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,

Size: px
Start display at page:

Download "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,"

Transcription

1 (ver. 4: ) (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 C, rank rank B rank C. (3) C A = ( ) Q T rank A = max{rank Q[R Q, J] t-rank T [R T, C \ J] J C}. (4) R Q Q R T T C A 1

2 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, J], J C, Γ(J) = {i R T j J : T ij 0}, J C, γ(j) = Γ(J), J C. (5) min( ) J C rank A ρ(j) γ(j) J C. = J C rank A ρ(j) γ(j) J C. = J T 1 A = x 1 x 2 x 3 x 4 x f 1 t t 2 f 2 0 t t 4 (6) C = {x 1, x 2, x 3, x 4, x 5 }, R T = {f 1, f 2 }. rank A = 4 J = {x 1, x 3 } (4) 2 2 = 4 J = {x 3, x 4 } (5) = Laplace 1 ([1, p.136, Lemma 4.2.1]) A = ( ) Q T J C Q[R Q, J], T [R T, C \ J] ( ) Laplace det A = J C, J = R Q ± det Q[R Q, J] det T [R T, C \ J] det A 0 J det Q[R Q, J] 0 det T [R T, C \ J] 0. 2

3 J 0 Q[R Q, J 0 ] T [R T, C \J 0 ] det T [R T, C \J 0 ] t 1 t 2 t k ( k = R T ) T J t 1 t 2 t k det Q[R Q, J 0 ] 0 det A A r = rank A r A R, C ( R = C = r) A A = ( ) Q T Q I Q T I T 1 J C Q[I Q, J], T [I T, C \ J] J = rank Q[I Q, J] rank Q[R Q, J], C \ J = t-rank T [I T, C \ J] t-rank T [R T, C \ J] rank A = r = C rank Q[R Q, J] t-rank T [R T, C \ J]. (4) rank A max{ }. (4) J Q[R Q, J], T [R T, C \ J] I Q R Q, J Q J, I T R T, J T C \ J I Q = J Q = rank Q[I Q, J Q ] = rank Q[R Q, J], I T = J T = t-rank T [I T, J T ] = t-rank T [R T, C \ J]. 1 A[I Q I T, J Q J T ] rank A[I Q I T, J Q J T ] = J Q J T. rank A rank A[I Q I T, J Q J T ] = J Q J T = rank Q[R Q, J] t-rank T [R T, C \ J] = max{ } rank A rank A[R, J] rank A[R, C \ J] rank A[R Q, J] rank A[R T, J] rank A[R, C \ J] = rank Q[R Q, J] rank T [R T, J] rank A[R, C \ J] ρ(j) γ(j) C \ J. rank Q[R Q, J] = ρ(j), rank T [R T, J] ( ) = γ(j), rank A[R, C \ J] ( ) = C \ J. 3

4 (a) (b) ( ) primal (c) dual 2. Q = Q = 3. (a) Q (b) primal = (c) rank A ρ(j) γ(j) J C J dual = G = (R T C Q, C; ). C Q = {j Q j C} C (j C j Q C Q ), = {(i, j) i R T, j C, T ij 0}, = {(j Q, j) j C}. M M M R T C Q C M M M = 2 M M Q C (M ) Q 1 rank A 1 Q M(Q) C Q C Q M M(Q) 4

5 R T C C Q x 1 f 1 x 2 x 1Q x 2Q x 3 x 3Q f 2 x 4 x 4Q x 5 x 5Q 1: Graph G ( : arc in a maximum independent matching M) 2 ([1, p.143, Theorem ]) M rank A[R, C M] = M. rank A M. rank A = max{ M M }. ( ) J Q = C (M ), J T = C (M ) J Q J T = C M rank Q[R Q, J Q ] = J Q, t-rank T [R T, J T ] = J T. 1 rank A[R, J Q J T ] = J Q J T = M. M rank A A = x 1 x 2 x 3 x 4 x f 1 t t 2 f 2 0 t t 4 1 C = {x 1, x 2, x 3, x 4, x 5 }, C Q = {x 1Q, x 2Q, x 3Q, x 4Q, x 5Q }, R T = {f 1, f 2 }. M = {(f 1, x 5 ), (f 2, x 2 ), (x 1Q, x 1 ), (x 3Q, x 3 )}. rank A = M = 4 J = C (M ) = {x 1, x 3 } (4) 1 2 (7) (7) M G = G M Ṽ = R T C Q C Ẽ = E M M M 5

6 M C R T C Q 3 2 E Q ( M(Q) ) I = C (M ), (8) J = {j C \ I rank Q[R Q, I {j}] = I } (9) rank Q[R Q, I] = I E i I, j J (i Q, j Q ) E I i j Q (10) E C Q S S S = (R T \ M) {j Q C Q j C \ (I J)}, S = C \ M S S Q P I, J, S, E P I P Q C I Z J = {j C \ I P ij = 0 ( i Z)}, E = {(i Q, j Q ) i I, j J, P ij 0} (11) P = I J K i 1 i 2 i 3 i 4 i 5 i 6 j 1 j 2 j 3 j 4 k 1 k 2 k 3 i e e e e i e e e e I i e e e e i e e e e i e e e e i e e e e z s s s Z z s s s z s s s z s s s (12) e E K = C \ (I J) s C Q C Q S = {j Q C Q j K} (7) M = {(f 2, x 2 ), (x 3Q, x 3 )} 2 P = x 1 x 2 x 3 x 4 x

7 f 1 R T C C Q f 2 x 1 x 2 x 3 x 4 x 5 x 1Q x 2Q x 3Q E x 4Q x 5Q 2: Graph G for an independent matching M ( : arc in M; : vertex in S ; : vertex in S ) S = {f 1, x 1Q, x 2Q }, S = {x 1, x 4, x 5 } (8) I = {x 3 }, (9) J = {x 4, x 5 }. 2 2 (7) M = {(f 1, x 1 ), (x 2Q, x 2 ), (x 4Q, x 4 )} E (i Q, j Q ) E = I i j Q (10) I (8) E R T E (i 1Q, j 1Q ),..., (i kq, j kq ) I = (I \ {i 1,..., i k }) {j 1,..., j k } Q (i p, j q ) (p < q) (k = 4 ) e 0 s I Q ( no-short cut lemma [1, p.83, Lemma ]). {i 1, i 2, i 3, i 4 }, {j 1, j 2, j 3, j 4 } 7

