. p.1/34

Size: px
Start display at page:

Download ". p.1/34"

Transcription

1 . p.1/34

2 (Optimization) (Mathematical Programming),,. p.2/34

3 p.3/34

4 p.4/34

5 4x + 2y 6, 2x + y 6, x 0, y 0 x, yx + yx, y x + y x + 2y 6, 2x + y 6, x 0, y 0. 2x, y x + y 4x 1, x 2, x 3, x 4 3x 1 + 4x 2 + 2x 3 2x 4 2x 1 + 3x 2 + 4x 3 + x 4 10, 3x 1 + 2x 2 x 3 + 6x 4 8, x 1, x 2, x 3, x 4 :(0). 100, 1000, p.5/34

6 : f(x) (or),: x = (x 1, x 2,..., x n ) S. x j, S : f : x = (x 1,..., x n ), S x f(x) n = 2 S =x, f(x) : f(x) : x* f(x*) f(x) :x (). p.6/34

7 : f(x) (or),: x = (x 1, x 2,..., x n ) S. x j, S : f : x = (x 1,..., x n ), 1.2 (a) (b) (c) =. p.7/34

8 : f(x) (or),: x = (x 1, x 2,..., x n ) S. x j, S : f : x = (x 1,..., x n ), 1.3 S { } S = x R n g j (x) 0 (j = 1, 2,..., p), : h k (x) = 0 (k = 1, 2,..., q), g j h k f,,,. p.8/34

9 (). p.9/34

10 3x, y, z 1, () 3x + 4y + 2z 2x + 3y + 4z 10, 3x + 2y z 8, x 0, y 0, z 0., (), 3x + 4y + 2z 2x + 3y + 4z 10, 3x + 2y z 8, x, y, z :. x + 3x 2 4xy 2y + y 2 + 2z 2 + 4z 2x + 3xy 5y + 4z 2 10, 3x + 2y 2 z 8, x 2 + 3y 2 + 5z p.10/34

11 3x, y, z 2 (), 3x + 4 cos y + 2 log z 2x + 3y 2 + 4z 10, 3e x + 2y z 8, x 0, y 0, z 0.. p.11/34

12 p.12/34

13 LSI. p.13/34

14 2.1 = = mn a ij :j1i c j :jb i :i x j :j : : c 1 x 1 + c 2 x c n x n () a i1 x 1 + a i2 x a in x n b i (i = 1, 2,..., m), x j 0 (j = 1, 2,..., n). =. p.14/34

15 a i :i(/) (i = 1, 2,..., 29) b j :j(/) (j = 1, 2,..., 283) c ij :ij(/) f i :i+(/) S {10,..., 29}: x ij :i j(/) a i f 10 x ij c b ij j (0-1 ) y i = { 1 0. p.15/34

16 p.16/34

17 p.17/34

18 . p.18/34

19 = 32. p.19/34

20 1, x 1 = (x 1 1, x1 2 ), x4 = (x 4 1, x4 2 ) = x i x j = x i x j x 2, x 3, x 4, x 1, x 2, x 3, x 4 x 1 x 1 x 4 = (x 1 1 x 4 1) 2 + (x 1 2 x 4 2) 2 = = 5 (). x 1 1, x 1 2. p.20/34

21 1 = x i x j = x i x j x 2, x 3, x 4, x 1, x 2, x 3, x 4 x 1 n + m+ m. p.21/34

22 x 1 x 2 x 3 a 4 = a 5 = a 6 = a 7 =??? (0,0) (0,1) (1,0) (1,1) x 1 x 2 2 = (x 1 1 x 2 1) 2 + (x 1 2 x 2 2) 2 = x 1 a 4 2 = (x 1 1) 2 + (x 1 2) 2 = p.22/34

23 x 1 x 2 x 3 a 4 = a 5 = a 6 = a 7 =??? (0,0) (0,1) (1,0) (1,1) x 1 x 2 2 = (x 1 1 x 2 1) 2 + (x 1 2 x 2 2) 2 = , x 1 x 3 2 = , x 2 x 3 2 = , x 1 a 4 2 = , x 2 a 5 2 = , x 2 a 7 2 = , x 3 a 6 2 = x 1, x 2, x 3 x 1 x x 3 a p.23/34

24 % : : : :30. p.24/34

25 % : : : :60. p.25/34

26 p.26/34

27 (Linear Program, LP) : 4x 1 + 7x 2 : 5x 1 + 3x 2 21 (1) x 1 + 3x 2 9 (2) x 1 0, x 2 0. nm n = 2, m = 4. (n, m 10, 000). p.27/34

28 (Linear Program, LP) : 4x 1 + 7x 2 : 5x 1 + 3x 2 21 (1) x 1 + 3x 2 9 (2) x 1 0, x x 2 (3,2) 0 0 S (1) (2) 4.2 (a) S= (b) = Dantzig, 1947) x 1. p.28/34

29 3.2 (1947) (1984) S ). p.29/34

30 3.3 (nx 1,..., x n m) : c 1 x c n x n : a i1 x a in x n b i or = b i (i = 1,..., m). n: m:, cont1 40, , , 991 rail4284 1, 092, 610 4, , 372, 358 spal , , , 167, 908 a ij CPLEX xpress-mp CPLEX xpress-mp cont ,389 2,174 2,229 rail ,235 8,928 spal004 4,669 > 200,000. p.30/34

31 p.31/34

32 globallib : 6.3x 5 x x x 3 + x x 6 : 0.820x 2 + x x 6 = 0, 0.98x 4 x 7 (0.01x 5 x 10 + x 4 ) = 0, x 1 x 11 3x 8 = 1.33, x 2 x x 3 + x 6 = 0, x 10 x x 11 = 35.82, x 5 x 12 x 2 ( x x 2 9) = 0, x 8 x x 9 ( x 9 ) 0.325x 7 = 0.574, lbd i x i ubd i (i = 1, 2,..., 14). x 1,..., x n n = 14. x 1,..., x n Sum of Squares of Polynomials). p.32/34

33 globallib : 6.3x 5 x x x 3 + x x 6 : 0.820x 2 + x x 6 = 0, 0.98x 4 x 7 (0.01x 5 x 10 + x 4 ) = 0, x 1 x 11 3x 8 = 1.33, x 2 x x 3 + x 6 = 0, x 10 x x 11 = 35.82, x 5 x 12 x 2 ( x x 2 9) = 0, x 8 x x 9 ( x 9 ) 0.325x 7 = 0.574, lbd i x i ubd i (i = 1, 2,..., 14). 14 ɛ obj ɛ feas ɛ obj ɛ feas 5.6e e ɛ obj = max{1, } () ɛ feas =. p.33/34

