|
|
- Kory McCoy
- 5 years ago
- Views:
Transcription
1
2
3
4
5 l t
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33 1
34
35 2
36 2 2
37 2 2
38
39 B B
40 m t t m 0 ṁ t = e ref a t m t B e ref a t t t x t a t δ a t0 = 0 ȧ t = x t δa t c(x t ) c x t, c (x t ) 0 c (x t ) 0 c(x t ) 0 c (x t ) c
41 c (x t ) x t t a t c (x t ) x t c(x t ) e ref min x t 0 e rt c(x t ) dt m t B ϕ t ṁ t = e ref a t ȧ t = x t δa t a t e ref µ t ν t λ t a t x t a t ν t µ t λ t e ref
42 B T T e ref t T, m t = B = a t = e ref = x t = δe ref T µ t r t < T, µ t = µe rt µ a t < e ref t < T, (r + δ) c (x t ) dc (x t ) dt = µe rt c (x t ) t c i (x i,t ) dt δ
43 (+) >0 <0 + (r + δ)c (x t ) x t t + dt c i (x i,t) + d dt c i (x i,t)dt r
44 t < T, dc (x t ) dt = (r + δ) c (x t ) µe rt µe rt (r + δ) c t (r + δ) c > µe rt dc (x t )/dt > 0 (r + δ) c < µe rt dc (x t )/dt < 0 µ (r + δ) c (δe ref ) µ (r + δ) c (δe ref ) µ = (r + δ)c (x 0 ) x 0
45 a t e ref c (x t ) t < T, c (x t ) = T t µe rθ e (δ+r)(θ t) dθ + e (δ+r)(t t) c (δe ref ) e (δ+r)(θ t) µe rθ T c (δe ref ) t T x, c (x) = C t µe rt = (r + δ)c
46 T T c (x t ) 2 / c (x t ) 2 / c (x t ) x t dt 2
47 l t l t = (r + δ) c (x t ) t $ 2 / 1/δ = 10 5%/ r + δ = 15%/ 2 l t = (r + δ) c (x t ) = µe rt + dc (x t ) dt $/( / ) = $ 2 / $ 2 /= x t l t x t = c 1 ( l t r+δ ).
48 l t µe rt dc (x t) ( dt ) dc (x t) > 0 dt i ā i i āi = e ref δ i c i min e rt (c i (x i,t )) dt x i,t 0 i ȧ i,t = x i,t δ i a i,t ν i,t a i,t ā i ṁ t = i λ i,t (ā i a i,t ) µ t m t B ϕ t ν i,t λ i,t i µ t
49 i, t < T i, (r + δ i ) c i (x i,t ) dc i (x i,t ) dt = µe rt i T i i, t < T i, c i (x i,t ) = Ti t µe rθ e (δ i+r)(θ t) dθ + +e (δ+r)(t i t) c i (δ i ā i ) ( δi = δ j, ā i > ā j y, c i(y) = c j(y) ) = t, c i (x i,t ) > c j (x j,t ) ( δi = δ j, ā i = ā j y, c i(y) > c j(y) ) = t, c i (x i,t ) > c j (x j,t )
50 ā i c i
51 γ i i, a i,t [0, ā i ] γ i (a i,t ) = 1 2 γm i a 2 i,t γ i (a i,t ) = γ m i a γi m 2 ā i 2
52 [ ] [ ] ā i 2 γi m 2 2 δ / i [%/ ] c m 2 i 2 3 i, x i,t 0, c i (x i,t ) = 1 2 cm i x 2 i,t c i (x i,t ) = c m i x i,t c m i (i, j), c m i c m j = γm i γ m j c m i T = 23 r = 4%
53 T 0 (ā i a i,t ) B i B 2 i āi B = δ i 2 2
54 δ i x i,t 2 x i,t x i,t a i,t
55
56 2 2
57 2 2
58 H(x t, a t, m t ) = e (c(x rt t ) + λ t (a t e ref ) + ν t (δa t x t ) ) + µ t (e ref a t ) + ϕ t (m t B) H = 0 x t c (x t ) = ν t H d (e rt ν t ) = 0 a t dt ν t (δ + r)ν t = λ t µ t H m + d (e rt µ t ) = 0 dt µ t rµ t = ϕ t ν t µ λ t (r + δ) c (x t ) dc (x t ) dt = (µ t λ t ) e ref e ref
59 t, (m t B)ϕ t = 0 m t B = µ t = µe rt t, (a t e ref )λ t = 0 T T t T, ṁ t = 0 = a t = e ref = x t = δe ref T a t < e ref x t a t e ref T
60 a t < e ref t a t < e ref, dc (x t ) dt = (r + δ) c (x t ) µe rt θ t > θ a t < e ref (r + δ) c (x θ ) < µe rθ (r + δ) c (x t ) e ref θ (r + δ) c (x θ ) = µe rθ dc (x θ ) = 0 c θ dθ θ c a t t < T a t a t t ȧ t = 0 ä t 0 ȧ t = x t δa t ä t = ẋ t δȧ t = ẋ t ẋ t 0 x t a t τ > t ẋ τ 0 m t B a t t < T, a t < e ref T < + c (x t ) = c (x 0 )e (r+δ)t µert ( e δt 1 ) δ + e ref t T t T, a t = e ref a t T e ref,t T
61 T c t < T, a t < e ref λ t = 0 t T, a t = e ref λ t 0 T t < T, (r + δ) c (x t ) dc (x t ) dt = µe rt t < T, T c (x t ) = e (r+δ)t e (r+δ)θ µe rθ dθ + e (r+δ)t C t C C T T a t x t c (x T ) = c (δe ref ) t < T, T c (x t ) = µe rt e δ(θ t) dθ + e (δ+r)(t t) c (δe ref ) t
62 c (x t ) = T t µe rθ e (δ+r)(θ t) dθ + T (r + δ) c (δe ref ) e (δ+r)(θ t) dθ T c (δe ref ) T c (x t ) E O K t < T, c (x t ) = µe rt e δ(θ t) dθ t }{{} E µe rt e δ(θ t) dθ T }{{} O + e (r+δ)(t t) c (δe ref ) } {{ } K O e ref T O T K c (δe ref ) T (r+δ)c (δe ref ) dc (x t )/dt = 0 T
63 min x t 0 ) e (c(x rt t ) + d(m t ) dt ṁ t = e ref a t µ t ȧ t = x t δa t a t e ref ν t λ t d(m t ) H(x t, a t, m t ) = e (c(x rt t ) + d(m t ) + λ t (a t e ref ) + ν t (δa t x t ) ) + µ t (e ref a t ) H = 0 x t c (x t ) = ν t H d (e rt ν t ) = 0 a t dt ν t (δ + r)ν t = λ t µ t H m + d (e rt µ t ) = 0 dt µ t = rµ t d (m t )
64 B λ t = 0 µ t t T, c (x t ) = T t µ θ e (δ+r)(θ t) dθ + T (r + δ) c (δe ref ) e (δ+r)(θ t) dθ µ t min x t 0 ) e (c(x rt t ) + µ t (e ref a t ) dt ȧ t = x t δa t ν t a t e ref λ t l t h t i 2 / h t δ i A 2 A = θ=t e r(θ t) h e δ i(θ t) dθ = h r + δ
65 h x t (x t + h) C $ C = c(x t + h) c(x t ) = h 0 h c (x t ) l t l t = C A l t = (r + δ) c (x t ) (x t ) (a t ) θ 1 δ dθ θ + dθ ( x t ) (ã t ) θ θ + dθ (x t ) ( x t ) P = 1 dθ [ ] c (1 δ dθ) (x θ ) (1 + r dθ) c (x θ+ dθ ) P dθ 0 (r + δ) c (x θ ) dc (x θ ) dθ P θ
66 (x t ) (a t ) θ e ref T T θ (ã t ) θ (1 δ dθ) θ + dθ ( x t ) H(x i,t, a i,t, m t ) = e rt ( i c i (x i,t ) + λ i,t (a i,t ā i ) + ν i,t (δ i a i,t x t ) i i ) +µ t (ā i a i,t ) + ϕ t (m t B) i
67 (i, t) c i (x i,t ) = ν i,t ( x i,t ) ν i,t (δ i + r)ν i,t = λ i,t µ t ( a i,t ) µ t rµ t = ϕ t ( m t ) ν i,t µ λ i,t T m ṁ t = 0 = i, a i,t = ā i = x i,t = δ i ā i T i i i T i, a i,t = ā i T m = max i (T i ) t < T m, m t < B ϕ t = 0 = µ t = µe rt T i t < T i, a i,t < ā i λ i,t = 0 (i, t), (r + δ i ) c i (x i,t ) dc i (x i,t ) dt = µe rt λ i,t