8 P = I i 1 i 2 i 3 i 4 j 1 j 2 j 3 j 4 i e 0 s 0 s 0 s i e 0 s 0 s i e 0 s i e I (13) S S S W J = W C 3 J 1. W S, W S =. 2. C C Q W (j W j Q W ) 3 J rank A ρ(j) γ(j) J C ( ). ( ) I T = W R T C J Q = J (M ) (Q J ) J T = J (M ) (T J ) K Q = (C \ J) (M ) (Q J ) K T = (C \ J) (M ) (T J ) Q J C \ J S C M S J Q J T K Q K T J Q I O 1 ρ(j) K Q O 2 O 1 O 2 I O 3 O 1 O 3 O 1 Q K I T T J γ(j) R T O 4 O 4 O 4 T K S R T O 4 O 4 O 4 (14) 1. T J, T K T 8

9 S R T C C Q I T J K T K Q J T J Q S W S 3: Graph G with no path from S to S ( : arc in M) 2. Q J Q K Q O C Q \ W C Q W C Q \ W {j Q C Q j K Q } (K Q C Q ) C Q W J C Q O W S = J C Q (, j J j Q S C Q ) O R T \ W (= R T \ I T ) J O M 2 rank A[R, C M] = C M. 7. O 2, O 3 ρ(j) = J Q. 8. O 4 T J Γ(J) = I T γ(j) = I T = J T. rank A rank A[R, C M] = C M = J Q J T C \ J = ρ(j) γ(j) C \ J M = {(f 1, x 5 ), (f 2, x 2 ), (x 1Q, x 1 ), (x 3Q, x 3 )} 4 P = x 1 x 2 x 3 x 4 x K T Q S C Q j K T j Q C Q Q K j 9

10 R T C C Q f 1 f 2 x 1 x 2 x 3 x 4 x 1Q x 2Q E x 3Q x 4Q x 5 x 5Q W 4: Graph G for a maximum independent matching M ( : arc in M; S =, : vertex in S, vertex in W is reachable to S ) S = {x 4 } W = {x 3, x 4, x 3Q, x 4Q }J = W C = {x 3, x 4 } J (5) 3 I T =, J T =, J Q = {x 3 }, K Q = {x 1 }, K T = {x 2, x 5 } 3 1 W Edmonds Hall Ore ( ) τ : 2 C Z: τ(j) = t-rank T [R T, J], J C, γ : 2 C Z Hall Ore τ(j) = min{γ(k) K K J} J, J C. (15) (Hall Ore Edmonds 1 A, Q, T C M(A), M(Q), M(T ) 1. A M(A) rank A = rank M(A). 2. Q, T M(Q), M(T ), ρ, τ M(Q), M(T ) 10

11 3. 1 M(A) M(Q) M(T ) M(A) = M(Q) M(T ). 4. Edmonds rank (M(Q) M(T )) = min{ρ(j) τ(j) J J C} C. (16) 2 (5) rank A = rank M(A) = rank (M(Q) M(T )) = min{ρ(j) τ(j) J J C} C J = min{ρ(j) min{γ(k) K K J} J C} C J K = min {min {ρ(j) J K} γ(k) K K C} C K J = min{ρ(k) γ(k) K K C} C. K 4 rank A = min{ρ(j) τ(j) J J C} C (17) (5) A = Q T rank A = max{rank Q[I, J] t-rank T [R \ I, C \ J] I R, J C}. (18) 4 ([1, p.141, Theorem ]) A = QT I R, J C (i) I J rank Q[I, J] = R C rank A, (ii) rank T [I, J] = 0. rank T [I, J] = 0 I J rank Q[I, J] R C rank A. = I R, J C rank T [I, J] = 0 I J rank Q[I, J] R C rank A. = 2 11

12 ([1, p.139, Lemma 4.2.7]) A = Q T I R, J C Q[I, J], T [R \ I, C \ J] ( ) det A = det(q T ) = ± det Q[I, J] det T [R \ I, C \ J]. I R J C det A 0 I, J det Q[I, J] 0 det T [R \ I, C \ J] 0. I 0, J 0 Q[I 0, J 0 ] T [R \ I 0, C \ J 0 ] det T [R \ I 0, C \J 0 ] t 1 t 2 t k ( k = R\I 0 ) T (I, J) t 1 t 2 t k det Q[I 0, J 0 ] 0 det A T [I, J] = O rank A rank A[R, J] rank A[R, C \ J] rank A[I, J] rank A[R \ I, J] rank A[R, C \ J] rank Q[I, J] R \ I C \ J A = Q T Ã 2 J R C I = R \ J, J = C J (1) ρ( J) = rank Q[I, J] R \ I (2) I Γ(J) = (3) rank T [I, J] = 0 (4) I J rank Q[I, J] R C rank A A = ( ) ( Q T A = Q ) ( O O ) T 4 (i), (ii) (I, J) 12

13 (1) I R Q (2) ρ(j) = rank Q[I, J] (3) γ(j) R \ I (4) rank A ρ(j) γ(j) J C 4 [1, 4.2.4]. Algorithm for computing the rank of an LM-matrix A Step 1: Step 2: Step 3: M := ; base[i] := 0 (i R Q ); P [i, j] := Q ij (i R Q, j C); S := unit matrix of order m Q. I := {i C i Q M C Q }; J := {j C \ I h : base[h] = 0 P [h, j] = 0}; S T := R T \ M ; S := S T S Q ; S := C \ M ; S Q := {j Q C Q j C \ (I J)}; E := {(i Q, j Q ) h R Q, j J, P [h, j] 0, i = base[h] 0}; [Ẽ is updated accordingly] If there exists in G = (Ṽ, Ẽ) a directed path from S to S then go to Step 3; otherwise (including the case where S = or S = ) stop with the conclusion that rank A = M. Let L ( Ẽ) be (the set of arcs on) a shortest path from S to S ( shortest in the number of arcs); M := (M \ L) {(j, i) (i, j) L } {(j, j Q ) (j Q, j) L }; If the initial vertex ( S ) of the path L belongs to S Q, then do the following: {Let j Q ( S Q C Q) be the initial vertex; Find h such that base[h] = 0 and P [h, j] 0; base[h] := j; w := 1/P [h, j]; P [k, l] := P [k, l] w P [k, j] P [h, l] [j C corresponds to j Q C Q ] (k R Q \ {h}, l C \ {j}); S[k, l] := S[k, l] w P [k, j] S[h, l] (k R Q \ {h}, l R Q ); P [k, j] := 0 (k R Q \ {h}) }; 13

