Problem 4a
edit
Describe Newton's method for finding a root of a smooth function
f
:
R
→
R
{\displaystyle f:R\rightarrow R\!\,}
Solution 4a
edit
Newton's method:
x
k
+
1
=
x
k
−
f
(
x
k
)
f
′
(
x
k
)
{\displaystyle x_{k+1}=x_{k}-{\frac {f(x_{k})}{f'(x_{k})}}\!\,}
Problem 4b
edit
Assume that
f
:
R
→
R
{\displaystyle f:R\rightarrow R\!\,}
is a smooth function, satisfies
f
′
(
x
)
>
0
,
f
″
(
x
)
>
0
,
for all
x
∈
R
{\displaystyle f'(x)>0,\quad f''(x)>0,\quad {\mbox{ for all }}x\in R\!\,}
and has a root
x
∗
{\displaystyle x^{*}\!\,}
. Draw a geometric picture illustrating the convergence of the method and give an analytic proof that Newton's method converges to
x
∗
{\displaystyle x^{*}\!\,}
for any initial guess
x
0
∈
R
{\displaystyle x_{0}\in R\!\,}
Solution 4b
edit
From the picture, notice that if
x
k
<
x
∗
{\displaystyle x_{k}<x_{*}\!\,}
, then after one step
x
k
+
1
{\displaystyle x_{k+1}\!\,}
will be greater than
x
∗
{\displaystyle x_{*}\!\,}
. This is because from hypothesis, the function is always increasing and concave up.
Then without loss of generality assume
x
k
>
x
∗
{\displaystyle x_{k}>x_{*}\!\,}
.
Subtracting
x
∗
{\displaystyle x_{*}\!\,}
from both sides of Newton's method gives an expression for the relationship between consecutive errors.
x
k
+
1
−
x
∗
⏟
e
k
+
1
=
x
k
−
x
∗
⏟
e
k
−
f
(
x
k
)
f
′
(
x
k
)
(
∗
)
{\displaystyle \underbrace {x_{k+1}-x_{*}} _{e_{k+1}}=\underbrace {x_{k}-x_{*}} _{e_{k}}-{\frac {f(x_{k})}{f'(x_{k})}}\qquad (*)\!\,}
Expanding
f
(
x
k
)
{\displaystyle f(x_{k})\!\,}
around
f
(
x
∗
)
{\displaystyle f(x_{*})\!\,}
using Taylor expansion gives
f
(
x
k
)
=
f
(
x
∗
)
⏟
0
+
f
′
(
ξ
)
(
x
k
−
x
∗
)
⏟
e
k
{\displaystyle f(x_{k})=\underbrace {f(x_{*})} _{0}+f'(\xi )\underbrace {(x_{k}-x_{*})} _{e_{k}}\!\,}
where
ξ
∈
[
x
∗
,
x
k
]
{\displaystyle \xi \in [x_{*},x_{k}]\!\,}
Substituting this expression into (*), we have
e
k
+
1
=
(
1
−
f
′
(
ξ
)
f
′
(
x
k
)
⏟
M
)
e
k
{\displaystyle e_{k+1}=(1-\underbrace {\frac {f'(\xi )}{f'(x_{k})}} _{M})e_{k}\!\,}
Since
f
′
(
x
)
>
0
{\displaystyle f'(x)>0\!\,}
and is always increasing (from hypothesis),
M
{\displaystyle M\!\,}
is a positive number less than 1. Therefore the error decreases as
k
{\displaystyle k\!\,}
increases which implies the method always converges.
Problem 5
edit
The goal of this problem is to solve the boundary value problem
−
u
″
=
f
on
(
0
,
1
)
,
u
(
0
)
=
0
,
u
(
1
)
=
0
{\displaystyle -u''=f{\mbox{ on }}(0,1),\quad u(0)=0,u(1)=0\!\,}
in a suitable finite element space.