34 =() =. p.34/34

AHPを用いた大相撲の新しい番付編成

AHPを用いた大相撲の新しい番付編成 5304050 2008/2/15 1 2008/2/15 2 42 2008/2/15 3 2008/2/15 4 195 2008/2/15 5 2008/2/15 6 i j ij >1 ij ij1/>1 i j i 1 ji 1/ j ij 2008/2/15 7 1 =2.01/=0.5 =1.51/=0.67 2008/2/15 8 1 2008/2/15 9 () u ) i i i

More information

( ) () () ( ) () () () ()

( ) () () ( ) () () () () 5 1! (Linear Programming, LP) LP OR LP 1.1 1.1.1 1. 2. 3. 4. 4 5. 1000 4 1.1? 1.2 1 1 http://allrecipes.com/ 6 1 1.1 ( ) () 1 0.5 1 0.75 200 () 1.5 1 0.5 1 50 ( ) 2 2 1 30 () 2.25 0.5 2 2.25 30 () 2 100

More information

最適化手法 第1回 [3mm] 整数計画法 (1) [3mm]

最適化手法 第1回 [3mm] 整数計画法 (1) [3mm] 1 (1) & 2014 4 9 ( ) (1) 2014 4 9 1 / 39 2013 ( ) (1) 2014 4 9 2 / 39 OR 1 OR 2 OR Excel ( ) (1) 2014 4 9 3 / 39 1 (4 9 ) 2 (4 16 ) 3 (4 23 ) 4 (4 30 ) 5 (5 7 ) 6 (5 14 ) 7 1 (5 21 ) ( ) (1) 2014 4 9 4

More information

OR 2 Excel 2 3.. 4. OK. 1a: Excel2007 Office. Excel2003 1.. 1b. 2.. 3. OK. 2.,,. ツール アドイン 1b: Excel2003 :,.,.,.,,,.,,. 1. Excel2003.

OR 2 Excel 2 3.. 4. OK. 1a: Excel2007 Office. Excel2003 1.. 1b. 2.. 3. OK. 2.,,. ツール アドイン 1b: Excel2003 :,.,.,.,,,.,,. 1. Excel2003. OR 2 Excel 1 2 2.1 Excel.,. 2.2, x mathematical programming optimization problem, OR 1., 1 : f(x) h i (x) = 0, i = 1,..., m, g j (x) 0, j = 1,..., l, f(x) h i (x) = 0, i = 1,..., m, g j (x) 0, j = 1,...,

More information

FX ) 2

FX ) 2 (FX) 1 1 2009 12 12 13 2009 1 FX ) 2 1 (FX) 2 1 2 1 2 3 2010 8 FX 1998 1 FX FX 4 1 1 (FX) () () 1998 4 1 100 120 1 100 120 120 100 20 FX 100 100 100 1 100 100 100 1 100 1 100 100 1 100 101 101 100 100

More information

xyr x y r x y r u u

xyr x y r x y r u u xyr x y r x y r u u y a b u a b a b c d e f g u a b c d e g u u e e f yx a b a b a b c a b c a b a b c a b a b c a b c a b c a u xy a b u a b c d a b c d u ar ar a xy u a b c a b c a b p a b a b c a

More information

untitled

untitled 20 7 1 22 7 1 1 2 3 7 8 9 10 11 13 14 15 17 18 19 21 22 - 1 - - 2 - - 3 - - 4 - 50 200 50 200-5 - 50 200 50 200 50 200 - 6 - - 7 - () - 8 - (XY) - 9 - 112-10 - - 11 - - 12 - - 13 - - 14 - - 15 - - 16 -

More information

untitled

untitled 19 1 19 19 3 8 1 19 1 61 2 479 1965 64 1237 148 1272 58 183 X 1 X 2 12 2 15 A B 5 18 B 29 X 1 12 10 31 A 1 58 Y B 14 1 25 3 31 1 5 5 15 Y B 1 232 Y B 1 4235 14 11 8 5350 2409 X 1 15 10 10 B Y Y 2 X 1 X

More information

a n a n ( ) (1) a m a n = a m+n (2) (a m ) n = a mn (3) (ab) n = a n b n (4) a m a n = a m n ( m > n ) m n 4 ( ) 552

a n a n ( ) (1) a m a n = a m+n (2) (a m ) n = a mn (3) (ab) n = a n b n (4) a m a n = a m n ( m > n ) m n 4 ( ) 552 3 3.0 a n a n ( ) () a m a n = a m+n () (a m ) n = a mn (3) (ab) n = a n b n (4) a m a n = a m n ( m > n ) m n 4 ( ) 55 3. (n ) a n n a n a n 3 4 = 8 8 3 ( 3) 4 = 8 3 8 ( ) ( ) 3 = 8 8 ( ) 3 n n 4 n n

More information

f(x) x S (optimal solution) f(x ) (optimal value) f(x) (1) 3 GLPK glpsol -m -d -m glpsol -h -m -d -o -y --simplex ( ) --interior --min --max --check -

f(x) x S (optimal solution) f(x ) (optimal value) f(x) (1) 3 GLPK glpsol -m -d -m glpsol -h -m -d -o -y --simplex ( ) --interior --min --max --check - GLPK by GLPK http://mukun mmg.at.infoseek.co.jp/mmg/glpk/ 17 7 5 : update 1 GLPK GNU Linear Programming Kit GNU LP/MIP ILOG AMPL(A Mathematical Programming Language) 1. 2. 3. 2 (optimization problem) X

More information

( ) ? () 1.1 ( 3 ) j x j 10 j 1 10 j = 1,..., 10 x 1 + x x 10 =

( ) ? () 1.1 ( 3 ) j x j 10 j 1 10 j = 1,..., 10 x 1 + x x 10 = 5 1! (Linear Programming, LP) LP OR LP 1.1 1.1.1 1. 2. 3. 4. 5. ( ) ( ) 1.1 6 1 1.1 ( ) 1 110 2 98 3 85 4 90 5 73 6 62 7 92 8 88 9 79 10 75 1.1.2 4? 900 40 80 120 () 1.1 ( 3 ) j x j 10 j 1 10 j = 1,...,

More information

応力とひずみ.ppt

応力とひずみ.ppt in yukawa@numse.nagoya-u.ac.jp 2 3 4 5 x 2 6 Continuum) 7 8 9 F F 10 F L L F L 1 L F L F L F 11 F L F F L F L L L 1 L 2 12 F L F! A A! S! = F S 13 F L L F F n = F " cos# F t = F " sin# S $ = S cos# S S

More information

A

A A05-132 2010 2 11 1 1 3 1.1.......................................... 3 1.2..................................... 3 1.3..................................... 3 2 4 2.1............................... 4 2.2

More information

untitled

untitled 186 17 100160250 1 10.1 55 2 18.5 6.9 100 38 17 3.2 17 8.4 45 3.9 53 1.6 22 7.3 100 2.3 31 3.4 47 OR OR 3 1.20.76 63.4 2.16 4 38,937101,118 17 17 17 5 1,765 1,424 854 794 108 839 628 173 389 339 57 6 18613

More information

untitled

untitled 1. 3 14 2. 1 12 9 7.1 3. 5 10 17 8 5500 4. 6 11 5. 1 12 101977 1 21 45.31982.9.4 79.71996 / 1997 89.21983 41.01902 6. 7 5 10 2004 30 16.8 37.5 3.3 2004 10.0 7.5 37.0 2004 8. 2 7 9. 6 11 46 37 25 55 10.

