Consider the system
x
=
1
+
h
e
−
x
2
1
+
y
2
,
y
=
.5
+
h
arctan
(
x
2
+
y
2
)
{\displaystyle x=1+h{\frac {e^{-x^{2}}}{1+y^{2}}}{\mbox{,}}\qquad y=.5+h\arctan(x^{2}+y^{2})\!\,}
.
Show that if the parameter
h
>
0
{\displaystyle h>0\!\,}
is chosen sufficiently small, then this system has a unique solution
(
x
∗
,
y
∗
)
{\displaystyle (x^{*},y^{*})\!\,}
within some rectangular region.
The system of equations may be expressed in matrix notation.
f
(
x
,
y
)
=
[
1
+
h
e
−
x
2
1
+
y
2
.5
+
h
arctan
(
x
2
+
y
2
)
]
{\displaystyle f(x,y)={\begin{bmatrix}1+h{\frac {e^{-x^{2}}}{1+y^{2}}}\\\\.5+h\arctan(x^{2}+y^{2})\end{bmatrix}}\!\,}
The Jacobian of
f
(
x
,
y
)
{\displaystyle f(x,y)\!\,}
,
J
(
f
)
{\displaystyle J(f)\!\,}
, is computed using partial derivatives
J
(
f
)
=
[
h
1
+
y
2
e
−
x
2
(
−
2
x
)
−
h
2
y
e
−
x
2
(
1
+
y
2
)
2
h
⋅
2
x
1
+
(
x
2
+
y
2
)
2
h
⋅
2
y
1
+
(
x
2
+
y
2
)
2
]
{\displaystyle J(f)={\begin{bmatrix}{\frac {h}{1+y^{2}}}e^{-x^{2}}(-2x)&{\frac {-h2ye^{-x^{2}}}{(1+y^{2})^{2}}}\\h\cdot {\frac {2x}{1+(x^{2}+y^{2})^{2}}}&h\cdot {\frac {2y}{1+(x^{2}+y^{2})^{2}}}\end{bmatrix}}\!\,}
If
h
{\displaystyle h\!\,}
is sufficiently small and
x
,
y
{\displaystyle x,y\!\,}
are restricted to a bounded region
B
{\displaystyle B\!\,}
,
‖
J
(
f
)
‖
⏟
C
<
1
{\displaystyle \underbrace {\|J(f)\|} _{C}<1\!\,}
From the mean value theorem, for
x
1
→
,
x
2
→
∈
B
{\displaystyle {\vec {x_{1}}},{\vec {x_{2}}}\in B\!\,}
, there exists
ξ
→
{\displaystyle {\vec {\xi }}\!\,}
such that
f
(
x
1
→
)
−
f
(
x
2
→
)
=
J
(
f
(
ξ
→
)
)
(
x
1
→
−
x
2
→
)
{\displaystyle f({\vec {x_{1}}})-f({\vec {x_{2}}})=J(f({\vec {\xi }}))({\vec {x_{1}}}-{\vec {x_{2}}})\!\,}
Since
‖
J
(
f
)
‖
{\displaystyle \|J(f)\|\!\,}
is bounded in the region
B
{\displaystyle B\!\,}
give sufficiently small
h
{\displaystyle h\!\,}
‖
f
(
x
1
→
)
−
f
(
x
2
→
)
‖
≤
C
‖
x
1
→
−
x
2
→
‖
{\displaystyle \|f({\vec {x_{1}}})-f({\vec {x_{2}}})\|\leq C\|{\vec {x_{1}}}-{\vec {x_{2}}}\|\!\,}
Therefore,
f
{\displaystyle f\!\,}
is a contraction and from the contraction mapping theorem there exists an unique fixed point (solution) in a rectangular region.
Derive a fixed point iteration scheme for solving the system and show that it converges.
