min. z = 602.5x x 2 + 2

Size: px
Start display at page:

Download "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"

Transcription

1 ? 1 LP (intger programming problem) x 1 = / /

2 min. z = 602.5x x x x x x x 7 s.t. 24x x x x x x x 7 = x 1 + 5x x x x x x x x x x x x x x j 0, j = 1,..., 7 (4.1) 1 x 1 x 3 x 5 x 7 x 2 x 4 x 6 x 1 = x 2 = 0, x 3 = 19.4, x 4 = 14.3, x 5 = 13.1, x 6 = x 7 = 0, z = ( ) x j 250, j = 1,..., 7 x 1 = 0.1, x 2 = 0, x 3 = 17.6, x 4 = 13.9, x 5 = 6.9, x 6 = 6.0, x 7 = 0, z =

3 ( 250 ) x j /? LP 128 LP / x j / z j (j = 1,..., 7) 0 1 z j = 0 j z j = 1 j / (z j = 1 or 0) 24x 1 1 z 1 = 0 24x 1 = 0 z 1 = x z 1 24x 1 250z 1 / min. z = 602.5x x x x x x x 7 s.t. 24x x x x x x x 7 = x 1 + 5x x x x x x x x x x x x x z 1 24x 1 250z 1, 100z 2 12x 2 250z 2 100z 3 14x 3 250z 3, 100z 4 18x 4 250z 4 100z 5 36x 5 250z 5, 100z 6 42x 6 250z 6 100z 7 24x 7 250z 7 x j 0, j = 1,..., 7 z j {0, 1}, j = 1,..., 7 (4.2)

4 ( ) z 1 = 1 z 2 = 0 z 3 = 1 z 4 = 1 z 5 = 1 z 6 = 1 z 7 = 0 x 1 = 4.2 x 2 = 0 x 3 = 17.9 x 4 = 13.9 x 5 = 6.9 x 6 = 3.6 x 7 = maximize subject to n j=1 c jx j n j=1 a ijx j = b i, x j 0, i = 1,..., m (1.5) maximize subject to n j=1 c jx j n j=1 a ijx j = b i, x j 0, x j Z, i = 1,..., m (4.3) ( ) ((mixed) integer programming problem) 2 Z (1.5) (4.3) (1.5) x (4.3) x c x c x (1.5) (4.3) x x maximize x 1 + x 2 + x 3 subject to 0.27x x 2 + 3x x x x 1 + x 2 x 3 1 x j 0, j = 1,..., 3 (4.4) x 1 = 2.80, x 2 = 1.51 x 3 = ( )

5 ( 3 ) (4.4) maximize x 1 + x 2 + x 3 subject to 0.27x x 2 + 3x x x x 1 + x 2 x 3 1 x j 0, j = 1,..., 3 x j Z, j = 1,..., 3 (4.5) x 1 = 3, x 2 = 0 x 3 = 0 x x = x LP IP [0, 1] [1, 2] 10 4, OS: Linux CPU: Intel Pentium GHz lp solve (Version 4.0) ( ) IP LP (branch and bounding method) 3 x x IP 1

6 LP IP ( )

7 LP IP ( ) 1400 LP IP time(sec) n max c x s.t. Ax = b P 0 l 0 j x j u 0 j, (4.6) x Z n P 0 Z n n l u P 0 P 0 P 0 P 0 (4.7) P 0 s.t. Ax = b max c x l 0 j x j u 0 j, P 0 P 0 P 0 P 0 P 0 P 0 (x 0 1,..., x0 n) x 0 s ξ ξ ( )

8 112 4 P 0 P 1 P 1 x 0 s 2 P 1 max s.t. c x Ax = b l 0 j x j u 0 j, l s x s x 0 s x Z n ; j s P 2 max s.t. c x Ax = b l 0 j x j u 0 j, ; j s x 0 s + 1 x s u 0 s x Z n P 1 P 2 P 0 P 1 P 2 P 0 P 1 P 2 ( ) P j P j 3 case 1) P j case 2) P j case 3) P j x j ^x c x j c ^x P j c x j > c ^x ( )