68 Ti c i (x i,t ) = µe rt e δi(θ t) dθ + e (δ+r)(ti t) c i (δ i ā i ) = Ti t t µe rθ e (δ i+r)(θ t) dθ + T i (r + δ i ) c i (δ i ā i ) e (δ i+r)(θ t) dθ T i i, t < T i, dc i (x i,t ) dt = (r + δ i )c i (x i,t ) µe rt δ i (i, j), δ i = δ j t min(t i, T j ) c i(x i,t ) = c j(x j,t ) = t min(t i, T j ), c i(x i,t ) = c j(x j,t ) {1, 2}
69 (+) (+) 1 ( ) ( ) x > 0, c 1(x) = c 2(x), δ 1 = δ 2 = δ, ā 1 > ā 2 t max(t 1, T 2 ), (r + δ)c 1(δā 1 ) > (r + δ)c 2(δā 2 ) t [T 1, T 2 ] { t < t, c 1(x 1,t ) c 2(x 2,t ) t > t, c 1(x 1,t ) > c 2(x 2,t ) = t < T 1, x 1,t x 2,t = T1 0 x 1,t e δ(t 2 t) dt = ā 1 < T1 0 x 2,t e δ(t 2 t) dt = a 2,T1 a 2,T2 ā 2 ā 1 < ā 2 c 1(x 1,t ) c 2(x 2,t ) t, c 1(x 1,t ) > c 2(x 2,t ) dci (x i,t) dt < 0 T i T 1 > T 2
70 x > 0, c 1(x) < c 2(x) ( ā 1 = ā 2 δ 1 = δ 2 ) ā 2 = a 2,T2 > ā 1 = ā 2 c 1 > c 2 ā 1 < ā 2 δ 1 δ 2 i µe rt i T i τ τ τ c i(x i,τ ) t τ δ i c i(x i,t ) V i (τ) V i (τ) = µτ + e rτ c i(x i,τ ) + τ e rt δ i c i(x i,t )dt
71 V τ ( V i (τ) = µ + e rτ c i(x i,τ ) r d ) dτ c i(x i,τ ) e rτ δ i c i(x i,τ ) = (r + δ i ) c i(x i,τ ) d dτ c i(x i,τ ) µe rτ V i (τ) = λ i,τ V i (τ) τ T i µe rt t a t γ γ a t, γ (a t ) > 0 γ (a t ) > 0 γ(a t ) > 0 γ (a t ) a t γ(a t )
72 t 0 r min a t 0 e rt γ (a t ) dt a t e ref λ t ṁ t = e ref a t m t B µ t ϕ t µe rt e ref T 0 t t 0 γ (a t ) = µe rt t 0 < t < T γ (e ref ) t T t 0
73 γ(a t ) a t µe rt γ ) H(a t, m t ) = e (γ(a rt t ) + λ t (a t e ref ) + µ t (e ref a t ) + ϕ t (m t B) ( a t ) γ (a t ) = (µ t λ t ) ( m t ) µ t rµ t = ϕ t
74 T ṁ t = 0 e ref t < T, a t < e ref m t < B t T, a t = e ref m t = B ϕ t t, ϕ t (m t B) = 0 = t < T, ϕ t = 0 µ t t < T, µ t = µe rt λ t t, λ t (a t e ref ) = 0 = t < T, λ t = 0
75 2
76
77
78
79 h l z t x i,t i k t δ eq
80 k i,t = x i,t δk t, i x i,t 0 k l,t0 = k z,t0 = 0 c i c i (x) > 0, c i(x) > 0, c i (x) > 0, x
81 c l(x) < c z(x), x c i(0) = 0 c i(0) i c i (x i,t ) i k t 0 c i (x i,t ), i c i (x i,t ) k t F i i c (x) c (0) c (x) c (0)
82 F h > F l > F z = 0 B m t B, t m t F i q j,t ṁ t = i F i q j,t S i,t S i,t0 Ṡ i,t = c i (x i,t ) S i,t 0 S z,t0 = i S h,t0 = u ( i c i (x i,t )) u x i,t c i (x i,t )
83 B ( ) max e [u rt c i (x i,t ) ] c i (x i,t ) dt x i,t,c i (x i,t ) 0 i i ki,t = x i,t δk t (ν t ) q j,t k j,t (γ i,t ) q j,t 0 (λ i,t ) x i,t 0 (ξ i,t ) ṁ t = F i q j,t i (µ t ) m t B (η t ) Ṡ i,t = c i (x i,t ) (α i,t ) S i,t 0 (β i,t ) r ν t γ i,t µ t α i,t i α h,t α l,t µ t
84 α h,t = α h e rt, α l,t = α l e rt, µ t = µe rt α h,t α l,t µ t α h = 0 α l,t = 0 α h,t α l,t α h,t α l,t α h,t α l,t γ i,t ν t c i (x i,t ) c i (x i,t ) ν t ξ i,t c i (x i,t ) = ν t + ξ i,t ν t c i(0) ξ i,t = 0 ν t > c i(0) c i (x i,t ) = ν t x i,t > 0 ν t c i(0) ν t γ i,t γ i,t = (δ + r) ν t ν t, t
85 γ i,t ν t γ i,t t ν t dt γ i,t δ t + dt ν t + ν t dt r ν t γ i,t ν t x i,t > 0 γ i,t = (δ + r) c i (x i,t ) d dt c i (x i,t ) r c i (x i,t ) = e (r+δ)t γ i,t dt γ i,t c i (x i,t ) > 0 u t = γ i,t + α i,t + µ t F i u t u t u ( i c i (x i,t )) γ i,t α i,t F i i µ t t
86 γ i,t i c i (x i,t ) < k t = γ i,t = 0 i j t 0 < c i (x i,t ) < k t & 0 < q j,t < k j,t = u t = α i,t + F i µ t = α j,t + F j µ t i α i,t + F i µ t t t t u t 0 < q h,t < k h,t = u t = α h,t + F h µ t
87 0 < q l,t < k l,t = u t = α l,t + F l µ t c i (x i,t ) = k t = γ i,t > 0 u t = γ i,t + α i,t + µ t F i t γ i
88 $/ $/ + = + l + l 0 l + l + l + / l + l 0 l / = + l + l + $/ $/ + l + l = l + l 0 + l / 0 = + ( ) = / z l h α i µf i p q u γ i = p α i µf i
89
90 γ i,t ν t ν t > c i(0) i
91 γ z,t = u t ν z,t = t e (r+δ)(θ t) u θ dθ, t (u θ ) (e (r+δ)(t θ) ) τ z + c z(x z,t ) = t e (r+δ)(θ t) u θ dθ, t τ + z T γ t > T γ = γ l,t = 0 ν l,t = Tγ t e (r+δ)(θ t) (u θ µ θ F l α l,θ )dθ ν l,t > c l (0) τl Tγ τ l e (r+δ)(θ τ l ) (u θ µ θ F i α i,θ )dθ = c l(0)
92 Tγ ν z,t ν l,t = e (r+δ)(θ t) (µ θ F l + α l,θ ) dθ + c z(x z,tγ )e (r+δ)(t Tγ) 0 t } {{ } } {{ } γ ν γ t τl ν T γ T γ ν z,t = c z(x z,tγ ) τ z + τ + l Th Th < τ z + τ + l Th < τ + l τ z + Th c l (0) = 0 τ l = T γ
93 τ + l Th < τ z + τ + l τ z + Th τ z + τ + l Th τ z + Th τ + l ( c i (x i,t ) = C i (1 A) x i,t + 1 ) 2 Ax2 i,t = c i (x i,t ) = C i ((1 A) + Ax i,t ) A (0, 1) C i C z > C l A = 0 A = 1 lim xi,t 0;A=0 c i (x i,t ) = 0 A (0, 1)
94
95 T hl = Th = τ + l T lz = T γ = τ z + τ z + = τ + l = t 0 Th
96 α i,t α h,t ( ) max e [u rt c i (x i,t ) ] (c i (x i,t ) + c i (x i,t )α i,t ) dt x i,t,c i (x i,t ) 0 i i ki,t = x i,t δk t (ν t ) q j,t k j,t (γ i,t ) q j,t 0 (λ i,t ) x i,t 0 (ξ i,t ) ṁ t = F i q j,t i (µ t ) m t B (η t ) T hl T lz T lz T hl
97 Ci m X i H i F i 2
98 r B D 0 G P e 2 δ A 1
99 D t P P = 90 e =.1 D t q(p) = D t e (p P ) p u t(q) ( u(q) = P + D ) t q q2 e 2e D 0 D 0 = G = D t = D 0 + t G k t c i (x i,t ) H i
100 D 0 k z,0 = k l,0 = 0; k h,0 = D 0 H h (X i ) Ci m c i (x i,t ) = C m i X i ( (1 A) x i,t X i + A 2 ( xi,t X i ) 2 ) = c i (X i ) = C m i e ref,t = F h = B = 22 2 B F l = r = 5 % δ δ = 1/
101
102 2 A B 2 A > 0.5 $/ A =.125 4$/
103
104