14 f 1 f 2 R T C C Q x 1 x 2 x 3 x 4 x 5 x 1Q x 2Q x 3Q x 4Q x 5Q 5: Graph G (0) (: vertex in S ; : vertex in S ) For all (i Q, j Q ) L E (in the order from S to S along L) do the following: {Find h such that i = base[h]; [j C corresponds to j Q C Q ] base[h] := j; w := 1/P [h, j]; P [k, l] := P [k, l] w P [k, j] P [h, l] (k R Q \ {h}, l C \ {j}); S[k, l] := S[k, l] w P [k, j] S[h, l] (k R Q \ {h}, l R Q ); P [k, j] := 0 (k R Q \ {h}) }; Go to Step Step 1: M := ; base := r 1 0 r 2 0, P := x 1 x 2 x 3 x 4 x 5 r r , S := Step 2: I := ; J := {x 5 }; S T := {f 1, f 2 }; S Q := {x 1Q, x 2Q, x 3Q, x 4Q }; S := {f 1, f 2, x 1Q, x 2Q, x 3Q, x 4Q }; S := {x 1, x 2, x 3, x 4, x 5 }; E := ; There exists a path from S to S. [ = G (0), 5] Step 3: L := {(x 1Q, x 1 )}; M := {(x 1, x 1Q )}; 14

15 f 1 f 2 R T C C Q x 1 x 2 x 3 x 4 x 5 x 1Q x 2Q x 3Q x 4Q x 5Q 6: Graph G (1) ( : arc in M; : vertex in S ; : vertex in S ) The initial vertex x 1Q of L is in S Q, and the matrices are updated (with h = r 1) to base := r 1 x 1 r 2 0, P := x 1 x 2 x 3 x 4 x 5 r r , S := Noting L E = we return to Step 2. Step 2: I := {x 1 }; J := {x 5 }; S T := {f 1, f 2 }; S Q := {x 2Q, x 3Q, x 4Q }; S := {f 1, f 2, x 2Q, x 3Q, x 4Q }; S := {x 2, x 3, x 4, x 5 }; E := ; There exists a path from S to S. [ = G (1), 6] Step 3: L := {(x 2Q, x 2 )}; M := {(x 1, x 1Q ), (x 2, x 2Q )}; The initial vertex x 2Q of L is in S Q, and the matrices are updated (with h = r 2) to base := r 1 x 1 r 2 x 2, P := x 1 x 2 x 3 x 4 x 5 r /2 1/2 0 r , S := 1 1/ Noting L E = we return to Step 2. Step 2: I := {x 1, x 2 }; J := {x 3, x 4, x 5 }; S T := {f 1, f 2 }; S Q := ; S := {f 1, f 2 }; S := {x 3, x 4, x 5 }; E := {(x 1Q, x 3Q ), (x 1Q, x 4Q ), (x 2Q, x 3Q ), (x 2Q, x 4Q )}; There exists a path from S to S. [ = G (2), 7] 15

16 f 1 f 2 R T C C Q x 1 x 2 x 3 x 4 x 5 x 1Q x 2Q x E 3Q x 4Q x 5Q 7: Graph G (2) ( : arc in M; : vertex in S ; : vertex in S ) R T C C Q f 1 x 2 f 2 x 1 x 3 x 4 x 5 x 1Q x 2Q x E 3Q x 4Q x 5Q 8: Graph G (3) ( : arc in M; : vertex in S ; : vertex in S ) Step 3: L := {(f 1, x 5 )}; M := {(x 1, x 1Q ), (x 2, x 2Q ), (x 5, f 1 )}; The initial vertex f 1 S Q and L E =, and therefore the matrices remain unchanged and we return to Step 2. Step 2: I := {x 1, x 2 }; J := {x 3, x 4, x 5 }; S T := {f 2}; S Q := ; S := {f 2 }; S := {x 3, x 4 }; E := {(x 1Q, x 3Q ), (x 1Q, x 4Q ), (x 2Q, x 3Q ), (x 2Q, x 4Q )}; There exists a path from S to S. [ = G (3), 8] Step 3: L := {(f 2, x 2 ), (x 2, x 2Q ), (x 2Q, x 3Q ), (x 3Q, x 3 )}; M := {(x 1, x 1Q ), (x 3, x 3Q ), (x 5, f 1 ), (x 2, f 2 )}; 16

17 R T C C Q f 1 f 2 x 1 x 2 x 3 x 4 x 1Q x 2Q x E 3Q x 4Q x 5 x 5Q 9: Graph G (4) ( : arc in M; S =, : vertex in S ) The initial vertex f 2 S Q and L E = {(x 2Q, x 3Q )}, and the matrices are updated (with h = r 2 ) to base := r 1 x 1 r 2 x 3, P := x 1 x 2 x 3 x 4 x 5 r r , S := Step 2: I := {x 1, x 3 }; J := {x 2, x 4, x 5 }; S T := ; S Q := ; S := ; S := {x 4 }; E := {(x 1Q, x 2Q ), (x 3Q, x 2Q ), (x 3Q, x 4Q )}; There exists no path from S (= ) to S ; We stop with the conclusion that rank A = M = 4. [ = G (4), 9] [1] K. Murota: Matrices and Matroids for Systems Analysis, Springer-Verlag, Berlin,

Page 1 of 6 B (The World of Mathematics) November 20, 2006 Final Exam 2006 Division: ID#: Name: 1. p, q, r (Let p, q, r are propositions. ) (10pts) (a

Page 1 of 6 B (The World of Mathematics) November 20, 2006 Final Exam 2006 Division: ID#: Name: 1. p, q, r (Let p, q, r are propositions. ) (10pts) (a Page 1 of 6 B (The World of Mathematics) November 0, 006 Final Exam 006 Division: ID#: Name: 1. p, q, r (Let p, q, r are propositions. ) (a) (Decide whether the following holds by completing the truth

More information

Test IV, March 22, 2016 6. Suppose that 2 n a n converges. Prove or disprove that a n converges. Proof. Method I: Let a n x n be a power series, which converges at x = 2 by the assumption. Applying Theorem

More information

Isogai, T., Building a dynamic correlation network for fat-tailed financial asset returns, Applied Network Science (7):-24, 206,

Isogai, T., Building a dynamic correlation network for fat-tailed financial asset returns, Applied Network Science (7):-24, 206, H28. (TMU) 206 8 29 / 34 2 3 4 5 6 Isogai, T., Building a dynamic correlation network for fat-tailed financial asset returns, Applied Network Science (7):-24, 206, http://link.springer.com/article/0.007/s409-06-0008-x

More information

h23w1.dvi

h23w1.dvi 24 I 24 2 8 10:00 12:30 1),. Do not open this problem booklet until the start of the examination is announced. 2) 3.. Answer the following 3 problems. Use the designated answer sheet for each problem.