9 P1 P11: P0 P12 P121: P2:»» P122:»ˆ Œ ^x x c ^x c x ɛ ɛ 2 max 5x 1 + 2x 2 s. t. 6x 1 + 2x x 1 + 4x 2 15 x 1 + 2x 2 5 x 1, x 2 0 x 1, x 2 Z (4.8) (4.8) P 0 max 5x 1 + 2x 2 s. t. 6x 1 + 2x x 1 + 4x 2 15 x 1 + 2x 2 5 x 1, x 2 0 (4.9) P 0 (x 0 1, x0 2 ) ( x 0 1 x 0 2) = ( ) z 0 = (4.8)

10 (4.8) 3 2 x x 1 P 0 P 1 P 2 x 0 1 = x 2 x 1 1 max 5x 1 + 2x 2 max 5x 1 + 2x 2 s. t. 6x 1 + 2x 2 15 s. t. 6x 1 + 2x x 1 + 4x x 1 + 4x 2 15 P 1 P 2 (4.10) x 1 + 2x 2 5 x 1 + 2x 2 5 x 1 2 x 1 1 x 1, x 2 0 x 1, x 2 0 P 1 P 1 : ( ) ( ) x 1 1 x 1 2 = z 1 = 13 P 2 P 1 P 11 P 12 x 1 2 = 1.5 P 1

11 P 1 P x 2 1 P 2 P x 1 x 2 2 x 2 1 max 5x 1 + 2x 2 s. t. 6x 1 + 2x x 1 + 4x 2 15 P 11 x 1 + 2x 2 5 x 1 2 x 2 2 x 1, x 2 0 P 12 max 5x 1 + 2x 2 s. t. 6x 1 + 2x x 1 + 4x 2 15 x 1 + 2x 2 5 x 1 2 x 2 1 x 1, x 2 0 (4.11) P 11 P 12 P 12 : ( ) ( ) x 12 1 x 12 2 = z 12 = P 11 P 12 P 121 P 122

12 116 4 P 121 max 5x 1 + 2x 2 s. t. 6x 1 + 2x x 1 + 4x 2 15 x 1 + 2x 2 5 x 1 3 x 2 1 x 1, x 2 0 P 122 max 5x 1 + 2x 2 s. t. 6x 1 + 2x x 1 + 4x 2 15 x 1 + 2x x 1 2 x 2 1 x 1, x 2 0 (4.12) P 121 P 122 P 122 : ( ) ( ) x x = 2 1 z 122 = 12 ( 2 1 ) P 2 P 2 : ( x 2 1 x 2 2) = ( ) z 2 = 10.5 z 2 = P 2 ( 2 1 ) (4.8) 4.2 P 0 x 0 1 x ( ) ( )

13 j (j = 1,..., 4) 0-1 x j x j = 1 j x j = 0 max 100x x x x 4 s.t. 70x x x x x j {0, 1}, j = 1,..., 4 (4.13) (4.13) (napsack problem) (4.13) ( 4.9) max 100x x x x x x 6 s.t. 70x x x x x x x x x 1 + x 5 1, x 4 + x 6 1 x 1 x 7 0 x 2 x 7 0 x 5 x 7 0 x 3 x 8 0 x 4 x 8 0 x 6 x 8 0 x j {0, 1}, j = 1,..., 8 (4.14)

14 118 4 (4.13) (4.14) (x 1, x 2, x 3, x 4 ) = (1, 0, 0, 1) (x 1, x 2, x 3, x 4, x 5, x 6, x 7, x 8 ) = (1, 0, 1, 0, 0, 1, 1, 1) ( ) ( ) A 20 B 18 C 32 D 9 E 22 5 (greedy method)

15 {1 x j (j A, B, C, D, E) min 20x A + 18x B + 32x C + 9x D + 22x E s.t. x A + x C + x D + x E 1 x A + x B 1 x C + x E 1 x A + x C + x E 1 x C + x D + x E 1 x A + x C 1 x A + x D + x E 1 x B + x C + x D 1 x j {0, 1}, j {A, B, C, D, E} x A = x D = x E = 1 x B = x C = (69 ) LP LP (set covering problem) A , B C D E F