{\displaystyle \!\,}
To solve this problem, we can use the Newton Method. In fact, we want to find the zeros of
g
(
x
,
y
)
=
[
x
y
]
−
[
1
+
h
e
−
x
2
1
+
y
2
.5
+
h
arctan
(
x
2
+
y
2
)
]
{\displaystyle g(x,y)={\begin{bmatrix}x\\\\y\end{bmatrix}}-{\begin{bmatrix}1+h{\frac {e^{-x^{2}}}{1+y^{2}}}\\\\.5+h\arctan(x^{2}+y^{2})\end{bmatrix}}\!\,}
The Jacobian of
g
(
x
,
y
)
{\displaystyle g(x,y)\!\,}
,
J
(
g
)
{\displaystyle J(g)\!\,}
, is computed using partial derivatives
J
(
g
)
=
[
1
−
h
1
+
y
2
e
−
x
2
(
−
2
x
)
−
h
e
−
x
2
1
+
y
2
h
⋅
2
x
1
+
(
x
2
+
y
2
)
2
1
−
h
⋅
2
y
1
+
(
x
2
+
y
2
)
2
]
{\displaystyle J(g)={\begin{bmatrix}1-{\frac {h}{1+y^{2}}}e^{-x^{2}}(-2x)&{\frac {-he^{-x^{2}}}{1+y^{2}}}\\h\cdot {\frac {2x}{1+(x^{2}+y^{2})^{2}}}&1-h\cdot {\frac {2y}{1+(x^{2}+y^{2})^{2}}}\end{bmatrix}}\!\,}
Then, the Newton method for solving this linear system of equations is given by
[
x
k
+
1
y
k
+
1
]
=
[
x
k
y
k
]
−
1
d
e
t
(
J
(
g
)
)
[
1
−
h
⋅
2
y
1
+
(
x
2
+
y
2
)
2
h
e
−
x
2
1
+
y
2
−
h
⋅
2
x
1
+
(
x
2
+
y
2
)
2
1
−
h
1
+
y
2
e
−
x
2
(
−
2
x
)
]
⏟
J
(
g
)
−
1
g
(
x
k
,
y
k
)
{\displaystyle {\begin{bmatrix}x_{k+1}\\\\y_{k+1}\end{bmatrix}}={\begin{bmatrix}x_{k}\\\\y_{k}\end{bmatrix}}-\underbrace {{\frac {1}{det(J(g))}}{\begin{bmatrix}1-h\cdot {\frac {2y}{1+(x^{2}+y^{2})^{2}}}&{\frac {he^{-x^{2}}}{1+y^{2}}}\\-h\cdot {\frac {2x}{1+(x^{2}+y^{2})^{2}}}&1-{\frac {h}{1+y^{2}}}e^{-x^{2}}(-2x)\end{bmatrix}}} _{J(g)^{-1}}g(x_{k},y_{k})\!\,}
Show convergence by showing Newton hypothesis hold
edit
Jacobian of g is Lipschitz
edit
We want to show that
J
(
g
)
{\displaystyle J(g)\!\,}
is a Lipschitz function. In fact,
‖
J
(
g
)
(
x
1
→
)
−
J
(
g
)
(
x
2
→
)
‖
=
‖
(
I
−
J
(
f
)
)
(
x
1
→
)
−
(
I
−
J
(
f
)
)
(
x
2
→
)
‖
=
‖
x
1
→
−
x
2
→
−
J
(
f
)
x
1
→
+
J
(
f
)
x
2
→
‖
≤
‖
x
1
→
−
x
2
→
‖
+
‖
J
(
f
)
x
2
→
−
J
(
f
)
x
1
→
‖
{\displaystyle {\begin{aligned}\|J(g)({\vec {x_{1}}})-J(g)({\vec {x_{2}}})\|&=\|(I-J(f))({\vec {x_{1}}})-(I-J(f))({\vec {x_{2}}})\|\\&=\|{\vec {x_{1}}}-{\vec {x_{2}}}-J(f){\vec {x_{1}}}+J(f){\vec {x_{2}}}\|\\&\leq \|{\vec {x_{1}}}-{\vec {x_{2}}}\|+\|J(f){\vec {x_{2}}}-J(f){\vec {x_{1}}}\|\end{aligned}}\!\,}
and now, using that
J
(
f
)
{\displaystyle J(f)\!\,}
is Lipschitz, we have
‖
J
(
g
)
(
x
1
→
)
−
J
(
g
)
(
x
2
→
)
‖
≤
(
1
+
C
)
‖
(
x
1
→
)
−
(
x
2
→
)
‖
{\displaystyle \|J(g)({\vec {x_{1}}})-J(g)({\vec {x_{2}}})\|\leq (1+C)\|({\vec {x_{1}}})-({\vec {x_{2}}})\|\!\,}
Jacobian of g is invertible
edit
Since
J
(
f
)
{\displaystyle J(f)\!\,}
is a contraction, the spectral radius of the Jacobian of
f
{\displaystyle f\!\,}
is less than 1 i.e.
ρ
(
J
(
f
)
)
<
1
{\displaystyle \rho (J(f))<1\!\,}
.
On the other hand, we know that the eigenvalues of
J
(
g
)
{\displaystyle J(g)\!\,}
are
λ
(
J
(
g
)
)
=
1
−
λ
(
J
(
f
)
)
{\displaystyle \lambda (J(g))=1-\lambda (J(f))\!\,}
.
Then, it follows that
d
e
t
(
I
−
(
J
(
g
)
)
)
≠
0
{\displaystyle det(I-(J(g)))\neq 0\!\,}
or equivalently
J
(
g
)
{\displaystyle J(g)\!\,}
is invertible.
inverse of Jacobian of g is bounded
edit
1
d
e
t
(
J
(
g
)
)
[
1
−
h
⋅
2
y
1
+
(
x
2
+
y
2
)
2
h
e
−
x
2
1
+
y
2
−
h
⋅
2
x
1
+
(
x
2
+
y
2
)
2
1
−
h
1
+
y
2
e
−
x
2
(
−
2
x
)
]
⏟
J
(
g
)
−
1
{\displaystyle \underbrace {{\frac {1}{det(J(g))}}{\begin{bmatrix}1-h\cdot {\frac {2y}{1+(x^{2}+y^{2})^{2}}}&{\frac {he^{-x^{2}}}{1+y^{2}}}\\-h\cdot {\frac {2x}{1+(x^{2}+y^{2})^{2}}}&1-{\frac {h}{1+y^{2}}}e^{-x^{2}}(-2x)\end{bmatrix}}} _{J(g)^{-1}}\!\,}
Since
J
(
g
)
−
1
{\displaystyle J(g)^{-1}\!\,}
exists,
det
(
J
(
g
)
)
≠
0
{\displaystyle {\mbox{det}}(J(g))\neq 0\!\,}
. Given a bounded region (bounded
x
,
y
{\displaystyle x,y\!\,}
), each entry of the above matrix is bounded. Therefore the norm is bounded.
norm of (Jacobian of g)^-1 (x_0) * g(x_0) bounded
edit
‖
J
(
g
)
−
1
(
x
0
)
∗
g
(
x
0
)
‖
<
∞
{\displaystyle \|J(g)^{-1}(x_{0})*g(x_{0})\|<\infty \!\,}
since
J
(
g
)
−
1
{\displaystyle J(g)^{-1}\!\,}
and
g
(
x
)
{\displaystyle g(x)\!\,}
is bounded.
