HW 1 Solutions - Brown University 1 Solutions 1:1:1: Prove Eqs. (1.1.8) and (1.1.9) for the L2...
Embed Size (px)
Transcript of HW 1 Solutions - Brown University 1 Solutions 1:1:1: Prove Eqs. (1.1.8) and (1.1.9) for the L2...

HW 1 Solutions
1.1.1. Prove Eqs. (1.1.8) and (1.1.9) for the L2 scalar product and norm.
1.1.8 (a) (f, g) = (g, f)
(f, g) =
∫ 2π
0
fgdx =
∫ 2π
0
fgdx = (g, f).
(b) (f + g, h) = (f, h) + (g, h)
(f + g, h) =
∫ 2π
0
(f + g)hdx =
∫ 2π
0
fh + ghdx = (f, h) + (g, h).
(c) (λf, g) = λ(f, g)
(λf, g) =
∫ 2π
0
λfgdx =
∫ 2π
0
λ fgdx = λ
∫ 2π
0
fgdx = λ(f, g).
(d) (f, λg) = λ(f, g)
(f, λg) =
∫ 2π
0
fλgdx = λ
∫ 2π
0
fgdx = λ(f, g).
1.1.9 (a) ||λf || = |λ| ||f ||
(λf, λf) =
∫ 2π
0
λλffdx = |λ|2∫ 2π
0
|f |2dx = |λ|2 ||f ||2.
Therefore,
||λf || = (λf, λf)1/2 = |λ| ||f ||.
(b) |(f, g)| ≤ ||f || ||g||
1

(i) if ||g|| = 0, then O.K.
(ii) if ||g|| 6= 0
Consider
(f + λg, f + λg) = ||f ||2 + λ(f, g)|+ λ(g, f) + |λ|2||g||2 ≥ 0. (1)
Let
λ = −(g, f)
||g||2 , (2)
and substituting λ into equation (1) yields
||f ||2 − (g, f)
||g||2 (f, g)− (g, f)
||g||2 (g, f) +(g, f)(g, f)
||g||2 ≥ 0. (3)
Multiplying equation (3) by ||g||2 gives
||f ||2||g||2 − (g, f)(f, g)− (g, f)(g, f) + (g, f)(g, f) ≥ 0. (4)
Therefore,
||f || ||g|| ≥ |(f, g)|. (5)
(c) ||f + g|| ≤ ||f ||+ ||g||
(f + g, f + g) = ||f ||2 + (f, g) + (g, f) + ||g||2
≤ ||f ||2 + 2||f || ||g||+ ||g||2 = (||f ||+ ||g||)2. (1)
(2)
=⇒ ||f + g|| ≤ ||f ||+ ||g||. (3)
(d)∣∣∣||f || − ||g||
∣∣∣ ≤ ||f − g||
2

||f − g||2 = ||f ||2 − (f, g)− (g, f) + ||g||2 ≥ (||f || − ||g||)2 (1)
=⇒ ||f − g|| ≤∣∣∣||f || − ||g||
∣∣∣. (2)
3

1.2.1. Derive estimates for
∣∣∣∣(
D − ∂3
∂x3
)eiωx
∣∣∣∣ .
1. D = D3+
h3D3+ = (E − E0)3 = E3 − 3E2 + 3E − E0. (1)
Then,
h3D3+eiωx = (e3iωh − 3e2iωh + 3eiωh − 1)eiωx
= (−iω3h3 + O(ω4h4))eiωx. (2)
Therefore,
∣∣∣∣(
D3+ −
∂3
∂x3
)eiωx
∣∣∣∣ = O(ω4h). (3)
2. D = D−D2+
h3D−D2+ = h3E−1D3
+ = E2 − 3E + 3E0 − E−1. (1)
Then,
h3D−D2+eiωx = (e2iωh − 3eiωh + 3− e−iωh)eiωx
= (−iω3h3 + O(ω4h4))eiωx (2)
Therefore,
∣∣∣∣(
D−D2+ −
∂3
∂x3
)eiωx
∣∣∣∣ = O(ω4h). (3)
4

3. D = D2−D+
h3D2−D+ = h3E−2D3
+ = E1 − 3E0 + 3E−1 − E−2. (1)
Then,
h3D2−D+eiωx = (eiωh − 3 + 3e−iωh − e−2iωh)eiωx
= (−iω3h3 + O(ω4h4))eiωx (2)
Therefore,∣∣∣∣(
D2−D+ − ∂3
∂x3
)eiωx
∣∣∣∣ = O(ω4h). (3)
4. D = D3−
h3D3− = h3E−3D3
+ = E0 − 3E−1 + 3E−2 − E−3. (1)
Then,
h3D3−eiωx = (1− 3e−iωh + 3e−2iωh − e−3iωh)eiωx
= (−iω3h3 + O(ω4h4))eiωx (2)
Therefore,∣∣∣∣(
D3− −
∂3
∂x3
)eiωx
∣∣∣∣ = O(ω4h). (3)
5. D = D0D+D−
h3D0D+D− =h3
2(D+ + D−)D+D− =
h3
2(E−1 + E−2)D3
+ (1)
Then,
h3D0D+D−eiωx =1
2(e2iωh − 2eiωh + 2e−iωh − e−2iωh)eiωx
= (−iω3h3 + O(ω5h5))eiωx (2)
5

Therefore,
∣∣∣∣(
D0D+D− − ∂3
∂x3
)eiωx
∣∣∣∣ = O(ω5h2). (3)
6

1.2.3. Compute ||D+D−||h.Clearly,
||D+D−||h ≤ ||D+|| ||D−|| = 4
h2. (1)
Consider uj = (−1)j. Then,
||u||2h = (N + 1)h. (2)
Now,
||D+D−u||2h =1
h3
N∑j=0
((−1)j+1 − 2(−1)j + (−1)j−1
)2=
16
h3(N + 1)
=16
h4||u||2h. (3)
From equations (1, 3),
||D+D−||h = supu6=0
||D+D−u||h||u||h =
4
h2. (4)
7

1.1.3 Write a program for computing v(x)−vN(x). Verify the theoretical
error estimate numerically.
The error of the partial sum can be obtained analytically,
v(x)− vN(x) = R((N + 1/2)x) + O
( |x|+ 1/N
N
), (1)
where
R(y) =π
2−
∫ y
0
sin t
tdt. (2)
Define a new variable
φN(x) = |v(x)− vN(x)−R((N + 1/2)x)|. (3)
Since the second term on the R.H.S of equation (1) behaves as ∼ 1/N for N
large, the new error estimate φN would show the same 1/N behavior.
Figure 2 (a) shows φN(x) for 4 different x. It is shown that when N > 100
φN(x) decreases as ∼ 1/N . To emphasize the behavior, φN normalized by
φ100 is displayed in figure 2 (b). All the curves collapse onto one curve for
N > 100.
8

x/π
v,v N
0 0.5 1 1.5 2-2
-1
0
1
2vvN
(a)
x/πv,
v N
0 0.5 1 1.5 2-2
-1
0
1
2vvN
(b)
Figure 1: v and vN for (a) N = 10 and (b) N = 100
N
φ N
101 102 103 10410-7
10-6
10-5
10-4
10-3
10-2
10-1
100
101(a)
N
φ N
101 102 103 10410-2
10-1
100
101(b)
Figure 2: (a) φN(x) for x = π/100, ◦; π/2, 4; 3π/2, ♦; and 199π/100,¤.
(b) φN(x)/φ100(x).
9