More information

() x + y + y + x dy dx = 0 () dy + xy = x dx y + x y ( 5) ( s55906) 0.7. (). 5 (). ( 6) ( s6590) 0.8 m n. 0.9 n n A. ( 6) ( s6590) f A (λ) = det(a λi)

() x + y + y + x dy dx = 0 () dy + xy = x dx y + x y ( 5) ( s55906) 0.7. (). 5 (). ( 6) ( s6590) 0.8 m n. 0.9 n n A. ( 6) ( s6590) f A (λ) = det(a λi) 0. A A = 4 IC () det A () A () x + y + z = x y z X Y Z = A x y z ( 5) ( s5590) 0. a + b + c b c () a a + b + c c a b a + b + c 0 a b c () a 0 c b b c 0 a c b a 0 0. A A = 7 5 4 5 0 ( 5) ( s5590) () A ()

More information

70の法則

70の法則 70 70 1 / 27 70 1 2 3 4 5 6 2 / 27 70 70 70 X r % = 70 2 r r r 10 72 70 72 70 : 1, 2, 5, 7, 10, 14, 35, 70 72 : 1, 2, 3, 4, 6, 8, 9, 12, 18, 24, 36, 72 3 / 27 r = 10 70 r = 10 70 1 : X, X 10 = ( X + X

More information

「産業上利用することができる発明」の審査の運用指針(案)

「産業上利用することができる発明」の審査の運用指針(案) 1 1.... 2 1.1... 2 2.... 4 2.1... 4 3.... 6 4.... 6 1 1 29 1 29 1 1 1. 2 1 1.1 (1) (2) (3) 1 (4) 2 4 1 2 2 3 4 31 12 5 7 2.2 (5) ( a ) ( b ) 1 3 2 ( c ) (6) 2. 2.1 2.1 (1) 4 ( i ) ( ii ) ( iii ) ( iv)

More information

106 4 4.1 1 25.1 25.4 20.4 17.9 21.2 23.1 26.2 1 24 12 14 18 36 42 24 10 5 15 120 30 15 20 10 25 35 20 18 30 12 4.1 7 min. z = 602.5x 1 + 305.0x 2 + 2

106 4 4.1 1 25.1 25.4 20.4 17.9 21.2 23.1 26.2 1 24 12 14 18 36 42 24 10 5 15 120 30 15 20 10 25 35 20 18 30 12 4.1 7 min. z = 602.5x 1 + 305.0x 2 + 2 105 4 0 1? 1 LP 0 1 4.1 4.1.1 (intger programming problem) 1 0.5 x 1 = 447.7 448 / / 2 1.1.2 1. 2. 1000 3. 40 4. 20 106 4 4.1 1 25.1 25.4 20.4 17.9 21.2 23.1 26.2 1 24 12 14 18 36 42 24 10 5 15 120 30

More information

2009 IA 5 I 22, 23, 24, 25, 26, (1) Arcsin 1 ( 2 (4) Arccos 1 ) 2 3 (2) Arcsin( 1) (3) Arccos 2 (5) Arctan 1 (6) Arctan ( 3 ) 3 2. n (1) ta

2009 IA 5 I 22, 23, 24, 25, 26, (1) Arcsin 1 ( 2 (4) Arccos 1 ) 2 3 (2) Arcsin( 1) (3) Arccos 2 (5) Arctan 1 (6) Arctan ( 3 ) 3 2. n (1) ta 009 IA 5 I, 3, 4, 5, 6, 7 6 3. () Arcsin ( (4) Arccos ) 3 () Arcsin( ) (3) Arccos (5) Arctan (6) Arctan ( 3 ) 3. n () tan x (nπ π/, nπ + π/) f n (x) f n (x) fn (x) Arctan x () sin x [nπ π/, nπ +π/] g n

More information

応用数学III-4.ppt

応用数学III-4.ppt III f x ( ) = 1 f x ( ) = P( X = x) = f ( x) = P( X = x) =! x ( ) b! a, X! U a,b f ( x) =! " e #!x, X! Ex (!) n! ( n! x)!x! " x 1! " x! e"!, X! Po! ( ) n! x, X! B( n;" ) ( ) ! xf ( x) = = n n!! ( n

More information

1 1 1 1 1 1 2 f z 2 C 1, C 2 f 2 C 1, C 2 f(c 2 ) C 2 f(c 1 ) z C 1 f f(z) xy uv ( u v ) = ( a b c d ) ( x y ) + ( p q ) (p + b, q + d) 1 (p + a, q + c) 1 (p, q) 1 1 (b, d) (a, c) 2 3 2 3 a = d, c = b

More information

I, II 1, 2 ɛ-δ 100 A = A 4 : 6 = max{ A, } A A 10

I, II 1, 2 ɛ-δ 100 A = A 4 : 6 = max{ A, } A A 10 1 2007.4.13. A 3-312 tel: 092-726-4774, e-mail: hara@math.kyushu-u.ac.jp, http://www.math.kyushu-u.ac.jp/ hara/lectures/lectures-j.html Office hours: B A I ɛ-δ ɛ-δ 1. 2. A 0. 1. 1. 2. 3. 2. ɛ-δ 1. ɛ-n

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

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

1 n A a 11 a 1n A =.. a m1 a mn Ax = λx (1) x n λ (eigenvalue problem) x = 0 ( x 0 ) λ A ( ) λ Ax = λx x Ax = λx y T A = λy T x Ax = λx cx ( 1) 1.1 Th

1 n A a 11 a 1n A =.. a m1 a mn Ax = λx (1) x n λ (eigenvalue problem) x = 0 ( x 0 ) λ A ( ) λ Ax = λx x Ax = λx y T A = λy T x Ax = λx cx ( 1) 1.1 Th 1 n A a 11 a 1n A = a m1 a mn Ax = λx (1) x n λ (eigenvalue problem) x = ( x ) λ A ( ) λ Ax = λx x Ax = λx y T A = λy T x Ax = λx cx ( 1) 11 Th9-1 Ax = λx λe n A = λ a 11 a 12 a 1n a 21 λ a 22 a n1 a n2

More information

BayesfiI‡É“ÅfiK‡È−w‘K‡Ì‡½‡ß‡ÌChow-Liu…A…‰…S…−…Y…•

BayesfiI‡É“ÅfiK‡È−w‘K‡Ì‡½‡ß‡ÌChow-Liu…A…‰…S…−…Y…• 1 / 21 Kruscal V : w i,j R: w i,j = w j,i i j Kruscal (w i,j 0 ) 1 E {{i, j} i, j V, i i} 2 E {} 3 while(e = ϕ) for w i,j {i, j} E 1 E E\{i, j} 2 G = (V, E {i, j}) = E E {i, j} G {i,j} E w i,j 2 / 21 w