105 ( ) H = e [u rt c i (x i,t ) c i (x i,t ) ν t (δ k t x i,t ) i i i µ t F i q j,t η t (m t B) (α i,t c i (x i,t ) β i,t S i,t ) i i γ i,t (q j,t k j,t ) + λ i,t q j,t + ] ξ i,t x i,t i i i H = 0 x i c i (x i,t ) = ν t + ξ i,t H = 0 q i λ i,t µ t R i α i,t + u t = γ i,t H = d (e rt ν t ) k i dt (δ + r) ν t ν t = γ i,t H = d (e rt µ t ) m t dt µ t r µ t = η t H = d (e rt α i,t ) S i dt α i r α i,t = β i,t i, t, ξ i,t 0, x i,t 0 ξ i,t x i,t = 0 i, t, λ i,t 0, c i (x i,t ) 0 λ i,t c i (x i,t ) = 0 i, t, η t 0, B m t 0 η t (B m t ) = 0 i, t, β i,t 0, S i,t 0 β i,t S i,t = 0 i, t, γ i,t 0, k t c i (x i,t ) 0 γ i,t (k t c i (x i,t )) = 0
106 η t β i,t α h,t = α h e rt α l,t = α l e rt µ t = µe rt
107 i h l z h l z k t i t c i (x i,t ) i t x i,t i t c i (x i,t ) i t ν t k t µ t 2 α i,t i γ i,t i u t m t 2 δ 1 r 1 F i i 2 D t t B 2 u ( i c i (x i,t ))
108
109 3 12 2
110
111
112 2
113 2 2 2
114 2 / 2 / 2 2 2
115
116 2 2
117 2
118 2 2
119
120
121
122
123
124
125 4
126 Marginal cost $/tco 2 Y N-1 N c i A i 4 X 5 Abatement potential MtCO 2 /yr N i A i c i 1 4
127
128
129 2 2 2
130 N i t t E ref t a i,t i t e t = E ref t N i=1 a i,t M t M t = e t t + M t t i A i 2
131 a i,t A i i c i a i,t i t I i,t I i,t = a i,t c i v i 2 t t t a i,t a i,t t + v i t 2 a i,0 = 0) A i A i = A i a i,0
132 T Ãi A i v i à i = min (v i T, A i ) A i /v i C r C = T t=t 0 N i=1 I i,t (1 + r) t t M t M obj M t M obj
133 c 2 A 2 v 2 2 m e tm E obj m N = 2 v r = 5% = 5 2 E ref t
134 2 M obj 2
135
136 2 2 m {1}, t 1 = 2050 E obj 1
137
138
139 Marginal abatement cost $/tco Marginal abatement cost $/tco GtCO 2 /yr GtCO2 /yr t 1 Ã 2 t 2 Ã 2
140 E obj 1 = E(2020) =
141 E obj 1 = E(2020) = E obj 2 = E(2050) =
142
143
144
145 5
146 Marginal cost $/tco 2 Y 5 c A X Abatement potential at T MtCO 2 /yr
147
148 Emissions GtCO 2 /yr Marginal cost $/tco 2 T Wedge curve time Potential at T MtCO 2 /yr Flipped MAC curve
149
150
151
152
153
154 2 2
155
156 +
157
158 2
159
160 MtCO Emission reductions in Metro Rail and Waterways In the 2030 strategy Bullet train Refineries Heat Integration In the 2020 strategy New processes Marginal cost $/tco Marginal cost $/tco Potential MtCO 2 /yr in Potential MtCO 2 /yr in 2030 D 2020 D 2030 D 2020 D 2030
161
162
163 i A i,t c i v i a T T r a i,t i t min e rt c i a i,t a i,t i,t (i, t), a i,t A i,t v i a i,t+1 a i,t + v i a i,t a T i
164 2 2
165
166
167 6
168
169
170
171
172
173 r t c(x t ) ȧ t = r t a t c(x t ) t c(x t ) u (c(x t )) u > 0 u < 0 W W = ρ 0 e ρt u(c(x t )) dt y t k p k c c(x t ) i p,t i c,t y t = c(x t ) + i p,t + i c,t i p,t i c,t δ k p,t = i p,t δ k p,t k c,t = i c,t δ k c,t
174 i p,t 0 i c,t 0 q t k t y t y t = F (A t, q p,t, q c,t ) q p,t k p,t q c,t k c,t A t q t k t t G q p,t m t ε ṁ t = G q p,t εm t t, q c,t = k c,t
175 ċ c = u (c) c u (c) (r t ρ) u (c) cu (c) > 0 r t ρ R p,t R c,t Π t = F (A t, q p,t, q c,t ) R c,t k c,t R p,t k p,t R c,t R p,t t qb F (q p,t, q c,t ) = R p,t qg F (q p,t, q c,t ) = R c,t R p,t = R c,t = r t + δ
176 m t m m t m max c,i,k 0 e ρt u(c(x t )) dt F (q p, k c ) c(x t ) i p,t i c,t = 0 k p,t = i p,t δk p,t k c,t = i c,t δk c,t ṁ t = G q p,t εm t m t m i p,t 0 q p,t k p,t λ t ν t χ t µ t d(m t ) ψ t β t λ t ν t χ t µ t t
177 u (c t ) = λ t = ν t + ψ t = χ t kc F = 1 λ ((δ + ρ)χ t χ t ) β t = 1 λ ((δ + ρ)ν t ν t ) qp F = β t + τ t G τ τ t = µ t λ t r t t, m t < m = τ t = τ t (r t + ε) m t = m k p,t = m ε/g ṁ t = 0 A t R c,t R p,t R c,t := 1 λ [(δ + ρ)χ t χ t ] R p,t := 1 λ [(δ + ρ)ν t ν t ] χ t ν t (R c,t, R p,t ) (χ t, ν t ) δ ρ χ t ν t
178 kc F = R c,t qp F = R p,t + τ t G β t R p,t r t r t = R c,t δ R p,t = R c,t l t l t l t = 1 λ t ((ρ + δ)ψ t ψ t ) [0, R c,t ] ψ t ν t χ t ψ t R p,t = β t 0 l t = R c,t R p,t R c,t
179 l t τ t τ t }{{} = q p F kc F G } {{ } l t [0, kc F ] + l t G }{{} 0 < l t R c,t R p,t < R c,t i p,t = 0
180 t 0 t 0 t i t (t 0, t i ), i b = 0 q p,t < k p,t k p,t q p,t t ss l t = 0 R p,t = R c,t i p,t > 0 l t kc F (= R c,t ) t 0
181 l t = R c,t R τ t G > kp F (k p,t, k c,t ) = p,t = 0 q p,t < k p,t qp F (q p, k c ) = τ t G R p,t R p,t β t m k b,t0 F u δ ρ τ t G
182 k b,0 m
183 θ c,t > 0 θ p,t > 0 π t t π t = F (q p,t, q c,t ) (λ t θ c,t ) i c,t (λ t + θ p,t ) i p,t λ t θ c,t θ p,t t ss,2 t ss,1 q p,1,t0 < k p,t0 q p,2,t0 = k p,t0 t 0 F (q p,2,t0, k c,t0 ) F (q p,1,t0, k c,t0 )
184 qp F = kc F 1 ((ρ + δ)ψ t λ ) ψ t + 1 ((δ + ρ)(θ c,t + θ p,t ) ( θ } t λ c,t + ) θ p,t ) {{}} t {{} l t τ t,2 G l t τ t,2 τ t,2 }{{} = q p F kc F G } {{ } + l t G }{{} l t τ t,2 t
185 y c y t ss t ss,2 < t ss,1
186 t > t 0, ν t = χ t + θ c,t + θ p,t ψ t ν t χ t (θ c,t + θ p,t ψ t 0)
187
188
189 k b,t0 m m t0 ρ δ
190 δ ε m 0 k 0 m < m 0 + G k 0 δ
191 H h (c t, a t ) = e ρt {u(c t ) + λ t [r t a t + y t c t ]} λ t t W t, c H h = 0 λ t = u (c t ) t, a H h + d(e ρt λ t ) dt = 0 λ t = (ρ r t )λ t λ
192 c t = u (c t ) c t c t u (c t ) (r t ρ) H t = e ρt { u(c(x t )) + λ t [F (q p, k c ) c(x t ) i p,t i c,t ] + ν t [i p,t δk p,t ] +χ t [i c,t δk c,t ] µ t [G q p,t εm t ] + d(m t ) [ m m t ] } + ψ t i p,t + β t [k p,t q p,t ] t ψ t 0 ψ t i p,t = 0 t β t 0 β t (k p,t q p,t ) = 0 t d(m t ) 0 d(m t ) ( m m t ) = 0 H t = 0 c t u (c t ) = λ t H t = 0 i p,t λ t = ν t + ψ t H t = 0 i c,t λ t = χ t H t = d(e ρt ν t ) k p,t dt ν t δ + β t = ν t + ρν t H t = d(e ρt χ t ) k c,t dt λ t kc F (k p,t, k c,t ) χ t δ = χ t + ρχ t