Then, using a good enough approximation
(
x
0
,
y
0
)
{\displaystyle (x_{0},y_{0})\!\,}
, we have that the Newton's method is at least quadratically convergent, i.e,
‖
(
x
k
−
1
,
y
k
+
1
)
−
(
x
∗
,
y
∗
)
‖
≤
K
‖
(
x
k
,
y
k
)
−
(
x
∗
,
y
∗
)
‖
2
.
{\displaystyle \|(x_{k-1},y_{k+1})-(x^{*},y^{*})\|\leq K\|(x_{k},y_{k})-(x^{*},y^{*})\|^{2}.\!\,}
Outline the derivation of the Adams-Bashforth methods for the numerical solution of the initial value problem
y
′
=
f
(
x
,
y
)
,
y
(
x
0
)
=
y
0
{\displaystyle y'=f(x,y){\mbox{, }}y(x_{0})=y_{0}\!\,}
.
We want to solve the following initial value problem:
y
′
=
f
(
x
,
y
)
,
y
(
x
0
)
=
y
0
{\displaystyle y'=f(x,y){\mbox{, }}y(x_{0})=y_{0}\!\,}
.
First, we integrate this expression over
[
t
n
,
t
n
+
1
]
{\displaystyle [t_{n},t_{n+1}]\!\,}
, to obtain
y
(
t
n
+
1
)
=
y
(
t
n
)
+
∫
t
n
t
n
+
1
f
(
t
,
y
(
t
)
)
d
t
{\displaystyle y(t_{n+1})=y(t_{n})+\int _{t_{n}}^{t_{n+1}}f(t,y(t))dt\!\,}
,
To approximate the integral on the right hand side, we approximate its integrand
f
(
t
,
y
(
t
)
)
{\displaystyle f(t,y(t))\!\,}
using an appropriate interpolation polynomial of degree
p
{\displaystyle p\!\,}
at
t
n
,
t
n
−
1
,
.
.
.
,
t
n
−
p
{\displaystyle t_{n},t_{n-1},...,t_{n-p}\!\,}
.
This idea generates the Adams-Bashforth methods.
y
n
+
1
=
y
n
+
∫
t
n
t
n
−
1
∑
k
=
0
p
g
(
t
n
−
k
)
l
k
(
t
)
d
t
{\displaystyle y_{n+1}=y_{n}+\int _{t_{n}}^{t_{n-1}}\sum _{k=0}^{p}g(t_{n-k})l_{k}(t)dt\!\,}
,
where,
y
n
{\displaystyle y_{n}\!\,}
denotes the approximated solution,
g
(
t
)
≡
f
(
t
,
y
)
{\displaystyle g(t)\equiv f(t,y)\!\,}
and
l
k
(
t
)
{\displaystyle l_{k}(t)\!\,}
denotes the associated Lagrange polynomial.
Derive the Adams-Bashforth formula
y
i
+
1
=
y
i
+
h
[
−
1
2
f
(
x
i
−
1
,
y
i
−
1
)
+
3
2
f
(
x
i
,
y
i
)
]
(
1
)
{\displaystyle y_{i+1}=y_{i}+h\left[-{\frac {1}{2}}f(x_{i-1},y_{i-1})+{\frac {3}{2}}f(x_{i},y_{i})\right]\qquad \qquad (1)\!\,}
From (a) we have
y
i
+
1
=
y
i
+
∫
x
i
x
i
+
1
f
d
x
{\displaystyle y_{i+1}=y_{i}+\int _{x_{i}}^{x_{i+1}}fdx\!\,}
where
∫
x
i
x
i
+
1
f
d
x
≈
∫
x
i
x
i
+
1
[
x
−
x
i
x
i
−
1
−
x
i
f
i
−
1
+
x
−
x
i
−
1
x
i
−
x
i
−
1
f
i
]
d
x
{\displaystyle \int _{x_{i}}^{x_{i+1}}fdx\approx \int _{x_{i}}^{x_{i+1}}\left[{\frac {x-x_{i}}{x_{i-1}-x_{i}}}f_{i-1}+{\frac {x-x_{i-1}}{x_{i}-x_{i-1}}}f_{i}\right]dx\!\,}
Then if we let
x
=
x
i
+
s
h
{\displaystyle x=x_{i}+sh\!\,}
, where h is the fixed step size we get
d
x
=
h
d
s
x
−
x
i
=
s
h
x
−
x
i
−
1
=
(
1
+
s
)
h
x
i
−
x
i
−
1
=
h
{\displaystyle {\begin{aligned}dx&=hds\\x-x_{i}&=sh\\x-x_{i-1}&=(1+s)h\\x_{i}-x_{i-1}&=h\end{aligned}}}
h
∫
0
1
[
s
h
−
h
f
i
−
1
+
(
1
+
s
)
h
h
f
i
]
d
s
=
h
[
−
1
2
f
i
−
1
+
3
2
f
i
]
{\displaystyle h\int _{0}^{1}\left[{\frac {sh}{-h}}f_{i-1}+{\frac {(1+s)h}{h}}f_{i}\right]ds=h\left[-{\frac {1}{2}}f_{i-1}+{\frac {3}{2}}f_{i}\right]\!\,}
So we have the Adams-Bashforth method as desired
y
i
+
1
=
y
i
+
h
[
−
1
2
f
(
x
i
−
1
,
y
i
−
1
)
+
3
2
f
(
x
i
,
y
i
)
]
{\displaystyle y_{i+1}=y_{i}+h\left[-{\frac {1}{2}}f(x_{i-1},y_{i-1})+{\frac {3}{2}}f(x_{i},y_{i})\right]\!\,}
Analyze the method (1). To be specific, find the local truncation error, prove convergence and find the order of convergence.