More information

.1 A cos 2π 3 sin 2π 3 sin 2π 3 cos 2π 3 T ra 2 deta T ra 2 deta T ra 2 deta a + d 2 ad bc a 2 + d 2 + ad + bc A 3 a b a 2 + bc ba + d c d ca + d bc +

.1 A cos 2π 3 sin 2π 3 sin 2π 3 cos 2π 3 T ra 2 deta T ra 2 deta T ra 2 deta a + d 2 ad bc a 2 + d 2 + ad + bc A 3 a b a 2 + bc ba + d c d ca + d bc + .1 n.1 1 A T ra A A a b c d A 2 a b a b c d c d a 2 + bc ab + bd ac + cd bc + d 2 a 2 + bc ba + d ca + d bc + d 2 A a + d b c T ra A T ra A 2 A 2 A A 2 A 2 A n A A n cos 2π sin 2π n n A k sin 2π cos 2π

More information

2.2 h h l L h L = l cot h (1) (1) L l L l l = L tan h (2) (2) L l 2 l 3 h 2.3 a h a h (a, h)

2.2 h h l L h L = l cot h (1) (1) L l L l l = L tan h (2) (2) L l 2 l 3 h 2.3 a h a h (a, h) 1 16 10 5 1 2 2.1 a a a 1 1 1 2.2 h h l L h L = l cot h (1) (1) L l L l l = L tan h (2) (2) L l 2 l 3 h 2.3 a h a h (a, h) 4 2 3 4 2 5 2.4 x y (x,y) l a x = l cot h cos a, (3) y = l cot h sin a (4) h a

More information

Chapter9 9 LDPC sum-product LDPC 9.1 ( ) 9.2 c 1, c 2, {0, 1, } SUM, PROD : {0, 1, } {0, 1, } SUM(c 1, c 2,, c n ) := { c1 + + c n (c n0 (1 n

Chapter9 9 LDPC sum-product LDPC 9.1 ( ) 9.2 c 1, c 2, {0, 1, } SUM, PROD : {0, 1, } {0, 1, } SUM(c 1, c 2,, c n ) := { c1 + + c n (c n0 (1 n 9 LDPC sum-product 9.1 9.2 LDPC 9.1 ( ) 9.2 c 1, c 2, {0, 1, } SUM, PROD : {0, 1, } {0, 1, } SUM(c 1, c 2,, c n ) := { c1 + + c n (c n0 (1 n 0 n)) ( ) 0 (N(0 c) > N(1 c)) PROD(c 1, c 2,, c n ) := 1 (N(0

More information

3 65 1 4 5 67 1 2 5 5 3 6 68 23 69 2 6 8m 10m 1. 2. 3. 70 66 600km 11 3 16 21 3 0 3m 2m 0 5m 71 11 3 17 0 5 0 0 72 73 74 75 3 76 77 4 78 79 5 80 81 82 83 2 83 . 84 6 a b c d e f g a b c 3 85 16 86 87 7

More information

10 1 1 (1) (2) (3) 3 3 1 3 1 3 (4) 2 32 2 (1) 1 1

10 1 1 (1) (2) (3) 3 3 1 3 1 3 (4) 2 32 2 (1) 1 1 10 10 1 1 (1) (2) (3) 3 3 1 3 1 3 (4) 2 32 2 (1) 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 (2) 1 (3) JI S JI S JI S JI S 25 175 J AS 3 (1) 3 70 (2) (3) 100 4 (1)69 (2) (3) (4) (5) (6) (7) (8)70 (9) (10)2 (11)

More information

情報理論 第5回 情報量とエントロピー

情報理論  第5回 情報量とエントロピー 5 () ( ) ( ) ( ) p(a) a I(a) p(a) p(a) I(a) p(a) I(a) (2) (self information) p(a) = I(a) = 0 I(a) = 0 I(a) a I(a) = log 2 p(a) = log 2 p(a) bit 2 (log 2 ) (3) I(a) 7 6 5 4 3 2 0 0.5 p(a) p(a) = /2 I(a)

More information

II Time-stamp: <05/09/30 17:14:06 waki> ii

II Time-stamp: <05/09/30 17:14:06 waki> ii II waki@cc.hirosaki-u.ac.jp 18 1 30 II Time-stamp: ii 1 1 1.1.................................................. 1 1.2................................................... 3 1.3..................................................

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

A A = a 41 a 42 a 43 a 44 A (7) 1 (3) A = M 12 = = a 41 (8) a 41 a 43 a 44 (3) n n A, B a i AB = A B ii aa

A A = a 41 a 42 a 43 a 44 A (7) 1 (3) A = M 12 = = a 41 (8) a 41 a 43 a 44 (3) n n A, B a i AB = A B ii aa 1 2 21 2 2 [ ] a 11 a 12 A = a 21 a 22 (1) A = a 11 a 22 a 12 a 21 (2) 3 3 n n A A = n ( 1) i+j a ij M ij i =1 n (3) j=1 M ij A i j (n 1) (n 1) 2-1 3 3 A A = a 11 a 12 a 13 a 21 a 22 a 23 a 31 a 32 a 33

More information

[FX8/FX8C]シリーズカタログ

[FX8/FX8C]シリーズカタログ 2018.6 FX8-120 P - SV 192 1 2 3 1 2 3 4 5 6 6 B 4 5 6 B 0.6±0.1 B +0.05 _0.2 C +0.05 _0.2 D±0.2 6.5 _0.3 0 5.1±0.3 2.45±0.25 (0.4) (0.75) (Ø0.9) t0.2±0.03 w0.25±0.03 (Ø0.6) 2-(C0.2) (1.5) E±0.1 FX8-60P-SV(**)

More information

May Copyright 2016 HIROSE ELECTRIC CO., LTD. All Rights Reserved w

May Copyright 2016 HIROSE ELECTRIC CO., LTD. All Rights Reserved w 2014.9w FX8-120 P - SV 192 1 2 3 1 2 3 4 5 6 6 B 4 5 6 B 0.6±0.1 B +0.05 _0.2 C +0.05 _0.2 D±0.2 6.5 _0.3 0 5.1±0.3 2.45±0.25 (0.4) (0.75) (Ø0.9) t0.2±0.03 w0.25±0.03 (Ø0.6) 2-(C0.2) (1.5) E±0.1 FX8-60P-SV(92)

More information

2 1/2 1/4 x 1 x 2 x 1, x 2 9 3x 1 + 2x 2 9 (1.1) 1/3 RDA 1 15 x /4 RDA 1 6 x /6 1 x 1 3 x 2 15 x (1.2) (1.3) (1.4) 1 2 (1.5) x 1