More information

…_…C…L…fi…J…o†[fiü“ePDF/−mflF™ƒ

…_…C…L…fi…J…o†[fiü“ePDF/−mflF™ƒ 80 80 80 3 3 5 8 10 12 14 14 17 22 24 27 33 35 35 37 38 41 43 46 47 50 50 52 54 56 56 59 62 65 67 71 74 74 76 80 83 83 84 87 91 91 92 95 96 98 98 101 104 107 107 109 110 111 111 113 115

More information

H J

H J H J qt q w e r t qt q w e r t qt q w e r t qt q w e r t qt q w e r t qt q w e r t qt q w e r t H qt q w e r t qt q w e r t J qt q w e r t D qt q w e r t qt q w e r t qt q w e r t qt D q w e r t qy q w

More information

Tabulation of the clasp number of prime knots with up to 10 crossings

Tabulation of the clasp number of prime knots  with up to 10 crossings . Tabulation of the clasp number of prime knots with up to 10 crossings... Kengo Kawamura (Osaka City University) joint work with Teruhisa Kadokami (East China Normal University).. VI December 20, 2013

More information

Numerical Analysis II, Exam End Term Spring 2017

Numerical Analysis II, Exam End Term Spring 2017 H. Ammari W. Wu S. Yu Spring Term 2017 Numerical Analysis II ETH Zürich D-MATH End Term Spring 2017 Problem 1 Consider dx = f(t, x), t [0, T ] dt x(0) = x 0 R [28 Marks] with f C subject to the Lipschitz

More information

1 # include < stdio.h> 2 # include < string.h> 3 4 int main (){ 5 char str [222]; 6 scanf ("%s", str ); 7 int n= strlen ( str ); 8 for ( int i=n -2; i

1 # include < stdio.h> 2 # include < string.h> 3 4 int main (){ 5 char str [222]; 6 scanf (%s, str ); 7 int n= strlen ( str ); 8 for ( int i=n -2; i ABC066 / ARC077 writer: nuip 2017 7 1 For International Readers: English editorial starts from page 8. A : ringring a + b b + c a + c a, b, c a + b + c 1 # include < stdio.h> 2 3 int main (){ 4 int a,

More information

2 (March 13, 2010) N Λ a = i,j=1 x i ( d (a) i,j x j ), Λ h = N i,j=1 x i ( d (h) i,j x j ) B a B h B a = N i,j=1 ν i d (a) i,j, B h = x j N i,j=1 ν i

2 (March 13, 2010) N Λ a = i,j=1 x i ( d (a) i,j x j ), Λ h = N i,j=1 x i ( d (h) i,j x j ) B a B h B a = N i,j=1 ν i d (a) i,j, B h = x j N i,j=1 ν i 1. A. M. Turing [18] 60 Turing A. Gierer H. Meinhardt [1] : (GM) ) a t = D a a xx µa + ρ (c a2 h + ρ 0 (0 < x < l, t > 0) h t = D h h xx νh + c ρ a 2 (0 < x < l, t > 0) a x = h x = 0 (x = 0, l) a = a(x,

More information

n 2 n (Dynamic Programming : DP) (Genetic Algorithm : GA) 2 i

n 2 n (Dynamic Programming : DP) (Genetic Algorithm : GA) 2 i 15 Comparison and Evaluation of Dynamic Programming and Genetic Algorithm for a Knapsack Problem 1040277 2004 2 25 n 2 n (Dynamic Programming : DP) (Genetic Algorithm : GA) 2 i Abstract Comparison and

More information

1 I

1 I 1 I 3 1 1.1 R x, y R x + y R x y R x, y, z, a, b R (1.1) (x + y) + z = x + (y + z) (1.2) x + y = y + x (1.3) 0 R : 0 + x = x x R (1.4) x R, 1 ( x) R : x + ( x) = 0 (1.5) (x y) z = x (y z) (1.6) x y =

More information

SQUFOF NTT Shanks SQUFOF SQUFOF Pentium III Pentium 4 SQUFOF 2.03 (Pentium 4 2.0GHz Willamette) N UBASIC 50 / 200 [

SQUFOF NTT Shanks SQUFOF SQUFOF Pentium III Pentium 4 SQUFOF 2.03 (Pentium 4 2.0GHz Willamette) N UBASIC 50 / 200 [ SQUFOF SQUFOF NTT 2003 2 17 16 60 Shanks SQUFOF SQUFOF Pentium III Pentium 4 SQUFOF 2.03 (Pentium 4 2.0GHz Willamette) 60 1 1.1 N 62 16 24 UBASIC 50 / 200 [ 01] 4 large prime 943 2 1 (%) 57 146 146 15

More information

AtCoder Regular Contest 073 Editorial Kohei Morita(yosupo) A: Shiritori if python3 a, b, c = input().split() if a[len(a)-1] == b[0] and b[len(

AtCoder Regular Contest 073 Editorial Kohei Morita(yosupo) A: Shiritori if python3 a, b, c = input().split() if a[len(a)-1] == b[0] and b[len( AtCoder Regular Contest 073 Editorial Kohei Morita(yosupo) 29 4 29 A: Shiritori if python3 a, b, c = input().split() if a[len(a)-1] == b[0] and b[len(b)-1] == c[0]: print( YES ) else: print( NO ) 1 B:

More information

¿ô³Ø³Ø½øÏÀ¥Î¡¼¥È

¿ô³Ø³Ø½øÏÀ¥Î¡¼¥È 2011 i N Z Q R C A def B, A B. ii..,.,.. (, ), ( ),.?????????,. iii 04-13 04-20 04-27 05-04 [ ] 05-11 05-18 05-25 06-01 06-08 06-15 06-22 06-29 07-06 07-13 07-20 07-27 08-03 10-05 10-12 10-19 [ ] 10-26

More information

Microsoft PowerPoint - 06graph3.ppt [互換モード]

Microsoft PowerPoint - 06graph3.ppt [互換モード] I118 グラフとオートマトン理論 Graphs and Automata 担当 : 上原隆平 (Ryuhei UEHARA) uehara@jaist.ac.jp http://www.jaist.ac.jp/~uehara/ 1/20 6.14 グラフにおける探索木 (Search Tree in a Graph) グラフG=(V,E) における探索アルゴリズム : 1. Q:={v { 0 }