Find Local Truncation Error Using Taylor Expansion
edit
Note that
y
i
≈
y
(
t
i
)
{\displaystyle y_{i}\approx y(t_{i})\!\,}
. Also, denote the uniform step size
Δ
t
{\displaystyle \Delta t\!\,}
as h. Hence,
t
i
−
1
=
t
i
−
h
{\displaystyle t_{i-1}=t_{i}-h\!\,}
t
i
+
1
=
t
i
+
h
{\displaystyle t_{i+1}=t_{i}+h\!\,}
Therefore, the given equation may be written as
y
(
t
i
+
h
)
−
y
(
t
i
)
≈
h
[
−
1
2
y
′
(
t
i
−
h
)
+
3
2
y
′
(
t
i
)
]
{\displaystyle y(t_{i}+h)-y(t_{i})\approx h\left[-{\frac {1}{2}}y'(t_{i}-h)+{\frac {3}{2}}y'(t_{i})\right]\!\,}
Expand Left Hand Side
edit
Expanding about
t
i
{\displaystyle t_{i}\!\,}
, we get
Order
y
(
t
i
+
h
)
y
(
t
i
)
Σ
0
y
(
t
i
)
y
(
t
i
)
0
1
−
y
′
(
t
i
)
h
0
−
y
′
(
t
i
)
h
2
1
2
!
y
″
(
t
i
)
h
2
0
1
2
y
″
(
t
i
)
h
2
3
−
1
3
!
y
‴
(
t
i
)
h
3
0
−
1
6
y
‴
(
t
i
)
h
3
4
O
(
h
4
)
0
O
(
h
4
)
{\displaystyle {\begin{array}{|c|c|c|c|}{\mbox{Order}}&y(t_{i}+h)&y(t_{i})&\Sigma \\\hline &&&\\0&y(t_{i})&y(t_{i})&0\\&&&\\1&-y'(t_{i})h&0&-y'(t_{i})h\\&&&\\2&{\frac {1}{2!}}y''(t_{i})h^{2}&0&{\frac {1}{2}}y''(t_{i})h^{2}\\&&&\\3&-{\frac {1}{3!}}y'''(t_{i})h^{3}&0&-{\frac {1}{6}}y'''(t_{i})h^{3}\\&&&\\4&{\mathcal {O}}(h^{4})&0&{\mathcal {O}}(h^{4})\\&&&\\\hline \end{array}}}
Expand Right Hand side
edit
Also expanding about
t
i
{\displaystyle t_{i}\!\,}
gives
Order
13
12
h
x
′
(
t
i
+
2
h
)
−
5
3
h
x
′
(
t
i
+
h
)
−
5
12
h
x
′
(
t
i
)
Σ
0
0
0
0
0
1
13
12
h
x
′
(
t
i
)
−
5
3
h
x
′
(
t
i
)
−
5
12
h
x
′
(
t
i
)
−
1
⋅
h
x
′
(
t
i
)
2
13
12
(
2
h
)
h
x
″
(
t
i
)
−
5
3
h
h
x
″
(
t
i
)
0
1
2
h
2
x
″
(
t
i
)
3
13
12
(
2
h
)
2
h
x
‴
(
t
i
)
2
−
5
3
h
2
h
x
‴
(
t
i
)
2
0
4
3
h
3
x
‴
(
t
i
)
4
O
(
h
4
)
O
(
h
4
)
0
O
(
h
4
)
{\displaystyle {\begin{array}{|c|c|c|c|c|}{\mbox{Order}}&{\frac {13}{12}}hx'(t_{i}+2h)&-{\frac {5}{3}}hx'(t_{i}+h)&-{\frac {5}{12}}hx'(t_{i})&\Sigma \\\hline &&&&\\0&0&0&0&0\\&&&&\\1&{\frac {13}{12}}hx'(t_{i})&-{\frac {5}{3}}hx'(t_{i})&-{\frac {5}{12}}hx'(t_{i})&-1\cdot hx'(t_{i})\\&&&&\\2&{\frac {13}{12}}(2h)hx''(t_{i})&-{\frac {5}{3}}hhx''(t_{i})&0&{\frac {1}{2}}h^{2}x''(t_{i})\\&&&&\\3&{\frac {13}{12}}(2h)^{2}{\frac {hx'''(t_{i})}{2}}&-{\frac {5}{3}}h^{2}{\frac {hx'''(t_{i})}{2}}&0&{\frac {4}{3}}h^{3}x'''(t_{i})\\&&&&\\4&{\mathcal {O}}(h^{4})&{\mathcal {O}}(h^{4})&0&{\mathcal {O}}(h^{4})\\\hline \end{array}}}
Show Convergence by Showing Stability and Consistency
edit
A method is convergent if and only if it is both stable and consistent.