2 1/2 1/4 x 1 x 2 x 1, x 2 9 3x 1 + 2x 2 9 (1.1) 1/3 RDA 1 15 x /4 RDA 1 6 x /6 1 x 1 3 x 2 15 x (1.2) (1.3) (1.4) 1 2 (1.5) x 1 1 1 [1] 1.1 1.1. TS 9 1/3 RDA 1/4 RDA 1 1/2 1/4 50 65 3 2 1/15 RDA 2/15 RDA 1/6 RDA 1 1/6 1 1960 2 1/2 1/4 x 1 x 2 x 1, x 2 9 3x 1 + 2x 2 9 (1.1) 1/3 RDA 1 15 x 1 + 2 1/4 RDA 1 6 x 1 1 4 1 1/6 1 x 1 3

More information

弾性定数の対称性について

弾性定数の対称性について () by T. oyama () ij C ij = () () C, C, C () ij ji ij ijlk ij ij () C C C C C C * C C C C C * * C C C C = * * * C C C * * * * C C * * * * * C () * P (,, ) P (,, ) lij = () P (,, ) P(,, ) (,, ) P (, 00,

More information

第3章 非線形計画法の基礎

第3章 非線形計画法の基礎 3 February 25, 2009 1 Armijo Wolfe Newton 2 Newton Lagrange Newton 2 SQP 2 1 2.1 ( ) S R n (n N) f (x) : R n x f R x S f (x ) = min x S R n f (x) (nonlinear programming) x 0 S k = 0, 1, 2, h k R n ɛ k

More information

1.2 y + P (x)y + Q(x)y = 0 (1) y 1 (x), y 2 (x) y 1 (x), y 2 (x) (1) y(x) c 1, c 2 y(x) = c 1 y 1 (x) + c 2 y 2 (x) 3 y 1 (x) y 1 (x) e R P (x)dx y 2

1.2 y + P (x)y + Q(x)y = 0 (1) y 1 (x), y 2 (x) y 1 (x), y 2 (x) (1) y(x) c 1, c 2 y(x) = c 1 y 1 (x) + c 2 y 2 (x) 3 y 1 (x) y 1 (x) e R P (x)dx y 2 1 1.1 R(x) = 0 y + P (x)y + Q(x)y = R(x)...(1) y + P (x)y + Q(x)y = 0...(2) 1 2 u(x) v(x) c 1 u(x)+ c 2 v(x) = 0 c 1 = c 2 = 0 c 1 = c 2 = 0 2 0 2 u(x) v(x) u(x) u (x) W (u, v)(x) = v(x) v (x) 0 1 1.2

More information

k m m d2 x i dt 2 = f i = kx i (i = 1, 2, 3 or x, y, z) f i σ ij x i e ij = 2.1 Hooke s law and elastic constants (a) x i (2.1) k m σ A σ σ σ σ f i x

k m m d2 x i dt 2 = f i = kx i (i = 1, 2, 3 or x, y, z) f i σ ij x i e ij = 2.1 Hooke s law and elastic constants (a) x i (2.1) k m σ A σ σ σ σ f i x k m m d2 x i dt 2 = f i = kx i (i = 1, 2, 3 or x, y, z) f i ij x i e ij = 2.1 Hooke s law and elastic constants (a) x i (2.1) k m A f i x i B e e e e 0 e* e e (2.1) e (b) A e = 0 B = 0 (c) (2.1) (d) e

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

M3 x y f(x, y) (= x) (= y) x + y f(x, y) = x + y + *. f(x, y) π y f(x, y) x f(x + x, y) f(x, y) lim x x () f(x,y) x 3 -

M3 x y f(x, y) (= x) (= y) x + y f(x, y) = x + y + *. f(x, y) π y f(x, y) x f(x + x, y) f(x, y) lim x x () f(x,y) x 3 - M3............................................................................................ 3.3................................................... 3 6........................................... 6..........................................

More information

OR#5.key

OR#5.key オペレーションズ リサーチ1 Operations Research 前学期 月曜 3限(3:00-4:30) 8 整数計画モデル Integer Programming 経営A棟106教室 山本芳嗣 筑波大学 大学院 システム情報工学研究科 整数計画問題 2 凸包 最小の凸集合 線形計画問題 変数の整数条件 ctx Ax b x 0 xj は整数 IP LP 3 4 Bx d!!!!!? P NP

More information

7 27 7.1........................................ 27 7.2.......................................... 28 1 ( a 3 = 3 = 3 a a > 0(a a a a < 0(a a a -1 1 6

7 27 7.1........................................ 27 7.2.......................................... 28 1 ( a 3 = 3 = 3 a a > 0(a a a a < 0(a a a -1 1 6 26 11 5 1 ( 2 2 2 3 5 3.1...................................... 5 3.2....................................... 5 3.3....................................... 6 3.4....................................... 7

More information

untitled

untitled 20010916 22;1017;23;20020108;15;20; 1 N = {1, 2, } Z + = {0, 1, 2, } Z = {0, ±1, ±2, } Q = { p p Z, q N} R = { lim a q n n a n Q, n N; sup a n < } R + = {x R x 0} n = {a + b 1 a, b R} u, v 1 R 2 2 R 3

More information

1 (1) ( i ) 60 (ii) 75 (iii) 315 (2) π ( i ) (ii) π (iii) 7 12 π ( (3) r, AOB = θ 0 < θ < π ) OAB A 2 OB P ( AB ) < ( AP ) (4) 0 < θ < π 2 sin θ

1 (1) ( i ) 60 (ii) 75 (iii) 315 (2) π ( i ) (ii) π (iii) 7 12 π ( (3) r, AOB = θ 0 < θ < π ) OAB A 2 OB P ( AB ) < ( AP ) (4) 0 < θ < π 2 sin θ 1 (1) ( i ) 60 (ii) 75 (iii) 15 () ( i ) (ii) 4 (iii) 7 1 ( () r, AOB = θ 0 < θ < ) OAB A OB P ( AB ) < ( AP ) (4) 0 < θ < sin θ < θ < tan θ 0 x, 0 y (1) sin x = sin y (x, y) () cos x cos y (x, y) 1 c

More information

Chap9.dvi

Chap9.dvi .,. f(),, f(),,.,. () lim 2 +3 2 9 (2) lim 3 3 2 9 (4) lim ( ) 2 3 +3 (5) lim 2 9 (6) lim + (7) lim (8) lim (9) lim (0) lim 2 3 + 3 9 2 2 +3 () lim sin 2 sin 2 (2) lim +3 () lim 2 2 9 = 5 5 = 3 (2) lim

More information

: , 2.0, 3.0, 2.0, (%) ( 2.

: , 2.0, 3.0, 2.0, (%) ( 2. 2017 1 2 1.1...................................... 2 1.2......................................... 4 1.3........................................... 10 1.4................................. 14 1.5..........................................