More information

! " # $ % & ' ( ) +, -. / 0 1 2 3 4 5 6 7 8 9 : ; < = >? @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z [ ] ^ _ ` a b c d e f h i j k l m n o p q r s t u v w x y z { } ~ This product is

More information

[2] 1. 2. 2 2. 1, [3] 2. 2 [4] 2. 3 BABOK BABOK(Business Analysis Body of Knowledge) BABOK IIBA(International Institute of Business Analysis) BABOK 7

[2] 1. 2. 2 2. 1, [3] 2. 2 [4] 2. 3 BABOK BABOK(Business Analysis Body of Knowledge) BABOK IIBA(International Institute of Business Analysis) BABOK 7 32 (2015 ) [2] Projects of the short term increase at present. In order to let projects complete without rework and delays, it is important that request for proposals (RFP) are written by reflecting precisely

More information

Feynman Encounter with Mathematics 52, [1] N. Kumano-go, Feynman path integrals as analysis on path space by time slicing approximation. Bull

Feynman Encounter with Mathematics 52, [1] N. Kumano-go, Feynman path integrals as analysis on path space by time slicing approximation. Bull Feynman Encounter with Mathematics 52, 200 9 [] N. Kumano-go, Feynman path integrals as analysis on path space by time slicing approximation. Bull. Sci. Math. vol. 28 (2004) 97 25. [2] D. Fujiwara and

More information

s t 1, 2,..., 10 s t a, b,..., k t s 1, 2,..., 10 1 a, b,..., k 1 s t ts 1 0 ( 2.25) ½ ¾ ½¼ x 1j = 1 x 2c = 1 x 3e = 1

s t 1, 2,..., 10 s t a, b,..., k t s 1, 2,..., 10 1 a, b,..., k 1 s t ts 1 0 ( 2.25) ½ ¾ ½¼ x 1j = 1 x 2c = 1 x 3e = 1 72 2 2 2 2.24 2 s t, 2,..., 0 s t a, b,..., k t s, 2,..., 0 a, b,..., k s t 0 ts 0 ( 2.25) 2.24 2 ½ ¾ ½¼ x j = x 2c = x 3e = x 4s = x 5g = x 6i = x 7d = x 8h = x 9f = x 0k = x ta = x tb = x ts = 9 2.26

More information

,,,,., C Java,,.,,.,., ,,.,, i

,,,,., C Java,,.,,.,., ,,.,, i 24 Development of the programming s learning tool for children be derived from maze 1130353 2013 3 1 ,,,,., C Java,,.,,.,., 1 6 1 2.,,.,, i Abstract Development of the programming s learning tool for children

More information

Abstract Gale-Shapley 2 (1) 2 (2) (1)

Abstract Gale-Shapley 2 (1) 2 (2) (1) ( ) 2011 3 Abstract Gale-Shapley 2 (1) 2 (2) (1) 1 1 1.1........................................... 1 1.2......................................... 2 2 4 2.1................................... 4 2.1.1 Gale-Shapley..........................

More information

2017 (413812)

2017 (413812) 2017 (413812) Deep Learning ( NN) 2012 Google ASIC(Application Specific Integrated Circuit: IC) 10 ASIC Deep Learning TPU(Tensor Processing Unit) NN 12 20 30 Abstract Multi-layered neural network(nn) has

More information

( 9 1 ) 1 2 1.1................................... 2 1.2................................................. 3 1.3............................................... 4 1.4...........................................

More information

Quiz 1 ID#: Name: 1. p, q, r (Let p, q and r be propositions. Determine whether the following equation holds or not by completing the truth table belo

Quiz 1 ID#: Name: 1. p, q, r (Let p, q and r be propositions. Determine whether the following equation holds or not by completing the truth table belo Quiz 1 ID#: Name: 1. p, q, r (Let p, q and r be propositions. Determine whether the following equation holds or not by completing the truth table below.) (p q) r p ( q r). p q r (p q) r p ( q r) x T T

More information

浜松医科大学紀要

浜松医科大学紀要 On the Statistical Bias Found in the Horse Racing Data (1) Akio NODA Mathematics Abstract: The purpose of the present paper is to report what type of statistical bias the author has found in the horse

More information

Steel Construction Vol. 6 No. 22(June 1999) Engineering

Steel Construction Vol. 6 No. 22(June 1999) Engineering An Experimental Study on the Shear Strength of Anchor Bolts Embedded in Concrete (Relations Between Shear Strength and Distance Mainly on Base Concrete) Hisao KAWANO Toshiaki TACHIBANA Kanshi MASUDA ABSTRACT

More information

1 1(a) MPR 1(b) MPR MPR MPR MPR MPR 2 1 MPR MPR MPR A MPR B MPR 2 MPR MPR MPR MPR MPR GPS MPR MPR MPR 3. MPR MPR 2 MPR 2 (1) (4) Zai

1 1(a) MPR 1(b) MPR MPR MPR MPR MPR 2 1 MPR MPR MPR A MPR B MPR 2 MPR MPR MPR MPR MPR GPS MPR MPR MPR 3. MPR MPR 2 MPR 2 (1) (4) Zai Popular MPR 1,a) 2,b) 2,c) GPS Most Popular Route( MPR) MPR MPR MPR MPR MPR MPR MPR Popular Popular MPR MPR Popular 1. GPS GPS GPS Google Maps *1 Zaiben [1] Most Popular Route( MPR) MPR MPR MPR 1 525 8577

More information

Microsoft Word - Win-Outlook.docx

Microsoft Word - Win-Outlook.docx Microsoft Office Outlook での設定方法 (IMAP および POP 編 ) How to set up with Microsoft Office Outlook (IMAP and POP) 0. 事前に https://office365.iii.kyushu-u.ac.jp/login からサインインし 以下の手順で自分の基本アドレスをメモしておいてください Sign

More information

149 (Newell [5]) Newell [5], [1], [1], [11] Li,Ryu, and Song [2], [11] Li,Ryu, and Song [2], [1] 1) 2) ( ) ( ) 3) T : 2 a : 3 a 1 :

149 (Newell [5]) Newell [5], [1], [1], [11] Li,Ryu, and Song [2], [11] Li,Ryu, and Song [2], [1] 1) 2) ( ) ( ) 3) T : 2 a : 3 a 1 : Transactions of the Operations Research Society of Japan Vol. 58, 215, pp. 148 165 c ( 215 1 2 ; 215 9 3 ) 1) 2) :,,,,, 1. [9] 3 12 Darroch,Newell, and Morris [1] Mcneil [3] Miller [4] Newell [5, 6], [1]