Problem 5a
edit
For
N
∈
N
{\displaystyle N\in \mathbb {N} \!\,}
, let
x
i
=
i
/
N
,
i
=
0
,
…
,
N
{\displaystyle x_{i}=i/N,i=0,\ldots ,N\!\,}
. Define a suitable
N
−
1
{\displaystyle N-1\!\,}
dimensional subspace
V
N
{\displaystyle V_{N}\!\,}
in
H
0
1
{\displaystyle H_{0}^{1}\!\,}
associated with the points
x
i
{\displaystyle x_{i}\!\,}
. Let
ϕ
1
,
…
,
ϕ
N
−
1
{\displaystyle \phi _{1},\ldots ,\phi _{N-1}\!\,}
be any basis in
V
N
{\displaystyle V_{N}\!\,}
. Explain how you can determine the coefficients
u
i
{\displaystyle u_{i}\!\,}
in the representation element solution
u
N
=
∑
i
=
1
N
−
1
u
i
ϕ
i
{\displaystyle u_{N}=\sum _{i=1}^{N-1}u_{i}\phi _{i}\!\,}
by solving a linear system. Prove that there exists a unique solution
Solution 5a
edit
Define Suitable Subspace
edit
V
N
=
{
v
∈
H
0
1
[
0
,
1
]
:
v
piecewise linear
}
{\displaystyle V_{N}=\{v\in H_{0}^{1}[0,1]:v{\mbox{ piecewise linear}}\}\!\,}
which has a basis the hat functions
{
ϕ
i
}
i
=
1
N
−
1
{\displaystyle \{\phi _{i}\}_{i=1}^{N-1}\!\,}
defined as follows:
ϕ
i
(
x
)
=
{
x
−
x
i
−
1
h
for
x
∈
[
x
i
−
1
,
x
i
]
x
i
+
1
−
x
h
for
x
∈
[
x
i
,
x
i
+
1
]
0
otherwise
{\displaystyle \phi _{i}(x)={\begin{cases}{\frac {x-x_{i-1}}{h}}&{\mbox{ for }}x\in [x_{i-1},x_{i}]\\{\frac {x_{i+1}-x}{h}}&{\mbox{ for }}x\in [x_{i},x_{i+1}]\\0&{\mbox{ otherwise}}\end{cases}}\!\,}
How to Determine Coefficients
edit
The discrete weak variational form is given as such:
Find
u
N
∈
V
N
{\displaystyle u_{N}\in V_{N}\!\,}
such that for all
v
∈
V
N
{\displaystyle v\in V_{N}\!\,}
∫
0
1
u
N
′
v
N
′
⏟
a
(
u
N
,
v
N
)
=
∫
0
1
f
v
N
⏟
F
(
v
N
)
{\displaystyle \underbrace {\int _{0}^{1}u_{N}'v_{N}'} _{a(u_{N},v_{N})}=\underbrace {\int _{0}^{1}fv_{N}} _{F(v_{N})}\!\,}
Since we have a basis
{
ϕ
i
}
i
=
1
N
−
1
{\displaystyle \{\phi _{i}\}_{i=1}^{N-1}\!\,}
, we have a system of equations (that can be expressed in matrix form):
For
j
=
1
,
2
,
…
,
N
−
1
{\displaystyle j=1,2,\ldots ,N-1\!\,}
∑
i
=
1
N
−
1
u
i
∫
0
1
ϕ
i
ϕ
j
=
∫
0
1
f
ϕ
j
{\displaystyle \sum _{i=1}^{N-1}u_{i}\int _{0}^{1}\phi _{i}\phi _{j}=\int _{0}^{1}f\phi _{j}\!\,}
Existence of Unique Solution
edit
The existence of a unique solution follows from Lax-Milgram.
Note the following:
bilinear form continuous (bounded) e.g.
a
(
u
N
,
v
N
)
≤
‖
u
N
‖
1
‖
v
N
‖
1
{\displaystyle a(u_{N},v_{N})\leq \|u_{N}\|_{1}\|v_{N}\|_{1}\!\,}
a
(
u
,
v
)
=
∫
u
′
v
′
≤
|
u
|
1
|
v
|
1
Cauchy Schwartz in L2
≤
‖
u
‖
1
‖
v
‖
1
dominance of spaces
{\displaystyle {\begin{aligned}a(u,v)&=\int u'v'\\&\leq |u|_{1}|v|_{1}{\mbox{ Cauchy Schwartz in L2 }}\\&\leq \|u\|_{1}\|v\|_{1}{\mbox{ dominance of spaces }}\end{aligned}}\!\,}
bilinear form coercive e.g.