193 H t = 0 q p,t λ t qp F (q p,t, k c,t ) µ t G = β t H t = d(e ρt µ t ) m t dt d(m t ) + εµ t = µ t ρµ t λ t λ t c t u (c t ) u (c t ) c t = (ρ + δ R c,t ) c t r t r t := R c,t δ µ t µ t = ( λ t τ t + λ t τ t ) τ t = τ t [ε + r t ] d(m t) λ t t ss t t ss, m t = m ṁ t = 0 = G q p,t = ε m k p,t = m ε/g t ss d(m t ) = 0 τ t = τ t [ε + r t ]
194 r t + ε t 0 lim t t 0 lim t t 0 q p,t = k p,t qp F (q p,t, q c,t ) = kc F (q p,t, q c,t ) t + 0 ψ t = 0 lim qp F (q p,t, q c,t ) = kc F (q p,t, q c,t ) + τ t0 G t t + 0 lim q p,t = k p,t t t + 0 τ t0 > 0 lim qp F (q p,t, q c,t ) lim qp F (q p,t, q c,t ) t t + 0 t t + 0 qp F q p,t lim t t + q p,t 0 lim t t + q p,t 0
195 t Π t = F (q p,t, k c,t ) R c,t k c,t R p,t k p,t τ t G q p,t R p,t R c,t τ t ȧ t = r t a t + y t c(x t ) + τ t G q p,t L(t) = Π t + β t (k p,t q p,t ) + γ t (k c,t q c,t ) qg L = 0 qc F (q p,t, q c,t ) = γ t qb L = 0 qp F (q p,t, q c,t ) = β t + τ t G kg L = 0 γ t = R c,t kb L = 0 β t = R p,t t γ t 0 γ t (k c,t q c,t ) = 0 β t 0 β t (k p,t q p,t ) = 0 γ t = qc F (q p,t, q c,t ) > 0 q c,t = k c,t t kc F (q p,t, k c,t ) = R c,t qp F (q p,t, k c,t ) = R p,t + τ t G
196 R c,t = r t + δ R p,t = R c,t = r t + δ t 0 H t = e ρt {F (q p,t, q c,t ) (λ t θ c,t ) i c,t (λ t + θ p,t ) i p,t +ν t [i p,t δk p,t ] + χ t [i c,t δk c,t ] + ψ t i p,t + β t [k p,t q p,t ]} H t = 0 i p,t λ t + θ p,t = ν t + ψ t H t = 0 i c,t λ t θ c,t = χ t H t = d(e ρt ν t ) k p,t dt ν t δ + β t = ν t + ρν t H t = d(e ρt χ t ) k c,t dt ρχ t χ t = λ t kc F (k p,t, k c,t ) χ t δ H t = 0 q p,t λ t qp F (q p,t, k c,t ) = β t t, β t [k p,t q p,t ] = 0
197 F t, k p,t = q p,t ν t + ψ t = χ t + θ c,t + θ p,t kc F = 1 λ ((δ + ρ)χ t χ t ) qp F = 1 λ ((δ + ρ)ν t ν t ) qp F = kc F + 1 ((δ + ρ)(θ c,t + θ p,t ) ( θ λ c,t + ) θ p,t ) } t {{} θ t 1 ((ρ + δ)ψ t λ ) ψ t } t {{} l t l t θ t (θ c,t + θ p,t ) θ t = τ t,2 G τ t,2 θ t G θ c,t + θ p,t ψ t kc F qp F l t θ t (= τ t,2 ) ψ t θ c,t + θ p,t
198 l t = 0 θ t θ t t t ss, θ t = τ t G t ss τ t,2 τ t,2 }{{} = q p F kc F G } {{ } l t [0, τ t,2 ] + l t G }{{} l t τ t,2 i p,t σ t
199 t, i p,t = sd t σ t sd t sd t = 0 H t = e ρt {u(c(x t )) + λ t [F (q p, k c ) c(x t ) i p,t i c,t ] + ν t [i p,t δk p,t ] +χ t [i c,t δk c,t ] + σ t (sd t i p,t ) + β t [k p,t q p,t ]} λ t ν t χ t u (c t ) = λ t = ν t σ t = χ t λ t kc F = (δ + ρ)χ t χ t λ t qp F = β t β t = (δ + ρ)ν t ν t σ t (θ c,t + θ p,t ψ t ) R p,t = R c,t + n t n t = 1 ((ρ + δ)σ t σ t ) λ t n t = θ t l t n t
200 max c,i,k 0 e ρt u(c(x t )) dt F (q p, k c ) c(x t ) i p,t i c,t = 0 k p,t = i p,t δk p,t k c,t = i c,t δk c,t ṁ t = G q p,t εm t m t m i p,t 0 q p,t k p,t q p,t = k p,t λ t ν t χ t µ t d(m t ) ψ t β t α t H t = e ρt {u(c(x t )) + λ t [F (q p, k c ) c(x t ) i p,t i c,t ] + ν t [i p,t δk p,t ] +χ t [i c,t δk c,t ] µ t [G q p,t εm t ] + d(m t ) [ m m t ] + ψ t i p,t + β t [k p,t q p,t ] + α t [q p,t k p,t ]}
201 β t α t = 1 λ ((δ + ρ)ν t ν t ) qp F = β t α t + τ t G β t α t qp F = β t α t + τ t G β t > 0 α t = 0 α t > 0 β t = 0 β t = 0 qp F = α t + τ t G α t qp F = kc F l t + τ t G 0 < l t < τ t G l t > 0 l t = 0 i b > 0 i b = 0 ψ t > 0 R p,t < R c,t R p,t
202 k p,t = k 0 e δt ṁ = k 0 e δt ε m m t = G k 0 δ ε e δt + ( m 0 + G k 0 δ ε ) e εt m max = G k 0 δ t max = 1 δ ln(m max ε G k 0 ) e δt m m max = m m = G k ( m ε 0 δ ε eln( G k ) 0 + m 0 + G k ) 0 e ε m ε ln( ) δ G k 0 δ ε m = [ ( m 0 + G k ) ( ) ε 0 ε δ ε G k 0 δ ( δ ε δ ) ] δ δ ϵ
203
204
205
206
207
208
209
210
211 2
212
213
214
215 2
216
217
218
219
220
221
222
223
224
225 2
226
227
228
Dynamic Asset Allocation - Identifying Regime Shifts in Financial Time Series to Build Robust Portfolios
Downloaded from orbit.dtu.dk on: Jan 22, 2019 Dynamic Asset Allocation - Identifying Regime Shifts in Financial Time Series to Build Robust Portfolios Nystrup, Peter Publication date: 2018 Document Version
More informationR k. t + 1. n E t+1 = ( 1 χ E) W E t+1. c E t+1 = χ E Wt+1 E. Γ E t+1. ) R E t+1q t K t. W E t+1 = ( 1 Γ E t+1. Π t+1 = P t+1 /P t
R k E 1 χ E Wt E n E t+1 t t + 1 n E t+1 = ( 1 χ E) W E t+1 c E t+1 = χ E Wt+1 E t + 1 q t K t Rt+1 E 1 Γ E t+1 Π t+1 = P t+1 /P t W E t+1 = ( 1 Γ E t+1 ) R E t+1q t K t Π t+1 Γ E t+1 K t q t q t K t j
More informationEconomic Growth (Continued) The Ramsey-Cass-Koopmans Model. 1 Literature. Ramsey (1928) Cass (1965) and Koopmans (1965) 2 Households (Preferences)
III C Economic Growth (Continued) The Ramsey-Cass-Koopmans Model 1 Literature Ramsey (1928) Cass (1965) and Koopmans (1965) 2 Households (Preferences) Population growth: L(0) = 1, L(t) = e nt (n > 0 is
More information5 n N := {1, 2,...} N 0 := {0} N R ++ := (0, ) R + := [0, ) a, b R a b := max{a, b} f g (f g)(x) := f(x) g(x) (Z, Z ) bz Z Z R f := sup z Z f(z) κ: Z R ++ κ f : Z R f(z) f κ := sup z Z κ(z). f κ < f κ
More informationNonlinear Systems and Control Lecture # 19 Perturbed Systems & Input-to-State Stability