More information

1 Fig. 1 Extraction of motion,.,,, 4,,, 3., 1, 2. 2.,. CHLAC,. 2.1,. (256 ).,., CHLAC. CHLAC, HLAC. 2.3 (HLAC ) r,.,. HLAC. N. 2 HLAC Fig. 2

1 Fig. 1 Extraction of motion,.,,, 4,,, 3., 1, 2. 2.,. CHLAC,. 2.1,. (256 ).,., CHLAC. CHLAC, HLAC. 2.3 (HLAC ) r,.,. HLAC. N. 2 HLAC Fig. 2 CHLAC 1 2 3 3,. (CHLAC), 1).,.,, CHLAC,.,. Suspicious Behavior Detection based on CHLAC Method Hideaki Imanishi, 1 Toyohiro Hayashi, 2 Shuichi Enokida 3 and Toshiaki Ejima 3 We have proposed a method for

More information

第 55 回自動制御連合講演会 2012 年 11 月 17 日,18 日京都大学 1K403 ( ) Interpolation for the Gas Source Detection using the Parameter Estimation in a Sensor Network S. T

第 55 回自動制御連合講演会 2012 年 11 月 17 日,18 日京都大学 1K403 ( ) Interpolation for the Gas Source Detection using the Parameter Estimation in a Sensor Network S. T 第 55 回自動制御連合講演会 212 年 11 月 日, 日京都大学 1K43 () Interpolation for the Gas Source Detection using the Parameter Estimation in a Sensor Network S. Tokumoto, T. Namerikawa (Keio Univ. ) Abstract The purpose of

More information

操作ガイド(本体操作編)

操作ガイド(本体操作編) J QT5-0571-V03 1 ...5...10...11...11...11...12...12...15...21...21...22...25...27...28...33...37...40...47...48...54...60...64...64...68...69...70...70...71...72...73...74...75...76...77 2 ...79...79...80...81...82...83...95...98

More information

50. (km) A B C C 7 B A 0

50. (km) A B C C 7 B A 0 49... 5 A B C. (. )?.. A A B C. A 4 0 50. (km) A B C..9 7. 4.5.9. 5. 7.5.0 4..4 7. 5.5 5.0 4. 4.. 8. 7 8.8 9.8. 8 5. 5.7.7 9.4 4. 4.7 0 4. 7. 8.0 4.. 5.8.4.8 8.5. 8 9 5 C 7 B 5 8 7 4 4 A 0 0 0 4 5 7 8

More information

ScanFront300/300P セットアップガイド

ScanFront300/300P セットアップガイド libtiff Copyright (c) 1988-1996 Sam Leffler Copyright (c) 1991-1996 Silicon Graphics, Inc. Permission to use, copy, modify, distribute, and sell this software and its documentation for any purpose is hereby

More information

(1) (2) (3) (4) (5) (6) (7) (8) (9) PLC PLC LAN MASTER PLC LAN MASTER PLC LAN MASTER PLC LAN MASTER PLC LAN MASTER MASTER MASTER PLC LAN PLC LAN PLC LAN MASTER PLC LAN MASTER MASTER TERMINAL MASTER TERMINAL

More information

1 [1, 2, 3, 4, 5, 8, 9, 10, 12, 15] The Boston Public Schools system, BPS (Deferred Acceptance system, DA) (Top Trading Cycles system, TTC) cf. [13] [

1 [1, 2, 3, 4, 5, 8, 9, 10, 12, 15] The Boston Public Schools system, BPS (Deferred Acceptance system, DA) (Top Trading Cycles system, TTC) cf. [13] [ Vol.2, No.x, April 2015, pp.xx-xx ISSN xxxx-xxxx 2015 4 30 2015 5 25 253-8550 1100 Tel 0467-53-2111( ) Fax 0467-54-3734 http://www.bunkyo.ac.jp/faculty/business/ 1 [1, 2, 3, 4, 5, 8, 9, 10, 12, 15] The

More information

inkiso.dvi

inkiso.dvi Ken Urai May 19, 2004 5 27 date-event uncertainty risk 51 ordering preordering X X X (preordering) reflexivity x X x x transitivity x, y, z X x y y z x z asymmetric x y y x x = y X (ordering) completeness

More information

2001 年度 『数学基礎 IV』 講義録

2001 年度 『数学基礎 IV』 講義録 4 A 95 96 4 1 n {1, 2,,n} n n σ ( ) 1 2 n σ(1) σ(2) σ(n) σ σ 2 1 n 1 2 {1, 2,,n} n n! n S n σ, τ S n {1, 2,,n} τ σ {1, 2,,n} n τ σ σ, τ τσ σ n σ 1 n σ 1 ( σ σ ) 1 σ = σσ 1 = ι 1 2 n ι 1 2 n 4.1. 4 σ =

More information

Ruby Ruby ruby Ruby G: Ruby>ruby Ks sample1.rb G: Ruby> irb (interactive Ruby) G: Ruby>irb -Ks irb(main):001:0> print( ) 44=>

Ruby Ruby ruby Ruby G: Ruby>ruby Ks sample1.rb G: Ruby> irb (interactive Ruby) G: Ruby>irb -Ks irb(main):001:0> print( ) 44=> Ruby Ruby 200779 ruby Ruby G: Ruby>ruby Ks sample1.rb G: Ruby> irb (interactive Ruby) G: Ruby>irb -Ks irb(main):001:0> print( 2+3+4+5+6+7+8+9 ) 44 irb(main):002:0> irb irb(main):001:0> 1+2+3+4 => 10 irb(main):002:0>

More information

Tomoyuki Shirai Institute of Mathematics for Industry, Kyushu University 1 Erdös-Rényi Erdös-Rényi 1959 Erdös-Rényi [4] 2006 Linial-Meshulam [14] 2000

Tomoyuki Shirai Institute of Mathematics for Industry, Kyushu University 1 Erdös-Rényi Erdös-Rényi 1959 Erdös-Rényi [4] 2006 Linial-Meshulam [14] 2000 Tomoyuki Shirai Istitute of Mathematics for Idustry, Kyushu Uiversity Erdös-Réyi Erdös-Réyi 959 Erdös-Réyi [] 6 Liial-Meshulam [] (cf. [, 7,, 8]) Kruskal-Katoa Erdös- Réyi ( ) Liial-Meshulam ( ) [8]. Kruskal

