0. Grover Grover positive support Grover Ihara weighted Grover Ihara [19] P GL(2, F ) (F : p- ) p- Selberg (Ihara-Selberg Ihara ) 1980 Serre [

Size: px
Start display at page:

Download "0. Grover Grover positive support Grover Ihara weighted Grover Ihara [19] P GL(2, F ) (F : p- ) p- Selberg (Ihara-Selberg Ihara ) 1980 Serre ["

Transcription

1 Grover Grover positive support Grover Ihara weighted Grover Ihara [19 P GL(2, F ) (F : p- ) p- Selberg (Ihara-Selberg Ihara ) 198 Serre [32 Ihara 1986 Sunada [36,37 Ihara Ihara Hashimoto [ Ihara 199 Hashimoto [17 edge matrix Ihara 1992 Bass [5 Ihara growth 1999 Bartholdi [4 Bartholdi 11 Ihara ζ(s) = n=1 1 n s, s = σ + it C ζ(s) = p (1 p s ) 1, p : prime, reduced cycles Ihara cycle cycles 1

2 G = (V, E) V E V = V (G) E = E(G) V u, v uv = {u, v} ( ) G uv (u, v), (v, u) V V symmetric digraph D G = (V, D(G)) (u, v) arc G arcs D(G) = {(u, v), (v, u) uv E(G)} e = (u, v) D(G) u, v e, u = o(e), v = t(e) e 1 = (v, u) e = (u, v) inverse G path P = (e 1,, e n ) e i D(G), t(e i ) = o(e i+1 )(1 i n 1) e 1,, e n n P P = n o(p ) = o(e 1 ), t(p ) = t(e n ) P (o(p ), t(p ))-path path P = (e 1,, e n ) backtracking ( bump) e 1 i+1 = e i o(e 1 ) = t(e n ) path P = (e 1,, e n ) cycle cycle C 1 = (e 1,, e n ) C 2 = (e 1,, e n) k e i = e i+k mod n [C C cycle B r B r B r cycle cycle C reduced C C 2 backtracking cycle C prime cycle B C B r G prime, reduced cycle G v G π 1 (G, v) 1 (Sunada) G Ihara : Z(G, u) = Z G (u) = [C(1 u C ) 1 [C G prime, reduced cycle C C ([36 ) u C u 12 Ihara G n v 1,, v n G A(G) = (a ij ) 1 i,j n v i v j E(G)( (v i, v j ) D(G)) a ij = 1, a ij = v i deg G v i = {v j v i v j E(G)} G v deg G v = k( ) G k- Ihara ([38 ) 1 (Ihara) G (q + 1)- G Ihara : Z G (u) = (1 u 2 ) (m n) det(i n A(G)u + qu 2 I n ) 1 = exp( k 1 N k k uk ) m = E(G), n = V (G) N k G k reduced cycles 13 Ihara G n v 1,, v n m n n D = (d ij ) d ii = deg G v i 2m 2m B = B(G) = ((B) e,f ) e,f D(G) and J = J (G) = ((J ) e,f ) e,f D(G) (B) e,f = { 1 if t(e) = o(f), otherwise, (J ) e,f = { 1 if f = e 1, otherwise 2

3 B J G edge matrix 2 (Hashimoto; Bass) G Z G (u) 1 = det(i 2m u(b J )) = (1 u 2 ) m n det(i n ua(g) + u 2 (D I n )) = exp( k 1 m = E(G), n = V (G) N k G k reduced cycles N k k uk ) cycle edge matrix Ihara Remark G Ihara R ( (G) 1) 1 < R < (δ(g) 1) 1 ([23 ) (G),δ(G) G Lubotzky [24, Stark and Terras [35 Hashimoto [17, Northshield [29 Hashimoto [18, Terras [39 15 weighted weighted Hashimoto [16 Stark and Terras [34 G arc G D(G) = {e 1,, e m, e m+1,, e 2m }(e m+i = e 1 i (1 i m)) 2m u =: (u 1,, u 2m ) cycle C = (e i1,, e ik ) g(c) = u i1 u ik G edge ζ G (u) ζ G (u) = [C (1 g(c)) 1 [C G prime, reduced cycle 3 (Stark and Terras) m G ζ G (u) 1 = det(i 2m (B J )U) = det(i 2m U(B J )) U = diag(u 1,, u 2m ) u 1,, u 2m Mizuno and Sato [27 G u k = w(e k )t G weighted G V (G) = {v 1,, v n } n n W = (w ij ) 1 i,j n (v i, v j ) D(G) ij w ij w ij = W = W(G) G weighted matrix w(v i, v j ) = w ij, v i, v j V (G) w(e) = w ij, e = (v i, v j ) D(G) G path P = (e 1,, e r ) P norm w(p ) w(p ) = w(e 1 ) w(e r ) G weighted Z(G, w, t) = (1 w(c)t C ) 1 [C 3

4 [C G prime, reduced cycles weighted edge 4 (Mizuno and Sato) G n W = W(G) G weighted matrix w e D(G) w(e 1 ) = w(e) 1 Z(G, w, t) 1 = (1 t 2 ) m n det(i n tw(g) + t 2 (D I n )) w Z(G, w, t) weighted matrix G edge ζ G (u) u G (G ) u Watanabe and Fukumizu [4 G, ζ G (u) weighted matrix Gudder [ Meyer [26 2 Nayak and Vishwanath [28; 21 Ambainis, Bach, Nayak, Vishwanath and Watrous [2; 21 Aharonov, Ambainis, Kempe and Vazirani [1 22 Childs, Farhi and Gutmann [6 22 Konno [2 26 Emms, Hancock, Severini and Wilson [1 Grover positive support 28 Emms [9 Grover 21 Ren, Aleksic, Emms, Wilson and Hancock [3 Grover positive support Ihara edge matrix 211 Konno and Sato [22 Ihara weighted Grover positive support 22 ([21) Z 4

5 p q = 1 p (1 p 1) p q n(n = 1, 2, ) k(k =, ±1, ±2, ) l m l + m = n, l + m = k l = n k 2, m = n + k 2 p l q n l p l q n l n C l X n n P (X n = k) = n C l p l q n l X n n k= n P (X n = k) = n nc l p l q n l = (p + q) n = 1 p = q = 1/2 l= lim P (a X b n 1 b) = exp( x2 n n a 2π 2 )dx 1/ 2π exp( x 2 /2) N(, 1) 23 ([21,25) Z k Z ψ k = [ αk k= ψ k 2 = C 2 β k k= ( α k 2 + β k 2 ) = 1 ψ k α k, β k [ a b U = c d a 2 + b 2 = b 2 + c 2 = 1 a b + c d =, c = b, d = ā( = ad bc) p,q P = [ a b [, Q = c d U = P + Q 1 = p + q p, q 5