It is easy to show that the method is zero stable as it satisfies the root condition. So stable.
Truncation error is of order 2. Truncation error tends to zero as h tends to zeros. So the method is consistent.
Order of Convergence
edit
Dahlquist principle: consistency + stability = convergent. Order of convergence will be 2.
Consider the problem
−
u
″
+
u
=
f
(
x
)
,
0
≤
x
≤
1
,
u
(
0
)
=
u
(
1
)
=
0.
(
2
)
{\displaystyle -u''+u=f(x),\;\;0\leq x\leq 1,\;\;u(0)=u(1)=0.\qquad \qquad (2)}
Give a variational formulation of (2), i.e. express (2) as
u
∈
H
,
B
(
x
,
y
)
=
F
(
v
)
,
∀
v
∈
H
(
3
)
{\displaystyle u\in H,\;\;B(x,y)=F(v),\;\;\forall v\in H\qquad \qquad (3)\!\,}
Define the Space H, the bilinear form B and the linear functional F, and state the relation between (2) and (3).
Multiplying (2) by a test function and using integration by parts we obtain:
−
∫
0
1
u
″
v
d
x
+
∫
0
1
u
v
d
x
=
∫
0
1
f
v
d
x
{\displaystyle -\int _{0}^{1}u''vdx+\int _{0}^{1}uvdx=\int _{0}^{1}fvdx\!\,}
−
u
′
v
|
0
1
⏟
0
+
∫
0
1
u
′
v
′
d
x
+
∫
0
1
u
v
d
x
=
∫
0
1
f
v
d
x
{\displaystyle \underbrace {\left.-u'v\right|_{0}^{1}} _{0}+\int _{0}^{1}u'v'dx+\int _{0}^{1}uvdx=\int _{0}^{1}fvdx\!\,}
Thus, the weak form or variational form associated with the problem (2) reads as follows: Find
u
∈
H
{\displaystyle u\in H\!\,}
such that
B
(
u
,
v
)
=
∫
0
1
u
′
v
′
d
x
+
∫
0
1
u
v
d
x
=
∫
0
1
f
v
d
x
=
F
(
v
)
{\displaystyle B(u,v)=\int _{0}^{1}u'v'dx+\int _{0}^{1}uvdx=\int _{0}^{1}fvdx=F(v)\!\,}
for all
v
∈
H
,
{\displaystyle v\in H,\!\,}
where
H
:=
H
0
1
(
0
,
1
)
=
{
v
:
v
∈
H
1
,
v
(
0
)
=
v
(
1
)
=
0
}
{\displaystyle H:=H_{0}^{1}(0,1)=\{v:v\in H^{1},v(0)=v(1)=0\}\!\,}
.
Let
0
=
x
0
<
x
1
<
⋯
<
x
n
=
1
{\displaystyle 0=x_{0}<x_{1}<\cdots <x_{n}=1\!\,}
be a mesh on
[
0
,
1
]
{\displaystyle [0,1]\!\,}
with
h
=
max
j
=
0
,
1
,
…
,
n
−
1
(
x
j
+
1
−
x
j
)
{\displaystyle h=\max _{j=0,1,\dots ,n-1}(x_{j+1}-x_{j})\!\,}
, and let
V
h
=
{
u
:
u
continuous on
[
0
,
1
]
,
u
|
[
x
j
,
x
j
+
1
]
is linear for each
j
,
u
(
0
)
=
u
(
1
)
=
0
}
{\displaystyle V_{h}=\left\{u:u{\mbox{ continuous on }}[0,1],u|_{[x_{j},x_{j+1}]}{\mbox{ is linear for each }}j,u(0)=u(1)=0\right\}\!\,}
.
Define the finite element approximation,
u
h
to
u
{\displaystyle u_{h}{\mbox{ to }}u\!\,}
based on the approximation space
V
h
{\displaystyle V_{h}\!\,}
. What can be said about
‖
u
−
u
h
‖
1
{\displaystyle \|u-u_{h}\|_{1}\!\,}
the error on the Sobolev norm on
H
1
(
0
,
1
)
{\displaystyle H^{1}(0,1)\!\,}
?
Define piecewise linear basis
edit
For our basis of
V
h
{\displaystyle V_{h}\!\,}
, we use the set of hat functions
{
ϕ
j
}
j
=
1
n
−
1
{\displaystyle \{\phi _{j}\}_{j=1}^{n-1}\!\,}
, i.e., for
j
=
1
,
2
,
…
,
n
−
1
{\displaystyle j=1,2,\ldots ,n-1\!\,}
ϕ
j
(
x
)
=
{
x
−
x
j
−
1
x
j
−
x
j
−
1
for
x
∈
[
x
j
−
1
,
x
j
]
x
j
+
1
−
x
x
j
+
1
−
x
j
for
x
∈
[
x
j
,
x
j
+
1
]
0
otherwise
{\displaystyle \phi _{j}(x)={\begin{cases}{\frac {x-x_{j-1}}{x_{j}-x_{j-1}}}&{\mbox{for }}x\in [x_{j-1},x_{j}]\\{\frac {x_{j+1}-x}{x_{j+1}-x_{j}}}&{\mbox{for }}x\in [x_{j},x_{j+1}]\\0&{\mbox{otherwise}}\end{cases}}\!\,}
Since
{
ϕ
j
}
j
=
1
n
−
1
{\displaystyle \{\phi _{j}\}_{j=1}^{n-1}\!\,}
is a basis for
V
h
{\displaystyle V_{h}\!\,}
, and
u
h
∈
V
h
{\displaystyle u_{h}\in V_{h}\!\,}
we have
u
h
=
∑
j
=
1
n
−
1
u
h
j
ϕ
j
{\displaystyle u_{h}=\sum _{j=1}^{n-1}u_{hj}\phi _{j}\!\,}
.