More information

I, II 1, A = A 4 : 6 = max{ A, } A A 10 10%

I, II 1, A = A 4 : 6 = max{ A, } A A 10 10% 1 2006.4.17. A 3-312 tel: 092-726-4774, e-mail: hara@math.kyushu-u.ac.jp, http://www.math.kyushu-u.ac.jp/ hara/lectures/lectures-j.html Office hours: B A I ɛ-δ ɛ-δ 1. 2. A 1. 1. 2. 3. 4. 5. 2. ɛ-δ 1. ɛ-n

More information

i

i i 3 4 4 7 5 6 3 ( ).. () 3 () (3) (4) /. 3. 4/3 7. /e 8. a > a, a = /, > a >. () a >, a =, > a > () a > b, a = b, a < b. c c n a n + b n + c n 3c n..... () /3 () + (3) / (4) /4 (5) m > n, a b >, m > n,

More information

CALCULUS II (Hiroshi SUZUKI ) f(x, y) A(a, b) 1. P (x, y) A(a, b) A(a, b) f(x, y) c f(x, y) A(a, b) c f(x, y) c f(x, y) c (x a, y b)

CALCULUS II (Hiroshi SUZUKI ) f(x, y) A(a, b) 1. P (x, y) A(a, b) A(a, b) f(x, y) c f(x, y) A(a, b) c f(x, y) c f(x, y) c (x a, y b) CALCULUS II (Hiroshi SUZUKI ) 16 1 1 1.1 1.1 f(x, y) A(a, b) 1. P (x, y) A(a, b) A(a, b) f(x, y) c f(x, y) A(a, b) c f(x, y) c f(x, y) c (x a, y b) lim f(x, y) = lim f(x, y) = lim f(x, y) = c. x a, y b

More information

1 No.1 5 C 1 I III F 1 F 2 F 1 F 2 2 Φ 2 (t) = Φ 1 (t) Φ 1 (t t). = Φ 1(t) t = ( 1.5e 0.5t 2.4e 4t 2e 10t ) τ < 0 t > τ Φ 2 (t) < 0 lim t Φ 2 (t) = 0

1 No.1 5 C 1 I III F 1 F 2 F 1 F 2 2 Φ 2 (t) = Φ 1 (t) Φ 1 (t t). = Φ 1(t) t = ( 1.5e 0.5t 2.4e 4t 2e 10t ) τ < 0 t > τ Φ 2 (t) < 0 lim t Φ 2 (t) = 0 1 No.1 5 C 1 I III F 1 F 2 F 1 F 2 2 Φ 2 (t) = Φ 1 (t) Φ 1 (t t). = Φ 1(t) t = ( 1.5e 0.5t 2.4e 4t 2e 10t ) τ < 0 t > τ Φ 2 (t) < 0 lim t Φ 2 (t) = 0 0 < t < τ I II 0 No.2 2 C x y x y > 0 x 0 x > b a dx

More information

untitled

untitled ,, 2 2.,, A, PC/AT, MB, 5GB,,,, ( ) MB, GB 2,5,, 8MB, A, MB, GB 2 A,,,? x MB, y GB, A (), x + 2y () 4 (,, ) (hanba@eee.u-ryukyu.ac.jp), A, x + 2y() x y, A, MB ( ) 8 MB ( ) 5GB ( ) ( ), x x x 8 (2) y y

More information

* n x 11,, x 1n N(µ 1, σ 2 ) x 21,, x 2n N(µ 2, σ 2 ) H 0 µ 1 = µ 2 (= µ ) H 1 µ 1 µ 2 H 0, H 1 *2 σ 2 σ 2 0, σ 2 1 *1 *2 H 0 H

* n x 11,, x 1n N(µ 1, σ 2 ) x 21,, x 2n N(µ 2, σ 2 ) H 0 µ 1 = µ 2 (= µ ) H 1 µ 1 µ 2 H 0, H 1 *2 σ 2 σ 2 0, σ 2 1 *1 *2 H 0 H 1 1 1.1 *1 1. 1.3.1 n x 11,, x 1n Nµ 1, σ x 1,, x n Nµ, σ H 0 µ 1 = µ = µ H 1 µ 1 µ H 0, H 1 * σ σ 0, σ 1 *1 * H 0 H 0, H 1 H 1 1 H 0 µ, σ 0 H 1 µ 1, µ, σ 1 L 0 µ, σ x L 1 µ 1, µ, σ x x H 0 L 0 µ, σ 0

More information

newmain.dvi

newmain.dvi 数論 サンプルページ この本の定価 判型などは, 以下の URL からご覧いただけます. http://www.morikita.co.jp/books/mid/008142 このサンプルページの内容は, 第 2 版 1 刷発行当時のものです. Daniel DUVERNEY: THÉORIE DES NOMBRES c Dunod, Paris, 1998, This book is published

More information

4 4 4 a b c d a b A c d A a da ad bce O E O n A n O ad bc a d n A n O 5 {a n } S n a k n a n + k S n a a n+ S n n S n n log x x {xy } x, y x + y 7 fx

4 4 4 a b c d a b A c d A a da ad bce O E O n A n O ad bc a d n A n O 5 {a n } S n a k n a n + k S n a a n+ S n n S n n log x x {xy } x, y x + y 7 fx 4 4 5 4 I II III A B C, 5 7 I II A B,, 8, 9 I II A B O A,, Bb, b, Cc, c, c b c b b c c c OA BC P BC OP BC P AP BC n f n x xn e x! e n! n f n x f n x f n x f k x k 4 e > f n x dx k k! fx sin x cos x tan

More information

DVIOUT

DVIOUT A. A. A-- [ ] f(x) x = f 00 (x) f 0 () =0 f 00 () > 0= f(x) x = f 00 () < 0= f(x) x = A--2 [ ] f(x) D f 00 (x) > 0= y = f(x) f 00 (x) < 0= y = f(x) P (, f()) f 00 () =0 A--3 [ ] y = f(x) [, b] x = f (y)

More information

1 1.1 ( ). z = a + bi, a, b R 0 a, b 0 a 2 + b 2 0 z = a + bi = ( ) a 2 + b 2 a a 2 + b + b 2 a 2 + b i 2 r = a 2 + b 2 θ cos θ = a a 2 + b 2, sin θ =

1 1.1 ( ). z = a + bi, a, b R 0 a, b 0 a 2 + b 2 0 z = a + bi = ( ) a 2 + b 2 a a 2 + b + b 2 a 2 + b i 2 r = a 2 + b 2 θ cos θ = a a 2 + b 2, sin θ = 1 1.1 ( ). z = + bi,, b R 0, b 0 2 + b 2 0 z = + bi = ( ) 2 + b 2 2 + b + b 2 2 + b i 2 r = 2 + b 2 θ cos θ = 2 + b 2, sin θ = b 2 + b 2 2π z = r(cos θ + i sin θ) 1.2 (, ). 1. < 2. > 3. ±,, 1.3 ( ). A