a
(
u
N
,
u
N
)
≥
C
‖
u
N
‖
1
2
{\displaystyle a(u_{N},u_{N})\geq C\|u_{N}\|_{1}^{2}\!\,}
a
(
v
,
v
)
=
∫
v
′
2
=
|
v
|
1
2
≥
C
‖
v
‖
1
2
Poincare inequality
{\displaystyle {\begin{aligned}a(v,v)&=\int v'^{2}\\&=|v|_{1}^{2}\\&\geq C\|v\|_{1}^{2}{\mbox{ Poincare inequality}}\end{aligned}}\!\,}
Poincare Inequality:
‖
v
‖
0
≤
‖
v
‖
1
≤
C
|
v
|
1
{\displaystyle \|v\|_{0}\leq \|v\|_{1}\leq C|v|_{1}\!\,}
F
(
v
N
)
≤
C
‖
v
N
‖
1
{\displaystyle F(v_{N})\leq C\|v_{N}\|_{1}\!\,}
∫
f
v
≤
‖
f
‖
0
‖
v
‖
0
≤
‖
f
‖
1
‖
v
‖
1
{\displaystyle {\begin{aligned}\int fv&\leq \|f\|_{0}\|v\|_{0}\\&\leq \|f\|_{1}\|v\|_{1}\end{aligned}}\!\,}
Problem 5b
edit
Show that
a
(
u
,
v
)
=
∫
0
1
u
′
v
′
d
x
{\displaystyle a(u,v)=\int _{0}^{1}u'v'dx\!\,}
defines an inner product on
H
0
1
{\displaystyle H_{0}^{1}\!\,}
and thus a notion of orthogonality in
H
0
1
{\displaystyle H_{0}^{1}\!\,}
Solution 5b
edit
a
(
u
,
v
)
=
a
(
v
,
u
)
{\displaystyle a(u,v)=a(v,u)\!\,}
a
(
α
u
,
v
)
=
∫
α
u
′
v
′
=
α
∫
u
′
v
′
=
α
a
(
u
,
v
)
{\displaystyle a(\alpha u,v)=\int \alpha u'v'=\alpha \int u'v'=\alpha a(u,v)\!\,}
a
(
u
+
w
,
v
)
=
∫
(
u
′
+
w
′
)
v
′
=
∫
u
′
v
′
+
∫
w
′
v
′
=
a
(
u
,
v
)
+
a
(
w
,
v
)
{\displaystyle a(u+w,v)=\int (u'+w')v'=\int u'v'+\int w'v'=a(u,v)+a(w,v)\!\,}
a
(
u
,
u
)
=
∫
u
′
2
≠
0
if
u
≠
0
{\displaystyle a(u,u)=\int u'^{2}\neq 0{\mbox{ if }}u\neq 0\!\,}
Problem 5c
edit
Solution 5c
edit
ϕ
1
=
{
2
x
for
x
∈
[
0
,
1
2
]
2
−
2
x
for
x
∈
[
1
2
,
1
]
{\displaystyle \phi _{1}={\begin{cases}2x&{\mbox{ for }}x\in [0,{\frac {1}{2}}]\\2-2x&{\mbox{ for }}x\in [{\frac {1}{2}},1]\end{cases}}\!\,}
ϕ
2
=
{
8
x
for
x
∈
[
0
,
1
4
]
−
8
(
x
−
1
2
)
for
x
∈
[
1
4
,
1
2
]
0
otherwise
{\displaystyle \phi _{2}={\begin{cases}8x&{\mbox{ for }}x\in [0,{\frac {1}{4}}]\\-8(x-{\frac {1}{2}})&{\mbox{ for }}x\in [{\frac {1}{4}},{\frac {1}{2}}]\\0&{\mbox{ otherwise }}\end{cases}}\!\,}
ϕ
3
=
{
8
(
x
−
1
2
)
for
x
∈
[
1
2
,
3
4
]
−
8
(
x
−
1
)
for
x
∈
[
1
4
,
1
]
0
otherwise
{\displaystyle \phi _{3}={\begin{cases}8(x-{\frac {1}{2}})&{\mbox{ for }}x\in [{\frac {1}{2}},{\frac {3}{4}}]\\-8(x-1)&{\mbox{ for }}x\in [{\frac {1}{4}},1]\\0&{\mbox{ otherwise }}\end{cases}}\!\,}
Define a new hat function on each new pair of adjoining subintervals. The hat functions should have all have the same height as the previous basis's hat functions.
Problem 5d
edit
What is the structure of the linear system in (a) for this special basis?
Solution 5d
edit
For our system in (a), this system yields a diagonal matrix.
Problem 6
edit
For solving the equation
y
′
=
f
(
x
,
y
)
{\displaystyle y'=f(x,y)\!\,}
, consider the scheme
y
n
+
1
=
y
n
+
h
2
(
y
n
′
+
y
n
+
1
′
)
+
h
2
12
(
y
n
″
−
y
n
+
1
″
)
{\displaystyle y_{n+1}=y_{n}+{\frac {h}{2}}(y_{n}'+y'_{n+1})+{\frac {h^{2}}{12}}(y''_{n}-y''_{n+1})\!\,}
where
y
n
′
=
f
(
x
n
,
y
n
)
{\displaystyle y_{n}'=f(x_{n},y_{n})\!\,}
and
y
n
″
=
f
x
(
x
n
,
y
n
)
+
f
(
x
n
,
y
n
)
f
y
(
x
n
,
y
n
)
{\displaystyle y_{n}''=f_{x}(x_{n},y_{n})+f(x_{n},y_{n})f_{y}(x_{n},y_{n})\!\,}
Problem 6a
edit
Show that this scheme is fourth-order accurate.