Now, we can write the discrete problem: Find
u
h
∈
V
h
{\displaystyle u_{h}\in V_{h}\!\,}
such that
B
(
u
h
,
v
h
)
=
F
(
v
h
)
{\displaystyle B(u_{h},v_{h})=F(v_{h})\!\,}
for all
v
h
∈
V
h
.
{\displaystyle v_{h}\in V_{h}.\!\,}
If we consider that
{
ϕ
j
}
j
=
1
n
−
1
{\displaystyle \{\phi _{j}\}_{j=1}^{n-1}\!\,}
is a basis of
V
h
{\displaystyle V_{h}\!\,}
and the linearity of the bilinear form
B
{\displaystyle B\!\,}
and the functional
F
{\displaystyle F\!\,}
, we obtain the equivalent problem:
Find
u
h
=
(
u
h
,
1
,
…
,
u
h
,
n
−
1
)
∈
R
n
−
1
{\displaystyle u_{h}=(u_{h,1},\dots ,u_{h,{n-1}})\in \mathbb {R} ^{n-1}\!\,}
such that
∑
j
=
1
n
−
1
u
h
,
j
{
∫
0
1
ϕ
j
′
ϕ
i
′
d
x
+
∫
0
1
ϕ
j
ϕ
i
d
x
}
=
∫
0
1
f
ϕ
i
d
x
,
i
=
1
,
…
,
n
−
1.
{\displaystyle \sum _{j=1}^{n-1}u_{h,j}\left\{\int _{0}^{1}\phi _{j}'\phi _{i}'dx+\int _{0}^{1}\phi _{j}\phi _{i}dx\right\}=\int _{0}^{1}f\phi _{i}dx,\quad i=1,\dots ,n-1.\!\,}
This last problem can be formulated as a matrix problem as follows:
Find
u
h
=
(
u
h
,
1
,
…
,
u
h
,
n
−
1
)
∈
R
n
−
1
{\displaystyle u_{h}=(u_{h,1},\dots ,u_{h,{n-1}})\in \mathbb {R} ^{n-1}\!\,}
such that
A
u
h
=
F
,
{\displaystyle Au_{h}=F,\!\,}
where
A
i
j
=
∫
0
1
ϕ
j
′
ϕ
i
′
d
x
+
∫
0
1
ϕ
j
ϕ
i
d
x
{\displaystyle A_{ij}=\int _{0}^{1}\phi _{j}'\phi _{i}'dx+\int _{0}^{1}\phi _{j}\phi _{i}dx\!\,}
and
F
j
=
∫
0
1
f
ϕ
i
d
x
{\displaystyle F_{j}=\int _{0}^{1}f\phi _{i}dx\!\,}
.
In general terms, we can use Cea's Lemma to obtain
‖
u
−
u
h
‖
1
≤
C
inf
v
h
∈
V
h
‖
u
−
v
h
‖
1
{\displaystyle \|u-u_{h}\|_{1}\leq C\inf _{v_{h}\in V_{h}}\|u-v_{h}\|_{1}}
In particular, we can consider
v
h
{\displaystyle v_{h}\!\,}
as the Lagrange interpolant, which we denote by
v
I
{\displaystyle v_{I}\!\,}
. Then,
‖
u
−
u
h
‖
1
≤
C
‖
u
−
v
I
‖
1
≤
C
h
‖
u
‖
H
2
(
0
,
1
)
{\displaystyle \|u-u_{h}\|_{1}\leq C\|u-v_{I}\|_{1}\leq Ch\|u\|_{H^{2}(0,1)}}
.
It's easy to prove that the finite element solution is nodally exact. Then it coincides with the Lagrange interpolant, and we have the following punctual estimation:
‖
u
(
x
)
−
u
h
(
x
)
‖
L
∞
(
[
x
i
−
1
,
x
i
]
)
≤
max
x
∈
[
x
i
−
1
,
x
i
]
u
(
2
)
(
x
)
(
2
)
!
(
h
2
)
2
{\displaystyle \|u(x)-u_{h}(x)\|_{L^{\infty }([x_{i-1},x_{i}])}\leq \max _{x\in [x_{i-1},x_{i}]}{\frac {u^{(2)}(x)}{(2)!}}\left({\frac {h}{2}}\right)^{2}}
Derive the estimate for
‖
u
−
u
h
‖
0
{\displaystyle \|u-u_{h}\|_{0}\!\,}
, the error in
L
2
(
0
,
1
)
{\displaystyle L_{2}(0,1)\!\,}
. Hint: Let w solve
(#) :
{
−
w
″
+
w
=
u
−
u
h
,
w
(
0
)
=
w
(
1
)
=
0.