More information

Part y mx + n mt + n m 1 mt n + n t m 2 t + mn 0 t m 0 n 18 y n n a 7 3 ; x α α 1 7α +t t 3 4α + 3t t x α x α y mx + n

Part y mx + n mt + n m 1 mt n + n t m 2 t + mn 0 t m 0 n 18 y n n a 7 3 ; x α α 1 7α +t t 3 4α + 3t t x α x α y mx + n Part2 47 Example 161 93 1 T a a 2 M 1 a 1 T a 2 a Point 1 T L L L T T L L T L L L T T L L T detm a 1 aa 2 a 1 2 + 1 > 0 11 T T x x M λ 12 y y x y λ 2 a + 1λ + a 2 2a + 2 0 13 D D a + 1 2 4a 2 2a + 2 a

More information

III 1 (X, d) d U d X (X, d). 1. (X, d).. (i) d(x, y) d(z, y) d(x, z) (ii) d(x, y) d(z, w) d(x, z) + d(y, w) 2. (X, d). F X.. (1), X F, (2) F 1, F 2 F

III 1 (X, d) d U d X (X, d). 1. (X, d).. (i) d(x, y) d(z, y) d(x, z) (ii) d(x, y) d(z, w) d(x, z) + d(y, w) 2. (X, d). F X.. (1), X F, (2) F 1, F 2 F III 1 (X, d) d U d X (X, d). 1. (X, d).. (i) d(x, y) d(z, y) d(x, z) (ii) d(x, y) d(z, w) d(x, z) + d(y, w) 2. (X, d). F X.. (1), X F, (2) F 1, F 2 F F 1 F 2 F, (3) F λ F λ F λ F. 3., A λ λ A λ. B λ λ

More information

x, y x 3 y xy 3 x 2 y + xy 2 x 3 + y 3 = x 3 y xy 3 x 2 y + xy 2 x 3 + y 3 = 15 xy (x y) (x + y) xy (x y) (x y) ( x 2 + xy + y 2) = 15 (x y)

x, y x 3 y xy 3 x 2 y + xy 2 x 3 + y 3 = x 3 y xy 3 x 2 y + xy 2 x 3 + y 3 = 15 xy (x y) (x + y) xy (x y) (x y) ( x 2 + xy + y 2) = 15 (x y) x, y x 3 y xy 3 x 2 y + xy 2 x 3 + y 3 = 15 1 1977 x 3 y xy 3 x 2 y + xy 2 x 3 + y 3 = 15 xy (x y) (x + y) xy (x y) (x y) ( x 2 + xy + y 2) = 15 (x y) ( x 2 y + xy 2 x 2 2xy y 2) = 15 (x y) (x + y) (xy

More information

微分積分 サンプルページ この本の定価 判型などは, 以下の URL からご覧いただけます. このサンプルページの内容は, 初版 1 刷発行時のものです.

微分積分 サンプルページ この本の定価 判型などは, 以下の URL からご覧いただけます.   このサンプルページの内容は, 初版 1 刷発行時のものです. 微分積分 サンプルページ この本の定価 判型などは, 以下の URL からご覧いただけます. ttp://www.morikita.co.jp/books/mid/00571 このサンプルページの内容は, 初版 1 刷発行時のものです. i ii 014 10 iii [note] 1 3 iv 4 5 3 6 4 x 0 sin x x 1 5 6 z = f(x, y) 1 y = f(x)

More information

分散分析・2次元正規分布

分散分析・2次元正規分布 2 II L10(2016-06-30 Thu) : Time-stamp: 2016-06-30 Thu 13:55 JST hig F 2.. http://hig3.net ( ) L10 2 II(2016) 1 / 24 F 2 F L09-Q1 Quiz :F 1 α = 0.05, 2 F 3 H 0, : σ 2 1 /σ2 2 = 1., H 1, σ 2 1 /σ2 2 1. 4

More information

arctan 1 arctan arctan arctan π = = ( ) π = 4 = π = π = π = =

arctan 1 arctan arctan arctan π = = ( ) π = 4 = π = π = π = = arctan arctan arctan arctan 2 2000 π = 3 + 8 = 3.25 ( ) 2 8 650 π = 4 = 3.6049 9 550 π = 3 3 30 π = 3.622 264 π = 3.459 3 + 0 7 = 3.4085 < π < 3 + 7 = 3.4286 380 π = 3 + 77 250 = 3.46 5 3.45926 < π < 3.45927

More information

,, etc. ( ) [Marti & Stoeckel 04] [Lloyd Smith, Chuang & Munro 90], [Staat & Heitzer 03] worst-case detection [Elishakoff, Haftka & Fang 94] 2 [Cheng

,, etc. ( ) [Marti & Stoeckel 04] [Lloyd Smith, Chuang & Munro 90], [Staat & Heitzer 03] worst-case detection [Elishakoff, Haftka & Fang 94] 2 [Cheng ( ) ( ) OPTIS 2006 p.1/17 ,, etc. ( ) [Marti & Stoeckel 04] [Lloyd Smith, Chuang & Munro 90], [Staat & Heitzer 03] worst-case detection [Elishakoff, Haftka & Fang 94] 2 [Cheng et al. 02], [Craig et al.

More information

9 2 1 f(x, y) = xy sin x cos y x y cos y y x sin x d (x, y) = y cos y (x sin x) = y cos y(sin x + x cos x) x dx d (x, y) = x sin x (y cos y) = x sin x

9 2 1 f(x, y) = xy sin x cos y x y cos y y x sin x d (x, y) = y cos y (x sin x) = y cos y(sin x + x cos x) x dx d (x, y) = x sin x (y cos y) = x sin x 2009 9 6 16 7 1 7.1 1 1 1 9 2 1 f(x, y) = xy sin x cos y x y cos y y x sin x d (x, y) = y cos y (x sin x) = y cos y(sin x + x cos x) x dx d (x, y) = x sin x (y cos y) = x sin x(cos y y sin y) y dy 1 sin

More information

2012_00表紙

2012_00表紙 02 Network Program 05 1 2 3 4 5 6 1 13 60 5,800 06 07 Program 10 12 Program 14 Program 18 20 Program 22 24 Program 25 26 27 28 29 30 31 32 Program 33 34 35 40 Program 41 42 43 44 45 46 Program

More information

タ 縺29135 タ 縺5 [ y 1 x i R 8 x j 1 7,5 2 x , チ7192, (2) チ41299 f 675

タ 縺29135 タ 縺5 [ y 1 x i R 8 x j 1 7,5 2 x , チ7192, (2) チ41299 f 675 139ィ 48 1995 3. 753 165, 2 6 86 タ7 9 998917619 4381 縺48 縺55 317832645 タ5 縺4273 971927, 95652539358195 45 チ5197 9 4527259495 2 7545953471 129175253471 9557991 3.9. タ52917652 縺1874ィ 989 95652539358195 45