6 [ ψ n α n k = k β n k n(n = 1, 2, ) k(k =, ±1, ±2, ) ψ n k = Pψ n 1 k+1 + Qψn 1 k 1 (n = ) [ ψ α = φ = β [ C 2, ψ k = (k ) φ 2 = α 2 + β 2 = 1 φ n = 1 ψ 1 1 = Pψ 2 + Qψ = ψ 1 1 = Pψ + Qψ 2 = [ a b [ a b [ [ α β [ + c d [ + c d [ α β [ [ = = cα + dβ [ aα + bβ, k ±1 k ± 1 ψ 1 k = Pψ k+1 + Qψ k 1 = [ a b [ [ + c d [ [ = n = 2 ψ 2 = Pψ Qψ 1 1 = [ a b [ cα + dβ [ + c d [ aα + bβ = [ b(cα + dβ) c(aα + bβ) [ ψ 2 2 = d(aα + bβ) [, ψ 2 a(cα + dβ) 2 = k, ±2 k ± 1 ±1 [ ψ 2 k = X n n n k P (X n = k) = ψ n k 2 = α n k 2 + β n k 2 U = 1 2 [ [ α, β = 1 [ 1 2 i 6

7 n/k /2 1/ /4 1/2 1/4 1 n ( ) ([2 ) 5 (Konno) [ α β ( α 2 + β 2 = 1) lim P (u X v n n n v) = u X n n Z (n ) [ 1 a 2 π(1 z 2 ) a 2 z {1 2 ( α 2 β 2 + aα b β + āᾱbβ a 2 z}dz lim P (u X n n n v) = 24 Grover v u 1 π(1 z 2 ) 1 z 2 dz G n m G v d v = deg G v D(G) Emms [9 D(G) arc e = (u, v) pure x e = x uv { x e e D(G)} C 2m arc (u, v) arc (w, x) v = w x uv ψ = (u,v) D(G) α uv x uv, α uv C P ( x e ) = α uv α uv (u,v) D(G) α uv α uv = 1 ψ t+1, ψ t U ψ t+1 = Uψ t D(G) Grover U = (U (w,x),(u,v) )([14 ): 2/d v if v = w, x u, U (w,x),(u,v) = 2/d v 1 if v = w, x = u, otherwise 7

8 D(G) G Grover G V (G) = {u, v, w, x} D(G) = {(u, v), (v, u), (v, w), (w, v), (v, x), (x, v)} D(G) order (u, v), (v, u), (w, v), (v, w), (x, v), (v, x) Grover U 1 1/3 2/3 2/3 U = 1 2/3 1/3 2/3 1 2/3 2/3 1/3 ψ t = a x uv b x wv (a 2 + b 2 = 1) ψ t+1 = Uψ t = au x uv bu x wv x uv = T (1), x wv = T (1) ψ t+1 = a T ( 1/3 2/3 2/3) b T ( 2/3 1/3 2/3) = ( 1/3a 2/3) x vu + (2/3a + 1/3b) x vw + 2/3(a b) x vx ( 1/3a 2/3) 2 + (2/3a + 1/3b) 2 + 4/9(a b) 2 = a 2 + b 2 = 1 Grover 25 G, H G = H f : V (G) V (H) uv E(G) f(u)f(v) E(H) G, H G = H G, H G = H f(g) = f(h) f(g) Φ(G; λ) = det(λi A(G)) Φ(G; λ) = Φ(H; λ) G = H G, H ([3) Ihara Z(G, u) = Z(H; u) G = H G, H ([7) Shiau, Joynt and Coopersmith [33, Emms, Severini, Wilson,and Hancock [11, Douglas and Wang [8, Gamble, Friesen, Zhou, Joynt and Coopersmith [12 Emms, Hancock, Severini and Wilson [1 A = (a ij ) A positive support A + = (a + ij ) a + ij = { 1 if aij >, otherwise 8

9 1 (Emms, Hancock, Severini and Wilson) G, H G = H Spec((U(G) 3 ) + ) = Spec((U(H) 3 ) + ) Spec(F) F ( ) U(G) G Grover G n, k, λ, µ (n, k, λ, µ)- V (G) = n G v d v = k u, v λ x, y µ K n,n (2n, n,, n)- 14 ([1) (16, 6, 2, 2)- 2 (36, 15, 6, 6)- 32,548 ([11) Emms et al [11 (?) Spec((U(G) 3 ) + ) Φ((U(G) 3 ) + ; λ) 3 weighted Grover 31 weighted G n m W = W(G) G weighted matrix 2 2m 2m B w = B w (G) = (B (w) e,f ) e,f D(G) J = J (G) = (J e,f ) e,f D(G) B (w) e,f = { w(f) if t(e) = o(f), otherwise, J e,f = G 2 weighted Z 1 (G, w, t) = det(i n t(b w J )) 1 { 1 if f = e 1, otherwise e D(G) w(e) = 1 G 2 weighted G Ihara 2 weighted ([31 ) 6 (Sato) G W = W(G) G weighted matrix G 2 weighted Z 1 (G, w, t) 1 = (1 t 2 ) m n det(i n tw(g) + t 2 (D w I n )) n = V (G), m = E(G) D w = (d ij ) d ii = o(e)=v i w(e), V (G) = {v 1,, v n } 32 Grover Grover G n m n n T(G) = (T uv ) u,v V (G) { 1/(deg T uv = G u) if (u, v) D(G), otherwise 9

10 7 (Konno and Sato) G n v 1,, v n m G Grover U det(λi 2m U) = (λ 2 1) m n det((λ 2 + 1)I n 2λT(G)) = (λ2 1) m n det((λ 2 + 1)D 2λA(G)) d v1 d vn Proof G n m V (G) = {v 1,, v n } D(G) = {e 1,, e m, e 1 1,, e 1 m } j = 1,, n d j = d vj = deg v j 2m 2m B d = (B ef ) e,f D(G) { 2/do(f) if t(e) = o(f), B ef = otherwise 6 det(i 2m t(b d J )) = (1 t 2 ) m n det(i n tw d (G) + t 2 (D d I n )) W d (G) = (w uv ) u,v V (G) D d = (d uv ) u,v V (G) w uv = { 2/du if (u, v) D(G), otherwise, d uv = { 2 if u = v, otherwise d j (2/d j ) = 2 (1 j n) det(i 2m t( T B d T J )) = (1 t 2 ) m n det(i n tw d (G) + t 2 I n ) T B d T J = U and W d (G) = 2T(G) det(i 2m tu) = (1 t 2 ) m n det((1 + t 2 )I n 2tT(G)) t = 1/λ det (I 2m 1λ ) U = ( 1 1 ) m n λ 2 det ((1 + 1λ ) 2 I n 2λ ) T(G) det(λi 2m U) = (λ 2 1) m n det((λ 2 + 1)I n 2λT(G)) T(G) = D 1 A(G) det(λi 2m U) = (λ 2 1) m n det((λ 2 + 1)I n 2λD 1 A(G)) = (λ 2 1) m n det D 1 det((λ 2 + 1)D 2λA(G)) det D 1 = 1/(d v1 d vn ) QED det(λi 2m U) = (λ2 1) m n det((λ 2 + 1)D 2λA(G)) d v1 d vn 7 Grover U T(G) ([1) 1