{\displaystyle {\begin{cases}-w''+w=u-u_{h},\\w(0)=w(1)=0.\end{cases}}}
We characterize
w
{\displaystyle w\!\,}
variationally as
w
∈
H
0
1
,
B
(
w
,
v
)
=
∫
(
u
−
u
h
)
v
d
x
,
∀
v
∈
H
0
1
{\displaystyle w\in H_{0}^{1},\;\;B(w,v)=\int (u-u_{h})vdx,\forall v\in H_{0}^{1}\!\,}
.
Let
v
=
u
−
u
h
{\displaystyle v=u-u_{h}\!\,}
to get
B
(
w
,
u
−
u
h
)
=
∫
(
u
−
u
h
)
2
d
x
=
‖
u
−
u
h
‖
L
2
2
.
(
4
)
{\displaystyle B(w,u-u_{h})=\int (u-u_{h})^{2}dx=\|u-u_{h}\|_{L_{2}}^{2}.\qquad \qquad (4)\!\,}
Use formula (4) to estimate
‖
u
−
u
h
‖
L
2
{\displaystyle \|u-u_{h}\|_{L_{2}}\!\,}
.
B
(
u
,
v
)
=
∫
u
v
+
∫
u
′
v
′
≤
‖
u
‖
0
‖
v
‖
0
+
‖
u
′
‖
0
‖
v
′
‖
0
(Cauchy-Schwarz in L2)
≤
(
‖
u
‖
0
2
+
‖
u
′
‖
0
2
)
1
2
(
‖
v
‖
0
2
+
‖
v
′
‖
2
)
1
2
( Cauchy-Schwarz in R2 )
=
‖
u
‖
1
⋅
‖
v
‖
1
{\displaystyle {\begin{aligned}B(u,v)&=\int uv+\int u'v'\\&\leq \|u\|_{0}\|v\|_{0}+\|u'\|_{0}\|v'\|_{0}{\mbox{ (Cauchy-Schwarz in L2) }}\\&\leq (\|u\|_{0}^{2}+\|u'\|_{0}^{2})^{\frac {1}{2}}(\|v\|_{0}^{2}+\|v'\|^{2})^{\frac {1}{2}}{\mbox{ ( Cauchy-Schwarz in R2 ) }}\\&=\|u\|_{1}\cdot \|v\|_{1}\end{aligned}}\!\,}
Orthogonality of the Error
edit
B
(
u
−
u
h
,
v
h
)
=
0
,
∀
v
h
∈
V
h
{\displaystyle B(u-u_{h},v_{h})=0,\quad \forall v_{h}\in V_{h}\!\,}
.
Bound for L2 norm of w
edit
‖
w
‖
0
2
≤
‖
w
‖
1
2
=
B
(
w
,
w
)
=
∫
(
u
−
u
h
)
w
≤
‖
u
−
u
h
‖
0
‖
w
‖
0
(Cauchy-Schwarz in L2 )
{\displaystyle {\begin{aligned}\|w\|_{0}^{2}&\leq \|w\|_{1}^{2}\\&=B(w,w)\\&=\int (u-u_{h})w\\&\leq \|u-u_{h}\|_{0}\|w\|_{0}{\mbox{ (Cauchy-Schwarz in L2 ) }}\end{aligned}}\!\,}
Hence,
(
∗
)
‖
w
‖
0
≤
‖
u
−
u
h
‖
0
{\displaystyle (*)\qquad \|w\|_{0}\leq \|u-u_{h}\|_{0}\!\,}
Bound for L2 norm of w
edit
From
(
#
)
{\displaystyle (\#)\!\,}
, we have
−
w
″
+
w
=
u
−
u
h
{\displaystyle -w''+w=u-u_{h}\!\,}
Then,
‖
w
″
‖
0
≤
‖
w
‖
0
+
‖
u
−
u
h
‖
0
(triangle inequality)
≤
‖
u
−
u
h
‖
0
+
‖
u
−
u
h
‖
0
(by (*) )
=
2
‖
u
−
u
h
‖
0
{\displaystyle {\begin{aligned}\|w''\|_{0}&\leq \|w\|_{0}+\|u-u_{h}\|_{0}{\mbox{ (triangle inequality) }}\\&\leq \|u-u_{h}\|_{0}+\|u-u_{h}\|_{0}{\mbox{ (by (*) ) }}\\&=2\|u-u_{h}\|_{0}\end{aligned}}\!\,}
‖
u
−
u
h
‖
L
2
2
=
B
(
w
,
u
−
u
h
)
( from equation (4) )
=
B
(
w
,
u
−
u
h
)
−
B
(
w
h
,
u
−
u
h
)
⏟
0
=
B
(
w
−
w
h
,
u
−
u
h
)
≤
‖
w
−
w
h
‖
H
1
(
0
,
1
)
‖
u
−
u
h
‖
H
1
(
0
,
1
)
(Continuity of Bilinear Form)
≤
C
h
‖
w
‖
H
2
(
0
,
1
)
‖
u
−
u
h
‖
H
1
(
0
,
1
)
(Cea Lemma and Polynomial Interpolation Error)