More information

. p.1/15

. p.1/15 . p./5 [ ] x y y x x y fx) y fx) x y. p.2/5 [ ] x y y x x y fx) y fx) x y y x y x y fx) f y) x f y) f f f f. p.2/5 [ ] x y y x x y fx) y fx) x y y x y x y fx) f y) x f y) f f f f [ ] a > y a x R ). p.2/5

More information

A A p.1/16

A A p.1/16 A A p./6 A p.2/6 [ ] x y y x x y fx) y fx) x y A p.3/6 [ ] x y y x x y fx) y fx) x y y x y x y fx) f y) x f y) f f f f A p.3/6 [ ] x y y x x y fx) y fx) x y y x y x y fx) f y) x f y) f f f f [ ] a > y

More information

カテゴリ変数と独立性の検定

カテゴリ変数と独立性の検定 II L04(2015-05-01 Fri) : Time-stamp: 2015-05-01 Fri 22:28 JST hig 2, Excel 2, χ 2,. http://hig3.net () L04 II(2015) 1 / 20 : L03-S1 Quiz : 1 2 7 3 12 (x = 2) 12 (y = 3) P (X = x) = 5 12 (x = 3), P (Y =

More information

Part () () Γ Part ,

Part () () Γ Part , Contents a 6 6 6 6 6 6 6 7 7. 8.. 8.. 8.3. 8 Part. 9. 9.. 9.. 3. 3.. 3.. 3 4. 5 4.. 5 4.. 9 4.3. 3 Part. 6 5. () 6 5.. () 7 5.. 9 5.3. Γ 3 6. 3 6.. 3 6.. 3 6.3. 33 Part 3. 34 7. 34 7.. 34 7.. 34 8. 35

More information

: BV15005

: BV15005 29 5 26 : BV15005 1 1 1.1............................................. 1 1.2........................................ 1 1.3........................................ 1 2 3 2.1.............................................

More information

II 1 3 2 5 3 7 4 8 5 11 6 13 7 16 8 18 2 1 1. x 2 + xy x y (1 lim (x,y (1,1 x 1 x 3 + y 3 (2 lim (x,y (, x 2 + y 2 x 2 (3 lim (x,y (, x 2 + y 2 xy (4 lim (x,y (, x 2 + y 2 x y (5 lim (x,y (, x + y x 3y

More information

( 28 ) ( ) ( ) 0 This note is c 2016, 2017 by Setsuo Taniguchi. It may be used for personal or classroom purposes, but not for commercial purp

( 28 ) ( ) ( ) 0 This note is c 2016, 2017 by Setsuo Taniguchi. It may be used for personal or classroom purposes, but not for commercial purp ( 28) ( ) ( 28 9 22 ) 0 This ote is c 2016, 2017 by Setsuo Taiguchi. It may be used for persoal or classroom purposes, but ot for commercial purposes. i (http://www.stat.go.jp/teacher/c2epi1.htm ) = statistics

More information

December 28, 2018

December 28, 2018 e-mail : kigami@i.kyoto-u.ac.jp December 28, 28 Contents 2............................. 3.2......................... 7.3..................... 9.4................ 4.5............. 2.6.... 22 2 36 2..........................

More information

() n C + n C + n C + + n C n n (3) n C + n C + n C 4 + n C + n C 3 + n C 5 + (5) (6 ) n C + nc + 3 nc n nc n (7 ) n C + nc + 3 nc n nc n (

() n C + n C + n C + + n C n n (3) n C + n C + n C 4 + n C + n C 3 + n C 5 + (5) (6 ) n C + nc + 3 nc n nc n (7 ) n C + nc + 3 nc n nc n ( 3 n nc k+ k + 3 () n C r n C n r nc r C r + C r ( r n ) () n C + n C + n C + + n C n n (3) n C + n C + n C 4 + n C + n C 3 + n C 5 + (4) n C n n C + n C + n C + + n C n (5) k k n C k n C k (6) n C + nc

More information

http://www2.math.kyushu-u.ac.jp/~hara/lectures/lectures-j.html 2 N(ε 1 ) N(ε 2 ) ε 1 ε 2 α ε ε 2 1 n N(ɛ) N ɛ ɛ- (1.1.3) n > N(ɛ) a n α < ɛ n N(ɛ) a n

http://www2.math.kyushu-u.ac.jp/~hara/lectures/lectures-j.html 2 N(ε 1 ) N(ε 2 ) ε 1 ε 2 α ε ε 2 1 n N(ɛ) N ɛ ɛ- (1.1.3) n > N(ɛ) a n α < ɛ n N(ɛ) a n http://www2.math.kyushu-u.ac.jp/~hara/lectures/lectures-j.html 1 1 1.1 ɛ-n 1 ɛ-n lim n a n = α n a n α 2 lim a n = 1 n a k n n k=1 1.1.7 ɛ-n 1.1.1 a n α a n n α lim n a n = α ɛ N(ɛ) n > N(ɛ) a n α < ɛ

More information

No δs δs = r + δr r = δr (3) δs δs = r r = δr + u(r + δr, t) u(r, t) (4) δr = (δx, δy, δz) u i (r + δr, t) u i (r, t) = u i x j δx j (5) δs 2

No δs δs = r + δr r = δr (3) δs δs = r r = δr + u(r + δr, t) u(r, t) (4) δr = (δx, δy, δz) u i (r + δr, t) u i (r, t) = u i x j δx j (5) δs 2 No.2 1 2 2 δs δs = r + δr r = δr (3) δs δs = r r = δr + u(r + δr, t) u(r, t) (4) δr = (δx, δy, δz) u i (r + δr, t) u i (r, t) = u i δx j (5) δs 2 = δx i δx i + 2 u i δx i δx j = δs 2 + 2s ij δx i δx j

More information

0 (18) /12/13 (19) n Z (n Z ) 5 30 (5 30 ) (mod 5) (20) ( ) (12, 8) = 4

0   (18) /12/13 (19) n Z (n Z ) 5 30 (5 30 ) (mod 5) (20) ( ) (12, 8) = 4 0 http://homepage3.nifty.com/yakuikei (18) 1 99 3 2014/12/13 (19) 1 100 3 n Z (n Z ) 5 30 (5 30 ) 37 22 (mod 5) (20) 201 300 3 (37 22 5 ) (12, 8) = 4 (21) 16! 2 (12 8 4) (22) (3 n )! 3 (23) 100! 0 1 (1)

More information

( )

( ) 18 10 01 ( ) 1 2018 4 1.1 2018............................... 4 1.2 2018......................... 5 2 2017 7 2.1 2017............................... 7 2.2 2017......................... 8 3 2016 9 3.1 2016...............................

More information