11 Corollary 1 (Emms, Hancock, Severini and Wilson) G n m Grover U 2n λ = λ T ± i 1 λ 2 T λ T T(G) U 2(m n) ±1 Proof 7 det(λi 2m U) = (λ 2 1) m n (λ λ T λ) λ T Spec(T(G)) λ λ T λ = λ = λ T ± i 1 λ 2 T QED Emms et al [1 Grover Grover G Grover U Corollary 2 G n v 1, v n m k- Grover U 2n λ = λ A ± i k 2 λ 2 A k λ A A(G) U 2(m n) ±1 Proof 7 D = ki n kλ 2 + k 2λ A λ = QED det(λi 2m U) = (λ2 1) m n d v1 d vn λ A Spec(A(G)) λ = λ A ± i k 2 λ 2 A k 32 Grover positive support (kλ 2 + k 2λ A λ) G Grover positive support U + A(G) ([1) Ren, Aleksic, Emms, Wilson and Hancock [3 Grover positive support U + Ihara edge matrix 8 (Ren, Aleksic, Emms, Wilson and Hancock) G G δ δ(g) 2 B J G Perron-Frobenius operator edge matrix U G Grover B J U positive support B J = ( T U) + 11

12 2 8 Grover positive support U + 9 (Konno and Sato) G n v 1,, v n m G Grover positive support U + Proof 2 8 u = 1/λ det(λi 2m U + ) = (λ 2 1) m n det((λ 2 1)I n λa(g) + D) det(λi 2m uu + ) = det(i 2m u( T B T J )) = det(i 2m u(b J )) = (1 t 2 ) m n det(i n ua(g) + u 2 (D I n )) det (I 2m 1λ ) ( U+ = 1 1 ) m n ( λ 2 det I n 1 λ A(G) + 1 ) λ 2 (D I n) QED det(λi 2m U + ) = (λ 2 1) m n det((λ 2 1)I n λa(g) + D) 9 G Grover positive support U + A(G) Corollary 3 (Emms, Hancock, Severini and Wilson) Let G n m k- δ(g) 2 Grover positive support U + 2n λ = λ A 2 ± i k 1 λ 2 A /4 λ A A(G) U + 2(m n) ±1 Proof 9 D = ki n det(λi 2m U + ) = (λ 2 1) m n det(λ 2 + k 1)I n λa(g)) kλ 2 + k 1 λ A λ = QED = (λ 2 1) m n λ A Spec(A(G)) (kλ2 + k 1 λ A λ) λ = λ A 2 ± i k 1 λ 2 A /4 Spec(U), Spec(U + ), Spec((U 2 ) + ) G, H (n, k, λ, µ)- ( ) Spec(A(G)) = Spec(A(H)) = {k, θ, τ} θ = (λ τ) + 2, τ = (λ τ) +, = (λ τ) 2 + 4(k µ) 2 12

13 θ, τ n, k, λ, µ ([13 ) Cor 2 Cor 3 Emms et al [1 U, U +, (U 2 ) + Spec(U(G)) = Spec(U(H)), Spec(U(G) + ) = Spec(U(H) + ), Spec((U(G) 2 ) + ) = Spec((U(H) 2 ) + ) G = H Spec(U), Spec(U + ), Spec((U 2 ) + ) 4 Further Remark (1) 9 (U 2 ) + A(G) (2) 1 (U 3 ) + 2 (U 3 ) +? 3 (U n ) + 1,2,3 3 [1 D Aharonov, A Ambainis, J Kempe, and U V Vazirani, Quantum walks on graphs, Proc of the 33rd Annual ACM Symposium on Theory of Computing, 5-59, 21 [2 A Ambainis, E Bach, A Nayak, A Vishwanath and J Watrous, One-dimensional quantum walks, Proc of the 33rd Annual ACM Symposium on Theory of Computing, 37-49, 21 [3 G A Baker, Drum shapes and isospectral graphs, J Math Phys 7 (1966), [4 L Bartholdi, Counting paths in graphs, Enseign Math 45 (1999), [5 H Bass, The Ihara-Selberg zeta function of a tree lattice, Internat J Math 3 (1992), [6 A M Childs, E Farhi and S Gutmann, An example of the difference between quantum and classical random walks, Quantum Inform Process 1 (22), [7 Y Cooper, Properties determined by the Ihara zeta function of a graph, Electronic J Combin 16 (29), R84 [8 B L Douglas and J B Wang, Classically efficient graph isomorphism algorithm using quantum walks, arxiv: [9 D M Emms, Analysis of graph structure using quantum walks, Ph D Thesis, University of York, 28 [1 D M Emms, E R Hancock, S Severini and R C Wilson, A matrix representation of graphs and its spectrum as a graph invariant, Electronic J Combin 13 (26), R34 [11 D M Emms, S Severini, R C Wilson and E R Hancock, Coined quantum walks lift the cospectrality of graphs and trees, Pattern Recognit 42 (29), [12 J K Gamble, M Friesen, D Zhou, R Joynt and S N Coopersmith, Two-particle quantum walks applied to the graph isomorphism problem, Phys Rev A 81 (21),