More information

6 ( ) 1 / 53

6 ( ) 1 / 53 6 / 6 (2014 11 05 ) / 53 6 (2014 11 05 ) 1 / 53 nodeedge 2 u v u v (u, v) 6 (2014 11 05 ) 2 / 53 Twitter 6 (2014 11 05 ) 3 / 53 Facebook 6 (2014 11 05 ) 4 / 53 N N N 1,2,..., N (i, j )i j 10 i j 2(i, j

More information

137. Tenancy specific information (a) Amount of deposit paid. (insert amount of deposit paid; in the case of a joint tenancy it should be the total am

137. Tenancy specific information (a) Amount of deposit paid. (insert amount of deposit paid; in the case of a joint tenancy it should be the total am 13Fast Fair Secure PRESCRIBED INFORMATION RELATING TO TENANCY DEPOSITS* The Letting Protection Service Northern Ireland NOTE: The landlord must supply the tenant with the Prescribed Information regarding

More information

31 4 MATLAB A, B R 3 3 A = , B = mat_a, mat_b >> mat_a = [-1, -2, -3; -4, -5, -6; -7, -8, -9] mat_a =

31 4 MATLAB A, B R 3 3 A = , B = mat_a, mat_b >> mat_a = [-1, -2, -3; -4, -5, -6; -7, -8, -9] mat_a = 3 4 MATLAB 3 4. A, B R 3 3 2 3 4 5 6 7 8 9, B = mat_a, mat_b >> mat_a = [-, -2, -3; -4, -5, -6; -7, -8, -9] 9 8 7 6 5 4 3 2 mat_a = - -2-3 -4-5 -6-7 -8-9 >> mat_b = [-9, -8, -7; -6, -5, -4; -3, -2, -]

More information

2 ( ) i

2 ( ) i 25 Study on Rating System in Multi-player Games with Imperfect Information 1165069 2014 2 28 2 ( ) i ii Abstract Study on Rating System in Multi-player Games with Imperfect Information Shigehiko MORITA

More information

ScanFront 220/220P 取扱説明書

ScanFront 220/220P 取扱説明書 libtiff Copyright (c) 1988-1996 Sam Leffler Copyright (c) 1991-1996 Silicon Graphics, Inc. Permission to use, copy, modify, distribute, and sell this software and its documentation for any purpose is hereby

More information

ScanFront 220/220P セットアップガイド

ScanFront 220/220P セットアップガイド libtiff Copyright (c) 1988-1996 Sam Leffler Copyright (c) 1991-1996 Silicon Graphics, Inc. Permission to use, copy, modify, distribute, and sell this software and its documentation for any purpose is hereby

More information

SO(2)

SO(2) TOP URL http://amonphys.web.fc2.com/ 1 12 3 12.1.................................. 3 12.2.......................... 4 12.3............................. 5 12.4 SO(2).................................. 6

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

all.dvi

all.dvi 72 9 Hooke,,,. Hooke. 9.1 Hooke 1 Hooke. 1, 1 Hooke. σ, ε, Young. σ ε (9.1), Young. τ γ G τ Gγ (9.2) X 1, X 2. Poisson, Poisson ν. ν ε 22 (9.) ε 11 F F X 2 X 1 9.1: Poisson 9.1. Hooke 7 Young Poisson G

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

Pari-gp /7/5 1 Pari-gp 3 pq

Pari-gp /7/5 1 Pari-gp 3 pq Pari-gp 3 2007/7/5 1 Pari-gp 3 pq 3 2007 7 5 Pari-gp 3 2007/7/5 2 1. pq 3 2. Pari-gp 3. p p 4. p Abel 5. 6. 7. Pari-gp 3 2007/7/5 3 pq 3 Pari-gp 3 2007/7/5 4 p q 1 (mod 9) p q 3 (3, 3) Abel 3 Pari-gp 3

More information

Vol.2.indb

Vol.2.indb 74 Migration Policy Review 2010 Vol.2 75 76 Migration Policy Review 2010 Vol.2 77 78 Migration Policy Review 2010 Vol.2 79 80 Migration Policy Review 2010 Vol.2 81 82 Migration Policy Review 2010 Vol.2

More information

25 II :30 16:00 (1),. Do not open this problem booklet until the start of the examination is announced. (2) 3.. Answer the following 3 proble

25 II :30 16:00 (1),. Do not open this problem booklet until the start of the examination is announced. (2) 3.. Answer the following 3 proble 25 II 25 2 6 13:30 16:00 (1),. Do not open this problem boolet until the start of the examination is announced. (2) 3.. Answer the following 3 problems. Use the designated answer sheet for each problem.

More information

MIDI_IO.book

MIDI_IO.book MIDI I/O t Copyright This guide is copyrighted 2002 by Digidesign, a division of Avid Technology, Inc. (hereafter Digidesign ), with all rights reserved. Under copyright laws, this guide may not be duplicated

More information

IV (2)

IV (2) COMPUTATIONAL FLUID DYNAMICS (CFD) IV (2) The Analysis of Numerical Schemes (2) 11. Iterative methods for algebraic systems Reima Iwatsu, e-mail : iwatsu@cck.dendai.ac.jp Winter Semester 2007, Graduate

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

04-04 第 57 回土木計画学研究発表会 講演集 vs

04-04 第 57 回土木計画学研究発表会 講演集 vs 04-04 vs. 1 2 1 980-8579 6-6-06 E-mail: shuhei.yamaguchi.p7@dc.tohoku.ac.jp 2 980-8579 6-6-06 E-mail: akamatsu@plan.civil.tohoku.ac.jp Fujita and Ogawa(1982) Fujita and Ogawa Key Words: agglomeration economy,

More information

2

2 NSCP-W61 08545-00U60 2 3 4 5 6 7 8 9 10 11 12 1 2 13 7 3 4 8 9 5 6 10 7 14 11 15 12 13 16 17 14 15 1 5 2 3 6 4 16 17 18 19 2 1 20 1 21 2 1 2 1 22 23 1 2 3 24 1 2 1 2 3 3 25 1 2 3 4 1 2 26 3 4 27 1 1 28

More information

,,.,.,,.,.,.,.,,.,..,,,, i

,,.,.,,.,.,.,.,,.,..,,,, i 22 A person recognition using color information 1110372 2011 2 13 ,,.,.,,.,.,.,.,,.,..,,,, i Abstract A person recognition using color information Tatsumo HOJI Recently, for the purpose of collection of

More information

J No J. J

J No J. J 教育科学と教育実践 2 Science of Education and Educational Practice - A Perspective on the Controversy on the Science of Education in Post-War Japan Part Takeo TANAKA 1950 E. J. E. J. E. J. Abstract In the latter

More information

記号と準備

記号と準備 tbasic.org * 1 [2017 6 ] 1 2 1.1................................................ 2 1.2................................................ 2 1.3.............................................. 3 2 5 2.1............................................