16 kg 1kg 500g 200g 6 (4.13) x 1 2 x 2 U(x 1, x 2 ) max U(x 1, x 2 ) s.t. 20x x x 1 = 100z 1 x 2 = 80z 2 x j 0 j = 1, 2 z j Z j = 1, max U(x 1, x 2 ) s.t. 20x x f f x 1 = 100z 1 x 2 = 80z 2 z j Mf j, j = 1, 2 x j 0, j = 1, 2 z j Z, j = 1, 2 f j {0, 1}, j = 1, 2 M ( M 50 ) z 1 = 0 f 1 =

17 d 3 d 2 transacton cost d 1 q q q quantity: x x j v j L ( )c j p j min s.t. n j=1 p jx j n j=1 c jx j + L x j 0, q 3 j y j x j 0-1 z j0 z j1 z j2 λ j0 λ j1 λ j2 λ j3 6 7

18 122 4 min s.t. n j=1 p jx j + n j=1 y j n j=1 c jx j + L x j = q 1 λ j1 + q 2 λ j2 + q 3 λ j3, y j = d 1 λ j1 + d 2 λ j2 + d 3 λ j3, λ j0 + λ j1 + λ j2 + λ j3 = 1, λ 0j z 1j, λ 1j z 1j + z 2j, λ 2j z 2j + z 3j, λ 3j z 3j, z 1j + z 2j + z 3j = 1, x j 0, y j 0, λ 0j, λ 1j, λ 2j, λ 3j 0, z 1j, z 2j, z 3j {0, 1}, (2 ) 8

19 (1 0 ) 7/5 Sun. 7/6 Mon. 7/18 Sat A B C D E W X A { 1 B { 1 C { { D { { E { {.. W { X { i j

20 124 4 x ij (i = A, B,, X; j = 1, 2,..., 70) x i,j 0-1 x ij = { 1, i j 0, i j (4.15) x A1 = A A x A4 = 0, x A5 = A x A1 + 4x A2 + 5x A3 + 7x A4 + 3x A5 + 5x A6 + 4x A x A69 + 3x A x A1 + 4x A2 + 5x A3 + 7x A4 + 3x A5 + 5x A6 + 4x A x A69 + 3x A70 70 (4.16) A x A1 + 4x A2 + 5x A3 12, 4x A2 + 5x A3 + 7x A4 12, 5x A3 + 7x A4 + 3x A5 12, 7x A4 + 3x A5 + 5x A6 12, 3x A5 + 5x A6 + 4x A7 + 5x A8 12 5x A6 + 4x A7 + 5x A8 12 (4.17). 4x A67 + 5x A68 + 7x A69 12, 5x A68 + 7x A69 + 3x A70 12 A 12 A 7 5 (1 x A2 ) + (1 x A3 ) + (1 x A4 ) 3(x A1 x A2 ) (x A1 = 1) (x A2 = 0) 3 (x A2 = x A3 = x A4 = 0) (x A1 = 1) (x A2 = 1) 0 8 x A4 x A5

21 (1 x A2 ) + (1 x A3 ) + (1 x A4 ) 3(x A1 x A2 ) (1 x A3 ) + (1 x A4 ) 2(x A2 x A3 ) (1 x A4 ) + (1 x A5 ) + (1 x A6 ) 3(x A3 x A4 ). (1 x A68 ) + (1 x A69 ) 2(x A67 x A68 ) (4.18) 3 A X (4.16) (4.17) (4.18) W W A B G H x W1 x A1 + x B1 + x G1 + x H1,. (4.19) x W70 x A70 + x B70 + x G70 + x H70 X x A1 + x B1 + + x X1 2 x A2 + x B2 + + x X2 3 x A70 + x B x X70 2 (4.20) M 50(5x A1 + 4x A2 + 5x A3 + 7x A4 + 3x A5 + 5x A6 + 4x A x A69 + 3x A70 ) + 80(5x B1 + 4x B2 + 5x B3 + 7x B4 + 3x B5 + 5x B6 + 4x B x B69 + 3x B70 ) 50(5x X1 + 4x X2 + 5x X3 + 7x X4 + 3x X5 + 5x X6 + 4x X x X69 + 3x X70 ) M (4.21) = ( ) = 3499