14 [13 C, Godsil and G Royle, Algebraic Graph Theory, Springer, New York, 21 [14 L Grover, A first quantum mechanical algorithm for database search, Proc of the 28 th Annual ACM Symposium on Theory of Computing, , 1996 [15 S P Gudder, Quantum Probability, Academic Press Inc CA, 1988 [16 K Hashimoto, Zeta Functions of Finite Graphs and Representations of p-adic Groups, Adv Stud Pure Math Vol 15, pp , Academic Press, New York (1989) [17 K Hashimoto, On the zeta- and L-functions of finite graphs, Internat J Math 1 (199), [18 K Hashimoto, Artin-type L-functions and the density theorem for prime cycles on finite graphs, Internat J Math 3 (1992), [19 Y Ihara, On discrete subgroups of the two by two projective linear group over p-adic fields, J Math Soc Japan 18 (1966), [2 N Konno, Quantum random walks in one dimension, Quantum Inform Process 1 (22), [21,,, 28 [22 N Konno and I Sato, On the relation between quantum walks and zeta functions, Quantum Inform Process (in press) [23 M Kotani and T Sunada, Zeta functions of finite graphs, J Math Sci U Tokyo 7 (2), 7-25 [24 A Lubotzky, Cayley graphs: Eigenvalues, expanders and random walks, in: Surveys in Combinatorics, in: London Math Soc Lecture Note Ser, vol 218, Cambridge Univ Press, Cambridge, 1995, pp [25,,,, 21 [26 D Meyer, From quantum cellular automata to quantum lattice gases, J Statist Phys 85 (1996), [27 H Mizuno and I Sato, Weighted zeta functions of graphs, J Combin Theory Ser B 91 (24), [28 A Nayak, and A Vishwanath, Quantum walk on the line, DIMACS Technical report, 2-43, 2 [29 S Northshield, A note on the zeta function of a graph, J Combin Theory Ser B 74 (1998), [3 P Ren, T Aleksic, D Emms, RC Wilson and ER Hancock, Quantum walks, Ihara zweta functions and cospectrality in regular graphs, Quantum Inform Process (in press) [31 I Sato, A new Bartholdi zeta function of a graph Int J Algebra 1 (27), [32 J -P Serre, Trees, Springer-Verlag, New York, 198 [33 S -Y Shiau, R Joynt and S N Coopersmith, Physically-motivatred dynamical algorithms for the graph isomorphism problem, Quantum Inform Comput 5 (25), [34 H M Stark and A A Terras, Zeta functions of finite graphs and coverings, Adv Math 121 (1996), [35 H M Stark and A A Terras, Zeta functions of finite graphs and coverings III, Adv Math 28 (27), [36 T Sunada, L-Functions in Geometry and Some Applications, in Lecture Notes in Math, Vol 121, pp , Springer-Verlag, New York (1986) [37,,, 1988 [38 A Terras, Fourier Analysis on Finite Groups and Applications, Cambridge Univ Press, Cambridge 14

15 (1999) [39 A Terras, Zeta functions and chaos, preprint [4 Y Watanabe and K Fukumizu, Graph zeta function in the Bethe free energy and loopy belief propagation, to appear in Advances in Neural Information Processing Systems, 21 15

,, Andrej Gendiar (Density Matrix Renormalization Group, DMRG) 1 10 S.R. White [1, 2] 2 DMRG ( ) [3, 2] DMRG Baxter [4, 5] 2 Ising 2 1 Ising 1 1 Ising

,, Andrej Gendiar (Density Matrix Renormalization Group, DMRG) 1 10 S.R. White [1, 2] 2 DMRG ( ) [3, 2] DMRG Baxter [4, 5] 2 Ising 2 1 Ising 1 1 Ising ,, Andrej Gendiar (Density Matrix Renormalization Group, DMRG) 1 10 S.R. White [1, 2] 2 DMRG ( ) [3, 2] DMRG Baxter [4, 5] 2 Ising 2 1 Ising 1 1 Ising Model 1 Ising 1 Ising Model N Ising (σ i = ±1) (Free

More information

meiji_resume_1.PDF

meiji_resume_1.PDF β β β (q 1,q,..., q n ; p 1, p,..., p n ) H(q 1,q,..., q n ; p 1, p,..., p n ) Hψ = εψ ε k = k +1/ ε k = k(k 1) (x, y, z; p x, p y, p z ) (r; p r ), (θ; p θ ), (ϕ; p ϕ ) ε k = 1/ k p i dq i E total = E

More information

A11 (1993,1994) 29 A12 (1994) 29 A13 Trefethen and Bau Numerical Linear Algebra (1997) 29 A14 (1999) 30 A15 (2003) 30 A16 (2004) 30 A17 (2007) 30 A18

A11 (1993,1994) 29 A12 (1994) 29 A13 Trefethen and Bau Numerical Linear Algebra (1997) 29 A14 (1999) 30 A15 (2003) 30 A16 (2004) 30 A17 (2007) 30 A18 2013 8 29y, 2016 10 29 1 2 2 Jordan 3 21 3 3 Jordan (1) 3 31 Jordan 4 32 Jordan 4 33 Jordan 6 34 Jordan 8 35 9 4 Jordan (2) 10 41 x 11 42 x 12 43 16 44 19 441 19 442 20 443 25 45 25 5 Jordan 26 A 26 A1

More information

compact compact Hermann compact Hermite ( - ) Hermann Hermann ( ) compact Hermite Lagrange compact Hermite ( ) a, Σ a {0} a 3 1

compact compact Hermann compact Hermite ( - ) Hermann Hermann ( ) compact Hermite Lagrange compact Hermite ( ) a, Σ a {0} a 3 1 014 5 4 compact compact Hermann compact Hermite ( - ) Hermann Hermann ( ) compact Hermite Lagrange compact Hermite ( ) 1 1.1. a, Σ a {0} a 3 1 (1) a = span(σ). () α, β Σ s α β := β α,β α α Σ. (3) α, β

More information

QCD 1 QCD GeV 2014 QCD 2015 QCD SU(3) QCD A µ g µν QCD 1

QCD 1 QCD GeV 2014 QCD 2015 QCD SU(3) QCD A µ g µν QCD 1 QCD 1 QCD GeV 2014 QCD 2015 QCD SU(3) QCD A µ g µν QCD 1 (vierbein) QCD QCD 1 1: QCD QCD Γ ρ µν A µ R σ µνρ F µν g µν A µ Lagrangian gr TrFµν F µν No. Yes. Yes. No. No! Yes! [1] Nash & Sen [2] Riemann

More information

2.1 H f 3, SL(2, Z) Γ k (1) f H (2) γ Γ f k γ = f (3) f Γ \H cusp γ SL(2, Z) f k γ Fourier f k γ = a γ (n)e 2πinz/N n=0 (3) γ SL(2, Z) a γ (0) = 0 f c

2.1 H f 3, SL(2, Z) Γ k (1) f H (2) γ Γ f k γ = f (3) f Γ \H cusp γ SL(2, Z) f k γ Fourier f k γ = a γ (n)e 2πinz/N n=0 (3) γ SL(2, Z) a γ (0) = 0 f c GL 2 1 Lie SL(2, R) GL(2, A) Gelbart [Ge] 1 3 [Ge] Jacquet-Langlands [JL] Bump [Bu] Borel([Bo]) ([Ko]) ([Mo]) [Mo] 2 2.1 H = {z C Im(z) > 0} Γ SL(2, Z) Γ N N Γ (N) = {γ SL(2, Z) γ = 1 2 mod N} g SL(2,

More information

Perturbation method for determining the group of invariance of hierarchical models

Perturbation method for determining the group of invariance of hierarchical models Perturbation method for determining the group of invariance of hierarchical models 1 2 1 1 2 2009/11/27 ( ) 2009/11/27 1 / 31 2 3 p 11 p 12 p 13 p 21 p 22 p 23 (p ij 0, i;j p ij = 1). p ij = a i b j log