{\displaystyle {\begin{aligned}\|u-u_{h}\|_{L^{2}}^{2}&=B(w,u-u_{h}){\mbox{ ( from equation (4) ) }}\\&=B(w,u-u_{h})-\underbrace {B(w_{h},u-u_{h})} _{0}\\&=B(w-w_{h},u-u_{h})\\&\leq \|w-w_{h}\|_{H^{1}(0,1)}\|u-u_{h}\|_{H^{1}(0,1)}{\mbox{ (Continuity of Bilinear Form) }}\\&\leq Ch\|w\|_{H^{2}(0,1)}\|u-u_{h}\|_{H^{1}(0,1)}{\mbox{ (Cea Lemma and Polynomial Interpolation Error) }}\end{aligned}}}
Finally, from (#), we have that
‖
w
‖
H
2
(
0
,
1
)
≤
C
‖
u
−
u
h
‖
L
2
(
0
,
1
)
{\displaystyle \|w\|_{H^{2}(0,1)}\leq C\|u-u_{h}\|_{L_{2}(0,1)}\!\,}
. Then,
‖
u
−
u
h
‖
L
2
(
0
,
1
)
2
≤
C
h
‖
u
−
u
h
‖
L
2
(
0
,
1
)
‖
u
−
u
h
‖
H
1
(
0
,
1
)
{\displaystyle \|u-u_{h}\|_{L^{2}(0,1)}^{2}\leq Ch\|u-u_{h}\|_{L_{2}(0,1)}\|u-u_{h}\|_{H^{1}(0,1)}\!\,}
,
or equivalently,
‖
u
−
u
h
‖
L
2
(
0
,
1
)
≤
C
h
‖
u
−
u
h
‖
H
1
(
0
,
1
)
≤
C
h
2
‖
u
‖
H
2
(
0
,
1
)
{\displaystyle \|u-u_{h}\|_{L^{2}(0,1)}\leq Ch\|u-u_{h}\|_{H^{1}(0,1)}\leq Ch^{2}\|u\|_{H^{2}(0,1)}\!\,}
.
Suppose
{
ϕ
1
h
,
…
,
ϕ
N
h
h
}
{\displaystyle \{\phi _{1}^{h},\dots ,\phi _{N_{h}}^{h}\}\!\,}
is a basis for
V
h
where
N
h
=
dim
V
h
, so
u
h
=
∑
j
=
1
N
h
c
j
h
ϕ
j
h
, for appropriate coefficients
c
j
h
.
{\displaystyle V_{h}{\mbox{ where }}N_{h}=\dim V_{h}{\mbox{, so }}u_{h}=\sum _{j=1}^{N_{h}}c_{j}^{h}\phi _{j}^{h}{\mbox{, for appropriate coefficients }}c_{j}^{h}.\!\,}
. Show that
‖
u
−
u
h
‖
1
2
=
‖
u
‖
1
2
−
C
T
A
C
{\displaystyle \|u-u_{h}\|_{1}^{2}=\|u\|_{1}^{2}-C^{T}AC\!\,}
where
C
=
[
c
1
h
,
…
,
c
N
h
h
]
T
and
A
{\displaystyle C=[c_{1}^{h},\dots ,c_{N_{h}}^{h}]^{T}{\mbox{ and }}A\!\,}
is the stiffness matrix.
We know that
‖
u
−
u
h
‖
1
2
=
⟨
u
−
u
h
,
u
−
u
h
⟩
H
1
(
0
,
1
)
=
⟨
u
−
u
h
,
u
⟩
H
1
(
0
,
1
)
−
⟨
u
−
u
h
,
u
h
⟩
H
1
(
0
,
1
)
⏟
0
=
‖
u
‖
H
1
(
0
,
1
)
2
−
⟨
u
h
,
u
⟩
H
1
(
0
,
1
)
=
‖
u
‖
H
1
(
0
,
1
)
2
−
‖
u
h
‖
H
1
(
0
,
1
)
2
{\displaystyle {\begin{aligned}\|u-u_{h}\|_{1}^{2}&=\langle u-u_{h},u-u_{h}\rangle _{H^{1}(0,1)}\\&=\langle u-u_{h},u\rangle _{H^{1}(0,1)}-\underbrace {\langle u-u_{h},u_{h}\rangle _{H^{1}(0,1)}} _{0}\\&=\|u\|_{H^{1}(0,1)}^{2}-\langle u_{h},u\rangle _{H^{1}(0,1)}\\&=\|u\|_{H^{1}(0,1)}^{2}-\|u_{h}\|_{H^{1}(0,1)}^{2}\end{aligned}}}
where the substitution in the last lines come from the orthogonality of error.
Now,
‖
u
h
‖
H
1
(
0
,
1
)
2
=
∑
j
=
1
n
−
1
∑
i
=
1
n
−
1
u
h
,
j
u
h
,
i
(
∫
0
1
ϕ
i
′
ϕ
j
′
d
x
+
∫
0
1
ϕ
i
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{\displaystyle \|u_{h}\|_{H^{1}(0,1)}^{2}=\sum _{j=1}^{n-1}\sum _{i=1}^{n-1}u_{h,j}u_{h,i}\left(\int _{0}^{1}\phi _{i}'\phi _{j}'dx+\int _{0}^{1}\phi _{i}\phi _{j}dx\right)=\sum _{j=1}^{n-1}\sum _{i=1}^{n-1}u_{h,j}A_{ij}u_{h,i}=C^{t}AC.\!\,}
Then, we have obtained
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{\displaystyle \|u-u_{h}\|_{1}^{2}=\|u\|_{H^{1}(0,1)}^{2}-C^{t}AC.\!\,}