More information

RR-US470 (RQCA1588).indd

RR-US470 (RQCA1588).indd RR-US470 Panasonic Corporation 2006 2 3 4 http://www.sense.panasonic.co.jp/ 1 2 3 ( ) ZOOM 5 6 7 8 9 10 4 2 1 3 4 2 3 1 3 11 12 1 4 2 5 3 1 2 13 14 q φ φ 1 2 3 4 3 1 2 3 4 2 3 15 16 1 2 3 [/]p/o 17 1 2

More information

No ii

No ii 2005 6 1 2 200004 103/7-2000041037-1 3 4 5 JIS JIS X 0208, 1997 o È o http://www.pref.hiroshima.jp/soumu/bunsyo/monjokan/index.htm 200004 3 6 188030489521435 6119865 1220007 2 1659361903 3118983 16 381963

More information

2

2 2011 8 6 2011 5 7 [1] 1 2 i ii iii i 3 [2] 4 5 ii 6 7 iii 8 [3] 9 10 11 cf. Abstracts in English In terms of democracy, the patience and the kindness Tohoku people have shown will be dealt with as an exception.

More information

syspro-0405.ppt

syspro-0405.ppt 3 4, 5 1 UNIX csh 2.1 bash X Window 2 grep l POSIX * more POSIX 3 UNIX. 4 first.sh #!bin/sh #first.sh #This file looks through all the files in the current #directory for the string yamada, and then prints

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

平成 19 年度 ( 第 29 回 ) 数学入門公開講座テキスト ( 京都大学数理解析研究所, 平成 19 ~8 年月 72 月日開催 30 日 ) 1 PCF (Programming language for Computable Functions) PCF adequacy adequacy

平成 19 年度 ( 第 29 回 ) 数学入門公開講座テキスト ( 京都大学数理解析研究所, 平成 19 ~8 年月 72 月日開催 30 日 ) 1 PCF (Programming language for Computable Functions) PCF adequacy adequacy 1 PCF (Programming language for Computable Functions) PCF adequacy adequacy 2 N X Y X Y f (x) f x f x y z (( f x) y) z = (( f (x))(y))(z) X Y x e X Y λx. e x x 2 + x + 1 λx. x 2 + x + 1 3 PCF 3.1 PCF PCF

More information

3. ( 1 ) Linear Congruential Generator:LCG 6) (Mersenne Twister:MT ), L 1 ( 2 ) 4 4 G (i,j) < G > < G 2 > < G > 2 g (ij) i= L j= N

3. ( 1 ) Linear Congruential Generator:LCG 6) (Mersenne Twister:MT ), L 1 ( 2 ) 4 4 G (i,j) < G > < G 2 > < G > 2 g (ij) i= L j= N RMT 1 1 1 N L Q=L/N (RMT), RMT,,,., Box-Muller, 3.,. Testing Randomness by Means of RMT Formula Xin Yang, 1 Ryota Itoi 1 and Mieko Tanaka-Yamawaki 1 Random matrix theory derives, at the limit of both dimension

More information

+ -

+ - i i C Matsushita Electric Industrial Co., Ltd.2001 -S F0901KK0 seconds ANTI-SKIP SYSTEM Portable CD player Operating Instructions -S + - + - 9 BATTERY CARRYING CASE K 3 - + 2 1 OP 2 + 3 - K K http://www.baj.or.jp

More information

kut-paper-template.dvi

kut-paper-template.dvi 26 Discrimination of abnormal breath sound by using the features of breath sound 1150313 ,,,,,,,,,,,,, i Abstract Discrimination of abnormal breath sound by using the features of breath sound SATO Ryo

More information

3 m = [n, n1, n 2,..., n r, 2n] p q = [n, n 1, n 2,..., n r ] p 2 mq 2 = ±1 1 1 6 1.1................................. 6 1.2......................... 8 1.3......................... 13 2 15 2.1.............................

More information

http://www.ike-dyn.ritsumei.ac.jp/ hyoo/wave.html 1 1, 5 3 1.1 1..................................... 3 1.2 5.1................................... 4 1.3.......................... 5 1.4 5.2, 5.3....................

More information

The Indirect Support to Faculty Advisers of die Individual Learning Support System for Underachieving Student The Indirect Support to Faculty Advisers of the Individual Learning Support System for Underachieving

More information

千葉県における温泉地の地域的展開

千葉県における温泉地の地域的展開 1) 1999 11 50 1948 23) 2 2519 9 3) 2006 4) 151 47 37 1.2 l 40 3.6 15 240 21 9.2 l 7. 210 1972 5) 1.9 l 5 1 0.2 l 6 1 1972 1.9 0.4 210 40-17- 292006 34 6 l/min.42 6) 2006 1 1 2006 42 60% 5060 4050 3040

More information

昭和恐慌期における長野県下農業・農村と産業組合の展開過程

昭和恐慌期における長野県下農業・農村と産業組合の展開過程 No. 3, 169-180 (2002) The Family in Modern Japan: its Past, Present and Future An Essay at Restoring Love as the Basis of Family Ties YAMANE Naoko Nihon University, Graduate School of Social and Cultural

More information

P072-076.indd

P072-076.indd 3 STEP0 STEP1 STEP2 STEP3 STEP4 072 3STEP4 STEP3 STEP2 STEP1 STEP0 073 3 STEP0 STEP1 STEP2 STEP3 STEP4 074 3STEP4 STEP3 STEP2 STEP1 STEP0 075 3 STEP0 STEP1 STEP2 STEP3 STEP4 076 3STEP4 STEP3 STEP2 STEP1

More information

STEP1 STEP3 STEP2 STEP4 STEP6 STEP5 STEP7 10,000,000 2,060 38 0 0 0 1978 4 1 2015 9 30 15,000,000 2,060 38 0 0 0 197941 2016930 10,000,000 2,060 38 0 0 0 197941 2016930 3 000 000 0 0 0 600 15

More information

1

1 1 2 3 4 5 6 7 8 9 0 1 2 6 3 1 2 3 4 5 6 7 8 9 0 5 4 STEP 02 STEP 01 STEP 03 STEP 04 1F 1F 2F 2F 2F 1F 1 2 3 4 5 http://smarthouse-center.org/sdk/ http://smarthouse-center.org/inquiries/ http://sh-center.org/

More information