More information

Siegel Hecke 1 Siege Hecke L L Fourier Dirichlet Hecke Euler L Euler Fourier Hecke [Fr] Andrianov [An2] Hecke Satake L van der Geer ([vg]) L [Na1] [Yo

Siegel Hecke 1 Siege Hecke L L Fourier Dirichlet Hecke Euler L Euler Fourier Hecke [Fr] Andrianov [An2] Hecke Satake L van der Geer ([vg]) L [Na1] [Yo Siegel Hecke 1 Siege Hecke L L Fourier Dirichlet Hecke Euler L Euler Fourier Hecke [Fr] Andrianov [An2] Hecke Satake L van der Geer ([vg]) L [Na1] [Yo] 2 Hecke ( ) 0 1n J n =, Γ = Γ n = Sp(n, Z) = {γ GL(2n,

More information

21 2 26 i 1 1 1.1............................ 1 1.2............................ 3 2 9 2.1................... 9 2.2.......... 9 2.3................... 11 2.4....................... 12 3 15 3.1..........

More information

1. R n Ω ε G ε 0 Ω ε B n 2 Ωε = with Bu = 0 on Ω ε i=1 x 2 i ε +0 B Bu = u (Dirichlet, D Ω ε ), Bu = u ν (Neumann, N Ω ε ), Ω ε G ( ) / 25

1. R n Ω ε G ε 0 Ω ε B n 2 Ωε = with Bu = 0 on Ω ε i=1 x 2 i ε +0 B Bu = u (Dirichlet, D Ω ε ), Bu = u ν (Neumann, N Ω ε ), Ω ε G ( ) / 25 .. IV 2012 10 4 ( ) 2012 10 4 1 / 25 1. R n Ω ε G ε 0 Ω ε B n 2 Ωε = with Bu = 0 on Ω ε i=1 x 2 i ε +0 B Bu = u (Dirichlet, D Ω ε ), Bu = u ν (Neumann, N Ω ε ), Ω ε G ( ) 2012 10 4 2 / 25 1. Ω ε B ε t

More information

(a) (b) (c) (d) 1: (a) (b) (c) (d) (a) (b) (c) 2: (a) (b) (c) 1(b) [1 10] 1 degree k n(k) walk path 4

(a) (b) (c) (d) 1: (a) (b) (c) (d) (a) (b) (c) 2: (a) (b) (c) 1(b) [1 10] 1 degree k n(k) walk path 4 1 vertex edge 1(a) 1(b) 1(c) 1(d) 2 (a) (b) (c) (d) 1: (a) (b) (c) (d) 1 2 6 1 2 6 1 2 6 3 5 3 5 3 5 4 4 (a) (b) (c) 2: (a) (b) (c) 1(b) [1 10] 1 degree k n(k) walk path 4 1: Zachary [11] [12] [13] World-Wide

More information

24 I ( ) 1. R 3 (i) C : x 2 + y 2 1 = 0 (ii) C : y = ± 1 x 2 ( 1 x 1) (iii) C : x = cos t, y = sin t (0 t 2π) 1.1. γ : [a, b] R n ; t γ(t) = (x

24 I ( ) 1. R 3 (i) C : x 2 + y 2 1 = 0 (ii) C : y = ± 1 x 2 ( 1 x 1) (iii) C : x = cos t, y = sin t (0 t 2π) 1.1. γ : [a, b] R n ; t γ(t) = (x 24 I 1.1.. ( ) 1. R 3 (i) C : x 2 + y 2 1 = 0 (ii) C : y = ± 1 x 2 ( 1 x 1) (iii) C : x = cos t, y = sin t (0 t 2π) 1.1. γ : [a, b] R n ; t γ(t) = (x 1 (t), x 2 (t),, x n (t)) ( ) ( ), γ : (i) x 1 (t),

More information

ばらつき抑制のための確率最適制御

ばらつき抑制のための確率最適制御 ( ) http://wwwhayanuemnagoya-uacjp/ fujimoto/ 2011 3 9 11 ( ) 2011/03/09-11 1 / 46 Outline 1 2 3 4 5 ( ) 2011/03/09-11 2 / 46 Outline 1 2 3 4 5 ( ) 2011/03/09-11 3 / 46 (1/2) r + Controller - u Plant y

More information

1 = = = (set) (element) a A a A a A a A a A {2, 5, (0, 1)}, [ 1, 1] = {x; 1 x 1}. (proposition) A = {x; P (x)} P (x) x x a A a A Remark. (i) {2, 0, 0,

1 = = = (set) (element) a A a A a A a A a A {2, 5, (0, 1)}, [ 1, 1] = {x; 1 x 1}. (proposition) A = {x; P (x)} P (x) x x a A a A Remark. (i) {2, 0, 0, 2005 4 1 1 2 2 6 3 8 4 11 5 14 6 18 7 20 8 22 9 24 10 26 11 27 http://matcmadison.edu/alehnen/weblogic/logset.htm 1 1 = = = (set) (element) a A a A a A a A a A {2, 5, (0, 1)}, [ 1, 1] = {x; 1 x 1}. (proposition)

More information

DVIOUT-fujin

DVIOUT-fujin 2005 Limit Distribution of Quantum Walks and Weyl Equation 2006 3 2 1 2 2 4 2.1...................... 4 2.2......................... 5 2.3..................... 6 3 8 3.1........... 8 3.2..........................

More information

k + (1/2) S k+(1/2) (Γ 0 (N)) N p Hecke T k+(1/2) (p 2 ) S k+1/2 (Γ 0 (N)) M > 0 2k, M S 2k (Γ 0 (M)) Hecke T 2k (p) (p M) 1.1 ( ). k 2 M N M N f S k+

k + (1/2) S k+(1/2) (Γ 0 (N)) N p Hecke T k+(1/2) (p 2 ) S k+1/2 (Γ 0 (N)) M > 0 2k, M S 2k (Γ 0 (M)) Hecke T 2k (p) (p M) 1.1 ( ). k 2 M N M N f S k+ 1 SL 2 (R) γ(z) = az + b cz + d ( ) a b z h, γ = SL c d 2 (R) h 4 N Γ 0 (N) {( ) } a b Γ 0 (N) = SL c d 2 (Z) c 0 mod N θ(z) θ(z) = q n2 q = e 2π 1z, z h n Z Γ 0 (4) j(γ, z) ( ) a b θ(γ(z)) = j(γ, z)θ(z)

More information

20 4 20 i 1 1 1.1............................ 1 1.2............................ 4 2 11 2.1................... 11 2.2......................... 11 2.3....................... 19 3 25 3.1.............................