p. 1/1 Nonlinear Systems and Control Lecture # 19 Perturbed Systems & Input-to-State Stability p. 2/1 Perturbed Systems: Nonvanishing Perturbation Nominal System: Perturbed System: ẋ = f(x), f(0) = 0 ẋ
More informationForchheimer Equations in Porous Media - Part III
Forchheimer Equations in Porous Media - Part III Luan Hoang, Akif Ibragimov Department of Mathematics and Statistics, Texas Tech niversity http://www.math.umn.edu/ lhoang/ luan.hoang@ttu.edu Applied Mathematics
More information( U t = jt c ln c t + j t ln h t ϕ ) 1 + η (n t + s t ) 1+η c t h t j c t j t n t s t c t c h t c f t c t = [ ( ) v 1 θ 1 ( θ c h θ t + (1 v) 1 θ c f t ) θ 1 ] θ θ 1 θ. θ v P t p h t P t h P t c t + p
More informationTime-Dependent Conduction :
Time-Dependent Conduction : The Lumped Capacitance Method Chapter Five Sections 5.1 thru 5.3 Transient Conduction A heat transfer process for which the temperature varies with time, as well as location
More informationHigh-Gain Observers in Nonlinear Feedback Control. Lecture # 3 Regulation
High-Gain Observers in Nonlinear Feedback Control Lecture # 3 Regulation High-Gain ObserversinNonlinear Feedback ControlLecture # 3Regulation p. 1/5 Internal Model Principle d r Servo- Stabilizing u y
More informationE E I M (E, I) E I 2 E M I I X I Y X Y I X, Y I X > Y x X \ Y Y {x} I B E B M E C E C C M r E X E r (X) X X r (X) = X E B M X E Y E X Y X B E F E F F E E E M M M M M M E B M E \ B M M 0 M M M 0 0 M x M
More informationEDP with strong anisotropy : transport, heat, waves equations
EDP with strong anisotropy : transport, heat, waves equations Mihaï BOSTAN University of Aix-Marseille, FRANCE mihai.bostan@univ-amu.fr Nachos team INRIA Sophia Antipolis, 3/07/2017 Main goals Effective
More informationTechnische Universität Dresden
Als Manuskript gedruckt Technische Universität Dresden Herausgeber: Der Rektor Modulus semigroups and perturbation classes for linear delay equations in L p H. Vogt, J. Voigt Institut für Analysis MATH-AN-7-2
More informationand in each case give the range of values of x for which the expansion is valid.
α β γ δ ε ζ η θ ι κ λ µ ν ξ ο π ρ σ τ υ ϕ χ ψ ω Mathematics is indeed dangerous in that it absorbs students to such a degree that it dulls their senses to everything else P Kraft Further Maths A (MFPD)
More informationMA5206 Homework 4. Group 4. April 26, ϕ 1 = 1, ϕ n (x) = 1 n 2 ϕ 1(n 2 x). = 1 and h n C 0. For any ξ ( 1 n, 2 n 2 ), n 3, h n (t) ξ t dt
MA526 Homework 4 Group 4 April 26, 26 Qn 6.2 Show that H is not bounded as a map: L L. Deduce from this that H is not bounded as a map L L. Let {ϕ n } be an approximation of the identity s.t. ϕ C, sptϕ
More informationEXTREMAL ANALYTICAL CONTROL AND GUIDANCE SOLUTIONS FOR POWERED DESCENT AND PRECISION LANDING. Dilmurat Azimov
EXTREMAL ANALYTICAL CONTROL AND GUIDANCE SOLUTIONS FOR POWERED DESCENT AND PRECISION LANDING Dilmurat Azimov University of Hawaii at Manoa 254 Dole Street, Holmes 22A Phone: (88)-956-2863, E-mail: azimov@hawaii.edu
More informationHousing Market Monitor
M O O D Y È S A N A L Y T I C S H o u s i n g M a r k e t M o n i t o r I N C O R P O R A T I N G D A T A A S O F N O V E M B E R İ Ī Ĭ Ĭ E x e c u t i v e S u m m a r y E x e c u t i v e S u m m a r y
More informationEvent-triggered stabilization of linear systems under channel blackouts
Event-triggered stabilization of linear systems under channel blackouts Pavankumar Tallapragada, Massimo Franceschetti & Jorge Cortés Allerton Conference, 30 Sept. 2015 Acknowledgements: National Science
More informationLienard-Wiechert for constant velocity
Problem 1. Lienard-Wiechert for constant velocity (a) For a particle moving with constant velocity v along the x axis show using Lorentz transformation that gauge potential from a point particle is A x
More informationLinear-quadratic control problem with a linear term on semiinfinite interval: theory and applications
Linear-quadratic control problem with a linear term on semiinfinite interval: theory and applications L. Faybusovich T. Mouktonglang Department of Mathematics, University of Notre Dame, Notre Dame, IN
More informationNOTES ON CALCULUS OF VARIATIONS. September 13, 2012
NOTES ON CALCULUS OF VARIATIONS JON JOHNSEN September 13, 212 1. The basic problem In Calculus of Variations one is given a fixed C 2 -function F (t, x, u), where F is defined for t [, t 1 ] and x, u R,
More informationHigh-Gain Observers in Nonlinear Feedback Control. Lecture # 2 Separation Principle
High-Gain Observers in Nonlinear Feedback Control Lecture # 2 Separation Principle High-Gain ObserversinNonlinear Feedback ControlLecture # 2Separation Principle p. 1/4 The Class of Systems ẋ = Ax + Bφ(x,
More informationDeterministic Dynamic Programming
Deterministic Dynamic Programming 1 Value Function Consider the following optimal control problem in Mayer s form: V (t 0, x 0 ) = inf u U J(t 1, x(t 1 )) (1) subject to ẋ(t) = f(t, x(t), u(t)), x(t 0
More informationMath 21B - Homework Set 8