Solution 6a
edit
Order
y
(
t
+
h
)
−
y
(
t
)
−
h
2
y
′
(
t
)
−
h
2
y
′
(
t
+
h
)
−
h
2
12
y
″
(
t
)
h
2
12
y
″
(
t
+
h
)
Σ
0
y
(
t
)
−
y
(
t
)
0
0
0
0
0
1
y
′
(
t
)
h
0
−
h
2
y
′
(
t
)
−
h
2
y
′
(
t
)
0
0
0
2
y
″
(
t
)
h
2
2
0
0
−
h
2
y
″
(
t
)
h
−
h
2
12
y
″
(
t
)
h
2
12
y
″
(
t
)
0
3
y
‴
(
t
)
h
3
6
0
0
−
h
2
y
‴
(
t
)
2
h
2
0
h
2
12
y
‴
(
t
)
h
0
4
y
⁗
(
t
)
h
4
24
0
0
−
h
2
y
⁗
(
t
)
h
3
6
0
h
2
12
y
⁗
(
t
)
h
2
2
0
5
O
(
h
5
)
0
0
O
(
h
5
)
0
O
(
h
5
)
O
(
h
5
)
{\displaystyle {\begin{array}{|c|c|c|c|c|c|c|c|}{\mbox{Order}}&y(t+h)&-y(t)&-{\frac {h}{2}}y'(t)&-{\frac {h}{2}}y'(t+h)&-{\frac {h^{2}}{12}}y''(t)&{\frac {h^{2}}{12}}y''(t+h)&\Sigma \\\hline &&&&&&&\\0&y(t)&-y(t)&0&0&0&0&0\\1&y'(t)h&0&-{\frac {h}{2}}y'(t)&-{\frac {h}{2}}y'(t)&0&0&0\\2&{\frac {y''(t)h^{2}}{2}}&0&0&-{\frac {h}{2}}y''(t)h&-{\frac {h^{2}}{12}}y''(t)&{\frac {h^{2}}{12}}y''(t)&0\\3&{\frac {y'''(t)h^{3}}{6}}&0&0&-{\frac {h}{2}}{\frac {y'''(t)}{2}}h^{2}&0&{\frac {h^{2}}{12}}y'''(t)h&0\\4&y''''(t){\frac {h^{4}}{24}}&0&0&-{\frac {h}{2}}y''''(t){\frac {h^{3}}{6}}&0&{\frac {h^{2}}{12}}y''''(t){\frac {h^{2}}{2}}&0\\5&O(h^{5})&0&0&O(h^{5})&0&O(h^{5})&O(h^{5})\\\hline \end{array}}}
Problem 6b
edit
For stability analysis, one takes
f
(
x
,
y
)
=
λ
y
{\displaystyle f(x,y)=\lambda y\!\,}
. State what it means for
λ
¯
=
h
λ
{\displaystyle {\overline {\lambda }}=h\lambda \!\,}
to belong to the region of absolute stability for this scheme, and show that the region of absolute stability contains the entire negative real axis.
Solution 6b
edit
y
n
″
=
d
d
x
λ
y
+
λ
y
d
d
y
λ
y
=
λ
2
y
{\displaystyle y_{n}''={\frac {d}{dx}}\lambda y+\lambda y{\frac {d}{dy}}\lambda y=\lambda ^{2}y\!\,}
y
n
+
1
=
y
n
+
h
2
λ
y
n
+
h
2
λ
y
n
+
1
+
h
2
12
[
λ
2
y
n
−
λ
2
y
n
+
1
]
{\displaystyle y_{n+1}=y_{n}+{\frac {h}{2}}\lambda y_{n}+{\frac {h}{2}}\lambda y_{n+1}+{\frac {h^{2}}{12}}[\lambda ^{2}y_{n}-\lambda ^{2}y_{n+1}]\!\,}
Letting
z
=
h
λ
{\displaystyle z=h\lambda \!\,}
and rearranging terms gives
y
n
+
1
=
(
1
+
z
2
+
z
2
12
1
−
z
2
+
z
2
12
)
⏟
M
y
n
{\displaystyle y_{n+1}=\underbrace {\left({\frac {1+{\frac {z}{2}}+{\frac {z^{2}}{12}}}{1-{\frac {z}{2}}+{\frac {z^{2}}{12}}}}\right)} _{M}y_{n}\!\,}
If
z
{\displaystyle z\!\,}
is a negative real number, then
M
<
1
{\displaystyle M<1\!\,}