Mathematics
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
Image Recovery
Problem:
An image is blurred and we want to sharpen it. Let intensity
function Itrue (x , y )dene the true image and Iblurred
(x , y )
dene
the blurred image.
A typical model results from convolving true image with
Gaussian point spread function
Z
Iblurred
where
∞
Z
∞
(x , y ) =
−∞
Ψ (u , v ) = e −(u
−∞
Itrue
(x − u , y − v ) Ψ (u , v )
2 +v 2 / 2σ 2
)( )
du dv
.
Think about discretizing this over an SVGA image
(1024
× 768).
But the discretized matrix should be sparse!
Instructor: Thomas Shores Department of Mathematics
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
Sparse Matrices and Iterative Methods
Sparse Matrix:
A matrix with suciently many zeros that we should pay attention
to them.
There are ecient ways of storing such matrices and doing
linear algebra on them.
Given a problem A
x=b
with A sparse, iterative methods
become attractive because they usually only require storage of
A,
x
and some auxillary vectors, and saxpy, gaxpy, dot
algorithms (scalar a*x+y , general A*x+y, dot product)
Classical methods: Jacobi, Gauss-Seidel, Gauss-Seidel SOR
and conjugate gradient.
Methods especially useful for tomographic problems:
Kaczmarz's method, ART (algebraic reconstruction technique).
Instructor: Thomas Shores Department of Mathematics
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
Sparse Matrices and Iterative Methods
Sparse Matrix:
A matrix with suciently many zeros that we should pay attention
to them.
There are ecient ways of storing such matrices and doing
linear algebra on them.
Given a problem A
x=b
with A sparse, iterative methods
become attractive because they usually only require storage of
A,
x
and some auxillary vectors, and saxpy, gaxpy, dot
algorithms (scalar a*x+y , general A*x+y, dot product)
Classical methods: Jacobi, Gauss-Seidel, Gauss-Seidel SOR
and conjugate gradient.
Methods especially useful for tomographic problems:
Kaczmarz's method, ART (algebraic reconstruction technique).
Instructor: Thomas Shores Department of Mathematics
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
Sparse Matrices and Iterative Methods
Sparse Matrix:
A matrix with suciently many zeros that we should pay attention
to them.
There are ecient ways of storing such matrices and doing
linear algebra on them.
Given a problem A
x=b
with A sparse, iterative methods
become attractive because they usually only require storage of
A,
x
and some auxillary vectors, and saxpy, gaxpy, dot
algorithms (scalar a*x+y , general A*x+y, dot product)
Classical methods: Jacobi, Gauss-Seidel, Gauss-Seidel SOR
and conjugate gradient.
Methods especially useful for tomographic problems:
Kaczmarz's method, ART (algebraic reconstruction technique).
Instructor: Thomas Shores Department of Mathematics
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
Sparse Matrices and Iterative Methods
Sparse Matrix:
A matrix with suciently many zeros that we should pay attention
to them.
There are ecient ways of storing such matrices and doing
linear algebra on them.
Given a problem A
x=b
with A sparse, iterative methods
become attractive because they usually only require storage of
A,
x
and some auxillary vectors, and saxpy, gaxpy, dot
algorithms (scalar a*x+y , general A*x+y, dot product)
Classical methods: Jacobi, Gauss-Seidel, Gauss-Seidel SOR
and conjugate gradient.
Methods especially useful for tomographic problems:
Kaczmarz's method, ART (algebraic reconstruction technique).
Instructor: Thomas Shores Department of Mathematics
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
Sparse Matrices and Iterative Methods
Sparse Matrix:
A matrix with suciently many zeros that we should pay attention
to them.
There are ecient ways of storing such matrices and doing
linear algebra on them.
Given a problem A
x=b
with A sparse, iterative methods
become attractive because they usually only require storage of
A,
x
and some auxillary vectors, and saxpy, gaxpy, dot
algorithms (scalar a*x+y , general A*x+y, dot product)
Classical methods: Jacobi, Gauss-Seidel, Gauss-Seidel SOR
and conjugate gradient.
Methods especially useful for tomographic problems:
Kaczmarz's method, ART (algebraic reconstruction technique).
Instructor: Thomas Shores Department of Mathematics
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
Yet Another Regularization Idea
To regularize in face of iteration:
Use the number of iteration steps taken as a regularization
parameter.
Conjugate gradient methods are designed to work with SPD
coecient matrices A in the equation A
x = b.
So in the unregularized least squares problem G
take A
= GTG
and
b
= G T d,
TGm =
G
Td
resulting in the CGLS method,
in which we avoid explicitly computing G
TG.
(0)
m (k ) = 0,then
G m − d is
Key fact: in exact arithmetic, if we start at
(k ) m is monotone increasing in k and
monotonically decreasing in k . So we can make an L-curve in
terms of k .
Do Example 6.3 from the CD. Change startuple path to
Examples/chap6/examp3
Instructor: Thomas Shores Department of Mathematics
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
Yet Another Regularization Idea
To regularize in face of iteration:
Use the number of iteration steps taken as a regularization
parameter.