Math B - Homework Set 8 Section 8.:. t cos t dt Let u t, du t dt and v sin t, dv cos t dt Let u t, du dt and v cos t, dv sin t dt t cos t dt u v v du t sin t t sin t dt [ t sin t u v ] v du [ ] t sin t
More informationThe semi-geostrophic equations - a model for large-scale atmospheric flows
The semi-geostrophic equations - a model for large-scale atmospheric flows Beatrice Pelloni, University of Reading with M. Cullen (Met Office), D. Gilbert, T. Kuna INI - MFE Dec 2013 Introduction - Motivation
More informationDynamic and Stochastic Brenier Transport via Hopf-Lax formulae on Was
Dynamic and Stochastic Brenier Transport via Hopf-Lax formulae on Wasserstein Space With many discussions with Yann Brenier and Wilfrid Gangbo Brenierfest, IHP, January 9-13, 2017 ain points of the
More informationConvolution and Linear Systems
CS 450: Introduction to Digital Signal and Image Processing Bryan Morse BYU Computer Science Introduction Analyzing Systems Goal: analyze a device that turns one signal into another. Notation: f (t) g(t)
More informationLie algebraic aspects of quantum control in interacting spin-1/2 (qubit) chains
.. Lie algebraic aspects of quantum control in interacting spin-1/2 (qubit) chains Vladimir M. Stojanović Condensed Matter Theory Group HARVARD UNIVERSITY September 16, 2014 V. M. Stojanović (Harvard)
More informationI. Introduction. A New Method for Inverting Integrals Attenuated Radon Transform (SPECT) D to N map for Moving Boundary Value Problems
I. Introduction A New Method for Inverting Integrals Attenuated Radon Transform (SPECT) D to N map for Moving Boundary Value Problems F(k) = T 0 e k2 t+ikl(t) f (t)dt, k C. Integrable Nonlinear PDEs in
More informationA t = B A F (φ A t K t, N A t X t ) S t = B S F (φ S t K t, N S t X t ) M t + δk + K = B M F (φ M t K t, N M t X t )
Notes on Kongsamut et al. (2001) The goal of this model is to be consistent with the Kaldor facts (constancy of growth rates, capital shares, capital-output ratios) and the Kuznets facts (employment in
More informationJoint work with Nguyen Hoang (Univ. Concepción, Chile) Padova, Italy, May 2018
EXTENDED EULER-LAGRANGE AND HAMILTONIAN CONDITIONS IN OPTIMAL CONTROL OF SWEEPING PROCESSES WITH CONTROLLED MOVING SETS BORIS MORDUKHOVICH Wayne State University Talk given at the conference Optimization,
More informationI. Relationship with previous work
x x i t j J t = {0, 1,...J t } j t (p jt, x jt, ξ jt ) p jt R + x jt R k k ξ jt R ξ t T j = 0 t (z i, ζ i, G i ), ζ i z i R m G i G i (p j, x j ) i j U(z i, ζ i, x j, p j, ξ j ; G i ) = u(ζ i, x j,
More informationAero III/IV Conformal Mapping
Aero III/IV Conformal Mapping View complex function as a mapping Unlike a real function, a complex function w = f(z) cannot be represented by a curve. Instead it is useful to view it as a mapping. Write
More informationModelling and Mathematical Methods in Process and Chemical Engineering
Modelling and Mathematical Methods in Process and Chemical Engineering Solution Series 3 1. Population dynamics: Gendercide The system admits two steady states The Jacobi matrix is ẋ = (1 p)xy k 1 x ẏ
More informationObserver design for a general class of triangular systems
1st International Symposium on Mathematical Theory of Networks and Systems July 7-11, 014. Observer design for a general class of triangular systems Dimitris Boskos 1 John Tsinias Abstract The paper deals
More informationOPTIMAL CONTROL SYSTEMS
SYSTEMS MIN-MAX Harry G. Kwatny Department of Mechanical Engineering & Mechanics Drexel University OUTLINE MIN-MAX CONTROL Problem Definition HJB Equation Example GAME THEORY Differential Games Isaacs
More informationPhase Synchronization
Phase Synchronization Lecture by: Zhibin Guo Notes by: Xiang Fan May 10, 2016 1 Introduction For any mode or fluctuation, we always have where S(x, t) is phase. If a mode amplitude satisfies ϕ k = ϕ k
More informationDeterministic Kalman Filtering on Semi-infinite Interval
Deterministic Kalman Filtering on Semi-infinite Interval L. Faybusovich and T. Mouktonglang Abstract We relate a deterministic Kalman filter on semi-infinite interval to linear-quadratic tracking control
More information06/12/ rws/jMc- modif SuFY10 (MPF) - Textbook Section IX 1
IV. Continuous-Time Signals & LTI Systems [p. 3] Analog signal definition [p. 4] Periodic signal [p. 5] One-sided signal [p. 6] Finite length signal [p. 7] Impulse function [p. 9] Sampling property [p.11]
More informationIntroduction to tensor calculus
Introduction to tensor calculus Dr. J. Alexandre King s College These lecture notes give an introduction to the formalism used in Special and General Relativity, at an undergraduate level. Contents 1 Tensor
More informationEEE 184: Introduction to feedback systems
EEE 84: Introduction to feedback systems Summary 6 8 8 x 7 7 6 Level() 6 5 4 4 5 5 time(s) 4 6 8 Time (seconds) Fig.. Illustration of BIBO stability: stable system (the input is a unit step) Fig.. step)
More informationWhat can be expressed via Conic Quadratic and Semidefinite Programming?