More information

linearal1.dvi

linearal1.dvi 19 4 30 I 1 1 11 1 12 2 13 3 131 3 132 4 133 5 134 6 14 7 2 9 21 9 211 9 212 10 213 13 214 14 22 15 221 15 222 16 223 17 224 20 3 21 31 21 32 21 33 22 34 23 341 23 342 24 343 27 344 29 35 31 351 31 352

More information

v v = v 1 v 2 v 3 (1) R = (R ij ) (2) R (R 1 ) ij = R ji (3) 3 R ij R ik = δ jk (4) i=1 δ ij Kronecker δ ij = { 1 (i = j) 0 (i

v v = v 1 v 2 v 3 (1) R = (R ij ) (2) R (R 1 ) ij = R ji (3) 3 R ij R ik = δ jk (4) i=1 δ ij Kronecker δ ij = { 1 (i = j) 0 (i 1. 1 1.1 1.1.1 1.1.1.1 v v = v 1 v 2 v 3 (1) R = (R ij ) (2) R (R 1 ) ij = R ji (3) R ij R ik = δ jk (4) δ ij Kronecker δ ij = { 1 (i = j) 0 (i j) (5) 1 1.1. v1.1 2011/04/10 1. 1 2 v i = R ij v j (6) [

More information

II 2 3.,, A(B + C) = AB + AC, (A + B)C = AC + BC. 4. m m A, m m B,, m m B, AB = BA, A,, I. 5. m m A, m n B, AB = B, A I E, 4 4 I, J, K

II 2 3.,, A(B + C) = AB + AC, (A + B)C = AC + BC. 4. m m A, m m B,, m m B, AB = BA, A,, I. 5. m m A, m n B, AB = B, A I E, 4 4 I, J, K II. () 7 F 7 = { 0,, 2, 3, 4, 5, 6 }., F 7 a, b F 7, a b, F 7,. (a) a, b,,. (b) 7., 4 5 = 20 = 2 7 + 6, 4 5 = 6 F 7., F 7,., 0 a F 7, ab = F 7 b F 7. (2) 7, 6 F 6 = { 0,, 2, 3, 4, 5 },,., F 6., 0 0 a F

More information

2016 Course Description of Undergraduate Seminars (2015 12 16 ) 2016 12 16 ( ) 13:00 15:00 12 16 ( ) 1 21 ( ) 1 13 ( ) 17:00 1 14 ( ) 12:00 1 21 ( ) 15:00 1 27 ( ) 13:00 14:00 2 1 ( ) 17:00 2 3 ( ) 12

More information

II (Percolation) ( 3-4 ) 1. [ ],,,,,,,. 2. [ ],.. 3. [ ],. 4. [ ] [ ] G. Grimmett Percolation Springer-Verlag New-York [ ] 3

II (Percolation) ( 3-4 ) 1. [ ],,,,,,,. 2. [ ],.. 3. [ ],. 4. [ ] [ ] G. Grimmett Percolation Springer-Verlag New-York [ ] 3 II (Percolation) 12 9 27 ( 3-4 ) 1 [ ] 2 [ ] 3 [ ] 4 [ ] 1992 5 [ ] G Grimmett Percolation Springer-Verlag New-York 1989 6 [ ] 3 1 3 p H 2 3 2 FKG BK Russo 2 p H = p T (=: p c ) 3 2 Kesten p c =1/2 ( )

More information

Macdonald, ,,, Macdonald. Macdonald,,,,,.,, Gauss,,.,, Lauricella A, B, C, D, Gelfand, A,., Heckman Opdam.,,,.,,., intersection,. Macdona

Macdonald, ,,, Macdonald. Macdonald,,,,,.,, Gauss,,.,, Lauricella A, B, C, D, Gelfand, A,., Heckman Opdam.,,,.,,., intersection,. Macdona Macdonald, 2015.9.1 9.2.,,, Macdonald. Macdonald,,,,,.,, Gauss,,.,, Lauricella A, B, C, D, Gelfand, A,., Heckman Opdam.,,,.,,., intersection,. Macdonald,, q., Heckman Opdam q,, Macdonald., 1 ,,. Macdonald,

More information

_TZ_4797-haus-local

_TZ_4797-haus-local 1.1.................................... 3.3.................................. 4.4......................... 8.5... 10.6.................... 1.7... 14 3 16 3.1 ()........................... 16 3. 7... 17

More information

D = [a, b] [c, d] D ij P ij (ξ ij, η ij ) f S(f,, {P ij }) S(f,, {P ij }) = = k m i=1 j=1 m n f(ξ ij, η ij )(x i x i 1 )(y j y j 1 ) = i=1 j

D = [a, b] [c, d] D ij P ij (ξ ij, η ij ) f S(f,, {P ij }) S(f,, {P ij }) = = k m i=1 j=1 m n f(ξ ij, η ij )(x i x i 1 )(y j y j 1 ) = i=1 j 6 6.. [, b] [, d] ij P ij ξ ij, η ij f Sf,, {P ij } Sf,, {P ij } k m i j m fξ ij, η ij i i j j i j i m i j k i i j j m i i j j k i i j j kb d {P ij } lim Sf,, {P ij} kb d f, k [, b] [, d] f, d kb d 6..

More information

Shunsuke Kobayashi 1 [6] [11] [7] u t = D 2 u 1 x 2 + f(u, v) + s L u(t, x)dx, L x (0.L), t > 0, Neumann 0 v t = D 2 v 2 + g(u, v), x (0, L), t > 0. x

Shunsuke Kobayashi 1 [6] [11] [7] u t = D 2 u 1 x 2 + f(u, v) + s L u(t, x)dx, L x (0.L), t > 0, Neumann 0 v t = D 2 v 2 + g(u, v), x (0, L), t > 0. x Shunsuke Kobayashi [6] [] [7] u t = D 2 u x 2 + fu, v + s L ut, xdx, L x 0.L, t > 0, Neumann 0 v t = D 2 v 2 + gu, v, x 0, L, t > 0. x2 u u v t, 0 = t, L = 0, x x. v t, 0 = t, L = 0.2 x x ut, x R vt, x

More information

T rank A max{rank Q[R Q, J] t-rank T [R T, C \ J] J C} 2 ([1, p.138, Theorem 4.2.5]) A = ( ) Q rank A = min{ρ(j) γ(j) J J C} C, (5) ρ(j) = rank Q[R Q,

T rank A max{rank Q[R Q, J] t-rank T [R T, C \ J] J C} 2 ([1, p.138, Theorem 4.2.5]) A = ( ) Q rank A = min{ρ(j) γ(j) J J C} C, (5) ρ(j) = rank Q[R Q, (ver. 4:. 2005-07-27) 1 1.1 (mixed matrix) (layered mixed matrix, LM-matrix) m n A = Q T (2m) (m n) ( ) ( ) Q I m Q à = = (1) T diag [t 1,, t m ] T rank à = m rank A (2) 1.2 [ ] B rank [B C] rank B rank