Conjugate gradient methods are designed to work with SPD
coecient matrices A in the equation A
x = b.
So in the unregularized least squares problem G
take A
= GTG
and
b
= G T d,
TGm =
G
Td
resulting in the CGLS method,
in which we avoid explicitly computing G
TG.
(0)
m (k ) = 0,then
G m − d is
Key fact: in exact arithmetic, if we start at
(k ) m is monotone increasing in k and
monotonically decreasing in k . So we can make an L-curve in
terms of k .
Do Example 6.3 from the CD. Change startuple path to
Examples/chap6/examp3
Instructor: Thomas Shores Department of Mathematics
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
Yet Another Regularization Idea
To regularize in face of iteration:
Use the number of iteration steps taken as a regularization
parameter.
Conjugate gradient methods are designed to work with SPD
coecient matrices A in the equation A
x = b.
So in the unregularized least squares problem G
take A
= GTG
and
b
= G T d,
TGm =
G
Td
resulting in the CGLS method,
in which we avoid explicitly computing G
TG.
(0)
m (k ) = 0,then
G m − d is
Key fact: in exact arithmetic, if we start at
(k ) m is monotone increasing in k and
monotonically decreasing in k . So we can make an L-curve in
terms of k .
Do Example 6.3 from the CD. Change startuple path to
Examples/chap6/examp3
Instructor: Thomas Shores Department of Mathematics
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
Yet Another Regularization Idea
To regularize in face of iteration:
Use the number of iteration steps taken as a regularization
parameter.
Conjugate gradient methods are designed to work with SPD
coecient matrices A in the equation A
x = b.
So in the unregularized least squares problem G
take A
= GTG
and
b
= G T d,
TGm =
G
Td
resulting in the CGLS method,
in which we avoid explicitly computing G
TG.
(0)
m (k ) = 0,then
G m − d is
Key fact: in exact arithmetic, if we start at
(k ) m is monotone increasing in k and
monotonically decreasing in k . So we can make an L-curve in
terms of k .
Do Example 6.3 from the CD. Change startuple path to
Examples/chap6/examp3
Instructor: Thomas Shores Department of Mathematics
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
7.1: Using Bounds as Constraints
Outline
Instructor: Thomas Shores Department of Mathematics
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
7.1: Using Bounds as Constraints
Regularization...Sort Of
Basic Idea:
Use prior knowledge about the nature of the solution to restrict it:
Most common restrictions: on the magnitude of the parameter
values. Which leads to the problem:
Minimize f
subject to l
(m)
≤ m ≤ u.
One could choose f
One could choose f
kG m − dk2 ≤ δ .
(m) = kG m − dk2 (BVLS)
(m) = cT · mwith
Instructor: Thomas Shores Department of Mathematics
additional constraint
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
7.1: Using Bounds as Constraints
Regularization...Sort Of
Basic Idea:
Use prior knowledge about the nature of the solution to restrict it:
Most common restrictions: on the magnitude of the parameter
values. Which leads to the problem:
Minimize f
subject to l
(m)
≤ m ≤ u.
One could choose f
One could choose f
kG m − dk2 ≤ δ .
(m) = kG m − dk2 (BVLS)
(m) = cT · mwith
Instructor: Thomas Shores Department of Mathematics
additional constraint
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
7.1: Using Bounds as Constraints
Regularization...Sort Of
Basic Idea:
Use prior knowledge about the nature of the solution to restrict it:
Most common restrictions: on the magnitude of the parameter
values. Which leads to the problem:
Minimize f
subject to l
(m)
≤ m ≤ u.
One could choose f
One could choose f
kG m − dk2 ≤ δ .
(m) = kG m − dk2 (BVLS)
(m) = cT · mwith
Instructor: Thomas Shores Department of Mathematics
additional constraint
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
7.1: Using Bounds as Constraints
Regularization...Sort Of
Basic Idea:
Use prior knowledge about the nature of the solution to restrict it:
Most common restrictions: on the magnitude of the parameter
values. Which leads to the problem:
Minimize f
subject to l
(m)
≤ m ≤ u.
One could choose f
One could choose f
kG m − dk2 ≤ δ .
(m) = kG m − dk2 (BVLS)
(m) = cT · mwith
Instructor: Thomas Shores Department of Mathematics
additional constraint
Math 4/896: Seminar in Mathematics Topic: Inverse Theo
Chapter 5: Tikhonov Regularization
Chapter 6: Iterative Methods A Brief Discussion
Chapter 7: Additional Regularization Techniques
7.1: Using Bounds as Constraints
Regularization...Sort Of
Basic Idea:
Use prior knowledge about the nature of the solution to restrict it:
Most common restrictions: on the magnitude of the parameter
values. Which leads to the problem:
Minimize f
subject to l
(m)
≤ m ≤ u.
One could choose f
One could choose f
kG m − dk2 ≤ δ .
(m) = kG m − dk2 (BVLS)
(m) = cT · mwith
Instructor: Thomas Shores Department of Mathematics
additional constraint
Math 4/896: Seminar in Mathematics Topic: Inverse Theo