What can be expressed via Conic Quadratic and Semidefinite Programming? A. Nemirovski Faculty of Industrial Engineering and Management Technion Israel Institute of Technology Abstract Tremendous recent
More informationWEYL S LEMMA, ONE OF MANY. Daniel W. Stroock
WEYL S LEMMA, ONE OF MANY Daniel W Stroock Abstract This note is a brief, and somewhat biased, account of the evolution of what people working in PDE s call Weyl s Lemma about the regularity of solutions
More informationStochastic optimal control theory
Stochastic optimal control theory Bert Kappen SNN Radboud University Nijmegen the Netherlands July 5, 2008 Bert Kappen Introduction Optimal control theory: Optimize sum of a path cost and end cost. Result
More informationRoots of Polynomials in Subgroups of F p and Applications to Congruences
Roots of Polynomials in Subgroups of F p and Applications to Congruences Enrico Bombieri, Jean Bourgain, Sergei Konyagin IAS, Princeton, IAS Princeton, Moscow State University The decimation problem Let
More informationLesson Rigid Body Dynamics
Lesson 8 Rigid Body Dynamics Lesson 8 Outline Problem definition and motivations Dynamics of rigid bodies The equation of unconstrained motion (ODE) User and time control Demos / tools / libs Rigid Body
More informationOn the consistent discretization in time of nonlinear thermo-elastodynamics
of nonlinear thermo-elastodynamics Chair of Computational Mechanics University of Siegen GAMM Annual Meeting, 3.03.00 0000 0000 Thermodynamic double pendulum Structure preserving integrators Great interest
More informationTrajectory Smoothing as a Linear Optimal Control Problem
Trajectory Smoothing as a Linear Optimal Control Problem Biswadip Dey & P. S. Krishnaprasad Allerton Conference - 212 Monticello, IL, USA. October 4, 212 Background and Motivation 1 Background and Motivation
More informationMikyoung LIM(KAIST) Optimization algorithm for the reconstruction of a conductivity in
Optimization algorithm for the reconstruction of a conductivity inclusion Mikyoung LIM (KAIST) Inclusion Ω in R d For a given entire harmonic function H, consider { (χ(r d \ Ω) + kχ(ω)) u = in R d, u(x)
More information3.3 Unsteady State Heat Conduction
3.3 Unsteady State Heat Conduction For many applications, it is necessary to consider the variation of temperature with time. In this case, the energy equation for classical heat conduction, eq. (3.8),
More information= m(0) + 4e 2 ( 3e 2 ) 2e 2, 1 (2k + k 2 ) dt. m(0) = u + R 1 B T P x 2 R dt. u + R 1 B T P y 2 R dt +
ECE 553, Spring 8 Posted: May nd, 8 Problem Set #7 Solution Solutions: 1. The optimal controller is still the one given in the solution to the Problem 6 in Homework #5: u (x, t) = p(t)x k(t), t. The minimum
More informationReconstruction Scheme for Active Thermography
Reconstruction Scheme for Active Thermography Gen Nakamura gnaka@math.sci.hokudai.ac.jp Department of Mathematics, Hokkaido University, Japan Newton Institute, Cambridge, Sept. 20, 2011 Contents.1.. Important
More informationDynamical Domain Wall and Localization
Dynamical Domain Wall and Localization Shin ichi Nojiri Department of Physics & Kobayashi-Maskawa Institute for the Origin of Particles and the Universe (KMI), Nagoya Univ. Typeset by FoilTEX 1 Based on
More informationApproximation around the risky steady state
Approximation around the risky steady state Centre for International Macroeconomic Studies Conference University of Surrey Michel Juillard, Bank of France September 14, 2012 The views expressed herein
More informationSTAT 512 sp 2018 Summary Sheet
STAT 5 sp 08 Summary Sheet Karl B. Gregory Spring 08. Transformations of a random variable Let X be a rv with support X and let g be a function mapping X to Y with inverse mapping g (A = {x X : g(x A}
More informationSolution: It could be discontinuous, or have a vertical tangent like y = x 1/3, or have a corner like y = x.
1. Name three different reasons that a function can fail to be differentiable at a point. Give an example for each reason, and explain why your examples are valid. It could be discontinuous, or have a
More informationSimplified Numerical Model for the Study of Wave Energy Convertes (WECs)
Simplified Numerical Model for the Study of Wave Energy Convertes (WECs) J. A. Armesto, R. Guanche, A. Iturrioz and A. D. de Andres Ocean Energy and Engineering Group IH Cantabria, Universidad de Cantabria
More informationCritical Region of the QCD Phase Transition
Critical Region of the QCD Phase Transition Mean field vs. Renormalization group B.-J. Schaefer 1 and J. Wambach 1,2 1 Institut für Kernphysik TU Darmstadt 2 GSI Darmstadt 18th August 25 Uni. Graz B.-J.
More informationPolynomial Jacobi Davidson Method for Large/Sparse Eigenvalue Problems
Polynomial Jacobi Davidson Method for Large/Sparse Eigenvalue Problems Tsung-Ming Huang Department of Mathematics National Taiwan Normal University, Taiwan April 28, 2011 T.M. Huang (Taiwan Normal Univ.)
More informationEnergy-consistent time-integration for dynamic finite deformation. thermoviscoelasticity. GAMM Annual Meeting,
for dynamic finite thermo-viscoelasticity Chair of Computational Mechanics University of Siegen GAMM Annual Meeting, 9.04.0 Thermo-viscoelastic double pendulum (closed system) Thermodynamically Consistent
More informationExercise 5: Exact Solutions to the Navier-Stokes Equations I
Fluid Mechanics, SG4, HT009 September 5, 009 Exercise 5: Exact Solutions to the Navier-Stokes Equations I Example : Plane Couette Flow Consider the flow of a viscous Newtonian fluid between two parallel
More informationÜbungen zu RT2 SS (4) Show that (any) contraction of a (p, q) - tensor results in a (p 1, q 1) - tensor.
Übungen zu RT2 SS 2010 (1) Show that the tensor field g µν (x) = η µν is invariant under Poincaré transformations, i.e. x µ x µ = L µ νx ν + c µ, where L µ ν is a constant matrix subject to L µ ρl ν ση
More informationDouble resonance with Landesman-Lazer conditions for planar systems of ordinary differential equations
Double resonance with Lesman-Lazer conditions for planar systems of ordinary differential equations Alessro Fonda Maurizio Garrione Dedicated to Alan Lazer Abstract We prove the existence of periodic solutions
More informationEXISTENCE AND UNIQUENESS FOR A THREE DIMENSIONAL MODEL OF FERROMAGNETISM
1 EXISTENCE AND UNIQUENESS FOR A THREE DIMENSIONAL MODEL OF FERROMAGNETISM V. BERTI and M. FABRIZIO Dipartimento di Matematica, Università degli Studi di Bologna, P.zza di Porta S. Donato 5, I-4126, Bologna,
More informationEE2007 Tutorial 7 Complex Numbers, Complex Functions, Limits and Continuity
EE27 Tutorial 7 omplex Numbers, omplex Functions, Limits and ontinuity Exercise 1. These are elementary exercises designed as a self-test for you to determine if you if have the necessary pre-requisite
More informationLINEAR RESPONSE THEORY
MIT Department of Chemistry 5.74, Spring 5: Introductory Quantum Mechanics II Instructor: Professor Andrei Tokmakoff p. 8 LINEAR RESPONSE THEORY We have statistically described the time-dependent behavior
More informationu =0with u(0,x)=f(x), (x) =
PDE LECTURE NOTES, MATH 37A-B 69. Heat Equation The heat equation for a function u : R + R n C is the partial differential equation (.) µ t u =0with u(0,x)=f(x), where f is a given function on R n. By
More informationDimensional Analysis - Concepts
Dimensional Analysis - Concepts Physical quantities: R j = v(r j )[R j ] = value unit, j = 1,..., m. Units: Dimension matrix of R 1,, R m : A = Change of units change of values: [R j ] = F a 1j 1 F a nj
More informationComplex Inversion Formula for Stieltjes and Widder Transforms with Applications
Int. J. Contemp. Math. Sciences, Vol. 3, 8, no. 16, 761-77 Complex Inversion Formula for Stieltjes and Widder Transforms with Applications A. Aghili and A. Ansari Department of Mathematics, Faculty of
More informationCointegration and the Ramsey Model
RamseyCointegration, March 1, 2004 Cointegration and the Ramsey Model This handout examines implications of the Ramsey model for cointegration between consumption, income, and capital. Consider the following
More informationTowards a Search for Stochastic Gravitational-Wave Backgrounds from Ultra-light Bosons
Towards a Search for Stochastic Gravitational-Wave Backgrounds from Ultra-light Bosons Leo Tsukada RESCEU, Univ. of Tokyo The first annual symposium "Gravitational Wave Physics and Astronomy: Genesis"
More informationFigure 1: Surface waves
4 Surface Waves on Liquids 1 4 Surface Waves on Liquids 4.1 Introduction We consider waves on the surface of liquids, e.g. waves on the sea or a lake or a river. These can be generated by the wind, by
More informationK(ζ) = 4ζ 2 x = 20 Θ = {θ i } Θ i=1 M = {m i} M i=1 A = {a i } A i=1 M A π = (π i ) n i=1 (Θ) n := Θ Θ (a, θ) u(a, θ) E γq [ E π m [u(a, θ)] ] C(π, Q) Q γ Q π m m Q m π m a Π = (π) U M A Θ
More informationZ i Q ij Z j. J(x, φ; U) = X T φ(t ) 2 h + where h k k, H(t) k k and R(t) r r are nonnegative definite matrices (R(t) is uniformly in t nonsingular).