More information

Mazur [Ma1] Schlessinger [Sch] [SL] [Ma1] [Ma1] [Ma2] Galois [] 17 R m R R R M End R M) M R ut R M) M R R G R[G] R G Sets 1 Λ Noether Λ k Λ m Λ k C Λ

Mazur [Ma1] Schlessinger [Sch] [SL] [Ma1] [Ma1] [Ma2] Galois [] 17 R m R R R M End R M) M R ut R M) M R R G R[G] R G Sets 1 Λ Noether Λ k Λ m Λ k C Λ Galois ) 0 1 1 2 2 4 3 10 4 12 5 14 16 0 Galois Galois Galois TaylorWiles Fermat [W][TW] Galois Galois Galois 1 Noether 2 1 Mazur [Ma1] Schlessinger [Sch] [SL] [Ma1] [Ma1] [Ma2] Galois [] 17 R m R R R

More information

[2, 3, 4, 5] * C s (a m k (symmetry operation E m[ 1(a ] σ m σ (symmetry element E σ {E, σ} C s 32 ( ( =, 2 =, (3 0 1 v = x 1 1 +

[2, 3, 4, 5] * C s (a m k (symmetry operation E m[ 1(a ] σ m σ (symmetry element E σ {E, σ} C s 32 ( ( =, 2 =, (3 0 1 v = x 1 1 + 2016 12 16 1 1 2 2 2.1 C s................. 2 2.2 C 3v................ 9 3 11 3.1.............. 11 3.2 32............... 12 3.3.............. 13 4 14 4.1........... 14 4.2................ 15 4.3................

More information

0. I II I II (1) linear type: GL( ), Sp( ), O( ), (2) loop type: loop current Kac-Moody affine, hyperbolic (3) diffeo t

0. I II I II (1) linear type: GL( ), Sp( ), O( ), (2) loop type: loop current Kac-Moody affine, hyperbolic (3) diffeo t e-mail: [email protected] 0. I II I II (1) linear type: GL( ), Sp( ), O( ), (2) loop type: loop current Kac-Moody affine, hyperbolic (3) diffeo type: diffeo universal Teichmuller modular I. I-. Weyl

More information

第5章 偏微分方程式の境界値問題

第5章 偏微分方程式の境界値問題 October 5, 2018 1 / 113 4 ( ) 2 / 113 Poisson 5.1 Poisson ( A.7.1) Poisson Poisson 1 (A.6 ) Γ p p N u D Γ D b 5.1.1: = Γ D Γ N 3 / 113 Poisson 5.1.1 d {2, 3} Lipschitz (A.5 ) Γ D Γ N = \ Γ D Γ p Γ N Γ

More information

20 9 19 1 3 11 1 3 111 3 112 1 4 12 6 121 6 122 7 13 7 131 8 132 10 133 10 134 12 14 13 141 13 142 13 143 15 144 16 145 17 15 19 151 1 19 152 20 2 21 21 21 211 21 212 1 23 213 1 23 214 25 215 31 22 33

More information

Chern-Simons Jones 3 Chern-Simons 1 - Chern-Simons - Jones J(K; q) [1] Jones q 1 J (K + ; q) qj (K ; q) = (q 1/2 q

Chern-Simons   Jones 3 Chern-Simons 1 - Chern-Simons - Jones J(K; q) [1] Jones q 1 J (K + ; q) qj (K ; q) = (q 1/2 q Chern-Simons E-mail: [email protected] Jones 3 Chern-Simons - Chern-Simons - Jones J(K; q) []Jones q J (K + ; q) qj (K ; q) = (q /2 q /2 )J (K 0 ; q), () J( ; q) =. (2) K Figure : K +, K, K 0

More information

(Basics of Proability Theory). (Probability Spacees ad Radom Variables,, (Ω, F, P ),, X,. (Ω, F, P ) (probability space) Ω ( ω Ω ) F ( 2 Ω ) Ω σ (σ-fi

(Basics of Proability Theory). (Probability Spacees ad Radom Variables,, (Ω, F, P ),, X,. (Ω, F, P ) (probability space) Ω ( ω Ω ) F ( 2 Ω ) Ω σ (σ-fi I (Basics of Probability Theory ad Radom Walks) 25 4 5 ( 4 ) (Preface),.,,,.,,,...,,.,.,,.,,. (,.) (Basics of Proability Theory). (Probability Spacees ad Radom Variables...............2, (Expectatios,

More information

数学Ⅱ演習(足助・09夏)

数学Ⅱ演習(足助・09夏) II I 9/4/4 9/4/2 z C z z z z, z 2 z, w C zw z w 3 z, w C z + w z + w 4 t R t C t t t t t z z z 2 z C re z z + z z z, im z 2 2 3 z C e z + z + 2 z2 + 3! z3 + z!, I 4 x R e x cos x + sin x 2 z, w C e z+w

More information

( ) ) ) ) 5) 1 J = σe 2 6) ) 9) 1955 Statistical-Mechanical Theory of Irreversible Processes )

( ) ) ) ) 5) 1 J = σe 2 6) ) 9) 1955 Statistical-Mechanical Theory of Irreversible Processes ) ( 3 7 4 ) 2 2 ) 8 2 954 2) 955 3) 5) J = σe 2 6) 955 7) 9) 955 Statistical-Mechanical Theory of Irreversible Processes 957 ) 3 4 2 A B H (t) = Ae iωt B(t) = B(ω)e iωt B(ω) = [ Φ R (ω) Φ R () ] iω Φ R (t)

More information

(Basics of Proability Theory). (Probability Spacees ad Radom Variables,, (Ω, F, P ),, X,. (Ω, F, P ) (probability space) Ω ( ω Ω ) F ( 2 Ω ) Ω σ (σ-fi

(Basics of Proability Theory). (Probability Spacees ad Radom Variables,, (Ω, F, P ),, X,. (Ω, F, P ) (probability space) Ω ( ω Ω ) F ( 2 Ω ) Ω σ (σ-fi II (Basics of Probability Theory ad Radom Walks) (Preface),.,,,.,,,...,,.,.,,.,,. (Basics of Proability Theory). (Probability Spacees ad Radom Variables...............2, (Expectatios, Meas).............................

More information

n (1.6) i j=1 1 n a ij x j = b i (1.7) (1.7) (1.4) (1.5) (1.4) (1.7) u, v, w ε x, ε y, ε x, γ yz, γ zx, γ xy (1.8) ε x = u x ε y = v y ε z = w z γ yz

n (1.6) i j=1 1 n a ij x j = b i (1.7) (1.7) (1.4) (1.5) (1.4) (1.7) u, v, w ε x, ε y, ε x, γ yz, γ zx, γ xy (1.8) ε x = u x ε y = v y ε z = w z γ yz 1 2 (a 1, a 2, a n ) (b 1, b 2, b n ) A (1.1) A = a 1 b 1 + a 2 b 2 + + a n b n (1.1) n A = a i b i (1.2) i=1 n i 1 n i=1 a i b i n i=1 A = a i b i (1.3) (1.3) (1.3) (1.1) (ummation convention) a 11 x

More information

φ 4 Minimal subtraction scheme 2-loop ε 2008 (University of Tokyo) (Atsuo Kuniba) version 21/Apr/ Formulas Γ( n + ɛ) = ( 1)n (1 n! ɛ + ψ(n + 1)

φ 4 Minimal subtraction scheme 2-loop ε 2008 (University of Tokyo) (Atsuo Kuniba) version 21/Apr/ Formulas Γ( n + ɛ) = ( 1)n (1 n! ɛ + ψ(n + 1) φ 4 Minimal subtraction scheme 2-loop ε 28 University of Tokyo Atsuo Kuniba version 2/Apr/28 Formulas Γ n + ɛ = n n! ɛ + ψn + + Oɛ n =,, 2, ψn + = + 2 + + γ, 2 n ψ = γ =.5772... Euler const, log + ax x

More information

20 6 4 1 4 1.1 1.................................... 4 1.1.1.................................... 4 1.1.2 1................................ 5 1.2................................... 7 1.2.1....................................

More information

Dynkin Serre Weyl

Dynkin Serre Weyl Dynkin Naoya Enomoto 2003.3. paper Dynkin Introduction Dynkin Lie Lie paper 1 0 Introduction 3 I ( ) Lie Dynkin 4 1 ( ) Lie 4 1.1 Lie ( )................................ 4 1.2 Killing form...........................................

More information

1.2 (Kleppe, cf. [6]). C S 3 P 3 3 S 3. χ(p 3, I C (3)) 1 C, C P 3 ( ) 3 S 3( S 3 S 3 ). V 3 del Pezzo (cf. 2.1), S V, del Pezzo 1.1, V 3 del Pe

1.2 (Kleppe, cf. [6]). C S 3 P 3 3 S 3. χ(p 3, I C (3)) 1 C, C P 3 ( ) 3 S 3( S 3 S 3 ). V 3 del Pezzo (cf. 2.1), S V, del Pezzo 1.1, V 3 del Pe 3 del Pezzo (Hirokazu Nasu) 1 [10]. 3 V C C, V Hilbert scheme Hilb V [C]. C V C S V S. C S S V, C V. Hilbert schemes Hilb V Hilb S [S] [C] ( χ(s, N S/V ) χ(c, N C/S )), Hilb V [C] (generically non-reduced)

More information

1 Abstract 2 3 n a ax 2 + bx + c = 0 (a 0) (1) ( x + b ) 2 = b2 4ac 2a 4a 2 D = b 2 4ac > 0 (1) 2 D = 0 D < 0 x + b 2a = ± b2 4ac 2a b ± b 2

1 Abstract 2 3 n a ax 2 + bx + c = 0 (a 0) (1) ( x + b ) 2 = b2 4ac 2a 4a 2 D = b 2 4ac > 0 (1) 2 D = 0 D < 0 x + b 2a = ± b2 4ac 2a b ± b 2 1 Abstract n 1 1.1 a ax + bx + c = 0 (a 0) (1) ( x + b ) = b 4ac a 4a D = b 4ac > 0 (1) D = 0 D < 0 x + b a = ± b 4ac a b ± b 4ac a b a b ± 4ac b i a D (1) ax + bx + c D 0 () () (015 8 1 ) 1. D = b 4ac

More information

( ) 1., ([SU] ): F K k., Z p -, (cf. [Iw2], [Iw3], [Iw6]). K F F/K Z p - k /k., Weil., K., K F F p- ( 4.1).,, Z p -,., Weil..,,. Weil., F, F projectiv

( ) 1., ([SU] ): F K k., Z p -, (cf. [Iw2], [Iw3], [Iw6]). K F F/K Z p - k /k., Weil., K., K F F p- ( 4.1).,, Z p -,., Weil..,,. Weil., F, F projectiv ( ) 1 ([SU] ): F K k Z p - (cf [Iw2] [Iw3] [Iw6]) K F F/K Z p - k /k Weil K K F F p- ( 41) Z p - Weil Weil F F projective smooth C C Jac(C)/F ( ) : 2 3 4 5 Tate Weil 6 7 Z p - 2 [Iw1] 2 21 K k k 1 k K

More information

2 2 MATHEMATICS.PDF 200-2-0 3 2 (p n ), ( ) 7 3 4 6 5 20 6 GL 2 (Z) SL 2 (Z) 27 7 29 8 SL 2 (Z) 35 9 2 40 0 2 46 48 2 2 5 3 2 2 58 4 2 6 5 2 65 6 2 67 7 2 69 2 , a 0 + a + a 2 +... b b 2 b 3 () + b n a

More information

211 [email protected] 1 R *1 n n R n *2 R n = {(x 1,..., x n ) x 1,..., x n R}. R R 2 R 3 R n R n R n D D R n *3 ) (x 1,..., x n ) f(x 1,..., x n ) f D *4 n 2 n = 1 ( ) 1 f D R n f : D R 1.1. (x,

More information

19 σ = P/A o σ B Maximum tensile strength σ % 0.2% proof stress σ EL Elastic limit Work hardening coefficient failure necking σ PL Proportional

19 σ = P/A o σ B Maximum tensile strength σ % 0.2% proof stress σ EL Elastic limit Work hardening coefficient failure necking σ PL Proportional 19 σ = P/A o σ B Maximum tensile strength σ 0. 0.% 0.% proof stress σ EL Elastic limit Work hardening coefficient failure necking σ PL Proportional limit ε p = 0.% ε e = σ 0. /E plastic strain ε = ε e

More information

JFE.dvi

JFE.dvi ,, Department of Civil Engineering, Chuo University Kasuga 1-13-27, Bunkyo-ku, Tokyo 112 8551, JAPAN E-mail : [email protected] E-mail : [email protected] SATO KOGYO CO., LTD. 12-20, Nihonbashi-Honcho

More information

X G P G (X) G BG [X, BG] S 2 2 2 S 2 2 S 2 = { (x 1, x 2, x 3 ) R 3 x 2 1 + x 2 2 + x 2 3 = 1 } R 3 S 2 S 2 v x S 2 x x v(x) T x S 2 T x S 2 S 2 x T x S 2 = { ξ R 3 x ξ } R 3 T x S 2 S 2 x x T x S 2

More information