2. LINEAR QUADRATIC DETERMINISTIC PROBLEM Notations: For a vector Z, Z = Z, Z is the Euclidean norm here Z, Z = i Z2 i is the inner product; For a vector Z and nonnegative definite matrix Q, Z Q = Z, QZ
More informationBrownian Motion: Fokker-Planck Equation
Chapter 7 Brownian Motion: Fokker-Planck Equation The Fokker-Planck equation is the equation governing the time evolution of the probability density of the Brownian particla. It is a second order differential
More informationz 0 > 0 z = 0 h; (x, 0)
n = (q 1,..., q n ) T (,, t) V (,, t) L(,, t) = T V d dt ( ) L q i L q i = 0, i = 1,..., n. l l 0 l l 0 l > l 0 {x} + = max(0, x) x = k{l l 0 } +ˆ, k > 0 ˆ (x, z) x z (0, z 0 ) (0, z 0 ) z 0 > 0 x z =
More informationReview of Fundamental Equations Supplementary notes on Section 1.2 and 1.3
Review of Fundamental Equations Supplementary notes on Section. and.3 Introduction of the velocity potential: irrotational motion: ω = u = identity in the vector analysis: ϕ u = ϕ Basic conservation principles:
More informationINDIAN INSTITUTE OF SCIENCE STOCHASTIC HYDROLOGY. Lecture -33 Course Instructor : Prof. P. P. MUJUMDAR Department of Civil Engg., IISc.
INDIAN INSTITUTE OF SCIENCE STOCHASTIC HYDROLOGY Lecture -33 Course Instructor : Prof. P. P. MUJUMDAR Department of Civil Engg., IISc. Summary of the previous lecture Regression on Principal components
More information3 Rigid Spacecraft Attitude Control
1 3 Rigid Spacecraft Attitude Control Consider a rigid spacecraft with body-fixed frame F b with origin O at the mass centre. Let ω denote the angular velocity of F b with respect to an inertial frame
More informationContrôle de la dynamique d un bateau
Contrôle de la dynamique d un bateau Lionel Rosier Université Henri Poincaré Nancy 1 Un peu de contrôle à Clermont-Ferrand... 27-29 Juin 2011 Joint work with Olivier Glass, Université Paris-Dauphine Control
More informationTHE ANALYSIS OF AN ECONOMIC GROWTH MODEL WITH TAX EVASION AND DELAY
Key words: delayed differential equations, economic growth, tax evasion In this paper we formulate an economic model with tax evasion, corruption and taxes In the first part the static model is considered,
More informationSuggested solutions, FYS 500 Classical Mechanics and Field Theory 2015 fall
UNIVERSITETET I STAVANGER Institutt for matematikk og naturvitenskap Suggested solutions, FYS 500 Classical Mecanics and Field Teory 015 fall Set 1 for 16/17. November 015 Problem 68: Te Lagrangian for
More informationLecturer: Bengt E W Nilsson
2009 05 07 Lecturer: Bengt E W Nilsson From the previous lecture: Example 3 Figure 1. Some x µ s will have ND or DN boundary condition half integer mode expansions! Recall also: Half integer mode expansions
More information2 Statement of the problem and assumptions
Mathematical Notes, 25, vol. 78, no. 4, pp. 466 48. Existence Theorem for Optimal Control Problems on an Infinite Time Interval A.V. Dmitruk and N.V. Kuz kina We consider an optimal control problem on
More informationFree Convolution with A Free Multiplicative Analogue of The Normal Distribution
Free Convolution with A Free Multiplicative Analogue of The Normal Distribution Ping Zhong Indiana University Bloomington July 23, 2013 Fields Institute, Toronto Ping Zhong free multiplicative analogue
More information10 The Finite Element Method for a Parabolic Problem
1 The Finite Element Method for a Parabolic Problem In this chapter we consider the approximation of solutions of the model heat equation in two space dimensions by means of Galerkin s method, using piecewise
More informationCERN Accelerator School. Intermediate Accelerator Physics Course Chios, Greece, September Low Emittance Rings
CERN Accelerator School Intermediate Accelerator Physics Course Chios, Greece, September 2011 Low Emittance Rings Part 1: Beam Dynamics with Synchrotron Radiation Andy Wolski The Cockcroft Institute, and
More informationLow Emittance Machines
CERN Accelerator School Advanced Accelerator Physics Course Trondheim, Norway, August 2013 Low Emittance Machines Part 1: Beam Dynamics with Synchrotron Radiation Andy Wolski The Cockcroft Institute, and
More informationLipschitz Metrics for a Class of Nonlinear Wave Equations
Lipschitz Metrics for a Class of Nonlinear Wave Equations Alberto Bressan and Geng Chen * Department of Mathematics, Penn State University, University Park, Pa 1680, USA ** School of Mathematics Georgia
More informationEndogenous Growth: AK Model
Endogenous Growth: AK Model Prof. Lutz Hendricks Econ720 October 24, 2017 1 / 35 Endogenous Growth Why do countries grow? A question with large welfare consequences. We need models where growth is endogenous.
More informationMIT Spring 2016
MIT 18.655 Dr. Kempthorne Spring 2016 1 MIT 18.655 Outline 1 2 MIT 18.655 Decision Problem: Basic Components P = {P θ : θ Θ} : parametric model. Θ = {θ}: Parameter space. A{a} : Action space. L(θ, a) :
More informationNonuniform in time state estimation of dynamic systems
Systems & Control Letters 57 28 714 725 www.elsevier.com/locate/sysconle Nonuniform in time state estimation of dynamic systems Iasson Karafyllis a,, Costas Kravaris b a Department of Environmental Engineering,
More informationMultisolitons for NLS
Multisolitons for NLS Stefan LE COZ Beijing 2007-07-03 Plan 1 Introduction 2 Existence of multi-solitons 3 (In)stability NLS (NLS) { iut + u + g( u 2 )u = 0 u t=0 = u 0 u : R t R d x C (A0) (regular) g
More informationStandard Model of Particle Physics SS 2013
Lecture: Standard Model of Particle Physics Heidelberg SS 213 Flavour Physics I + II 1 Contents PART I Determination of the CKM Matrix CP Violation in Kaon system CP violation in the B-system PART II Search
More informationResearch Article On Existence, Uniform Decay Rates, and Blow-Up for Solutions of a Nonlinear Wave Equation with Dissipative and Source
Abstract and Applied Analysis Volume, Article ID 65345, 7 pages doi:.55//65345 Research Article On Existence, Uniform Decay Rates, and Blow-Up for Solutions of a Nonlinear Wave Equation with Dissipative
More informationNEW DEVELOPMENTS IN PREDICTIVE CONTROL FOR NONLINEAR SYSTEMS
NEW DEVELOPMENTS IN PREDICTIVE CONTROL FOR NONLINEAR SYSTEMS M. J. Grimble, A. Ordys, A. Dutka, P. Majecki University of Strathclyde Glasgow Scotland, U.K Introduction Model Predictive Control (MPC) is
More information1 Sectorial operators
1 1 Sectorial operators Definition 1.1 Let X and A : D(A) X X be a Banach space and a linear closed operator, respectively. If the relationships i) ρ(a) Σ φ = {λ C : arg λ < φ}, where φ (π/2, π); ii) R(λ,
More informationOn the Stability of the Best Reply Map for Noncooperative Differential Games
On the Stability of the Best Reply Map for Noncooperative Differential Games Alberto Bressan and Zipeng Wang Department of Mathematics, Penn State University, University Park, PA, 68, USA DPMMS, University
More informationFirst Order Initial Value Problems
First Order Initial Value Problems A first order initial value problem is the problem of finding a function xt) which satisfies the conditions x = x,t) x ) = ξ 1) where the initial time,, is a given real
More informationAndrea Marini. Introduction to the Many-Body problem (I): the diagrammatic approach
Introduction to the Many-Body problem (I): the diagrammatic approach Andrea Marini Material Science Institute National Research Council (Monterotondo Stazione, Italy) Zero-Point Motion Many bodies and
More informationNonlinear representation, backward SDEs, and application to the Principal-Agent problem
Nonlinear representation, backward SDEs, and application to the Principal-Agent problem Ecole Polytechnique, France April 4, 218 Outline The Principal-Agent problem Formulation 1 The Principal-Agent problem
More information