22 A 4.6 A 12

( ) ? () 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

数値計算:有限要素法

数値計算:有限要素法 ( ) 1 / 61 1 2 3 4 ( ) 2 / 61 ( ) 3 / 61 P(0) P(x) u(x) P(L) f P(0) P(x) P(L) ( ) 4 / 61 L P(x) E(x) A(x) x P(x) P(x) u(x) P(x) u(x) (0 x L) ( ) 5 / 61 u(x) 0 L x ( ) 6 / 61 P(0) P(L) f d dx ( EA du dx

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

untitled

untitled 960-8055 TEL0245317966FAX0245318160 [email protected] 960-1426 61 (1)-3160 32. 3.25 (4)-6157 33. 6.11 960-8032 824 SSTFUKUSHIMA11A 024-563-5440 F 024-563-5441 024-526-0746 F 024-526-0748 (8)-10310

More information

????? 1???

????? 1??? SUN MON TUE WED THU FRI SAT SUN MON TUE WED THU FRI SAT SUN MON TUE WED THU FRI SAT SUN MON TUE WED THU FRI SAT SUN MON TUE WED THU FRI SAT SUN MON TUE WED THU FRI SAT SUN MON TUE WED THU FRI SAT SUN MON

More information

サービス付き高齢者向け住宅賠償責任保険.indd

サービス付き高齢者向け住宅賠償責任保険.indd 1 2 1 CASE 1 1 2 CASE 2 CASE 3 CASE 4 3 CASE 5 4 3 4 5 6 2 CASE 1 CASE 2 CASE 3 7 8 3 9 10 CASE 1 CASE 2 CASE 3 CASE 4 11 12 13 14 1 1 2 FAX:03-3375-8470 2 3 3 4 4 3 15 16 FAX:03-3375-8470 1 2 0570-022808

More information

目    次

目    次 1 2 3 t 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 IP 169 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67

More information

*2015カタログ_ブック.indb

*2015カタログ_ブック.indb -319 -320 -321 -322-40 1600-20 0 20 40 60 80 100 1600 1000 600 400 200 100 60 40 20 VG 22 VG 32 VG 46 VG 68 VG 100 36 16 ν opt. 10 5 5-40 -25-10 0 10 30 50 70 90 115 t min = -40 C t max = +115 C 0.5 0.4

More information

/ 2 ( ) ( ) ( ) = R ( ) ( ) 1 1 1/ 3 = 3 2 2/ R :. (topology)

/ 2 ( ) ( ) ( ) = R ( ) ( ) 1 1 1/ 3 = 3 2 2/ R :. (topology) 3 1 3.1. (set) x X x X x X 2. (space) Hilbert Teichmüller 2 R 2 1 2 1 / 2 ( ) ( ) ( ) 1 0 1 + = R 2 0 1 1 ( ) ( ) 1 1 1/ 3 = 3 2 2/ R 2 3 3.1:. (topology) 3.2 30 3 3 2 / 3 3.2.1 S O S (O1)-(O3) (O1) S

More information

1 2 3 4 10 5 30 87 50 20 3 7 2 2 6 3 70 7 5 10 20 20 30 14 5 1,000 24 112 2 3 1 8 110 9 JR 10 110 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 25 30 31 32 25 A 33 B C D E F G PR PR or 34 35

More information

16 41 17 22 12 10

16 41 17 22 12 10 1914 11 1897 99 16 41 17 22 12 10 11 10 18 11 2618 12 22 28 15 1912 13 191516 2,930 1914 5,100 43 1.25 11 14 25 34364511 7.54 191420 434849 72 191536 1739 17 1918 1915 60 1913 70 10 10 10 99.5 1898 19034.17.6

More information

製品案内 価格表 2014/4/1

製品案内 価格表 2014/4/1 4 (17) 3 43 5/20370/ 231(504,150) 11 12 10 14-16 10 3 100 17 100kg 5-6 3 13 3 18 18 # # # # #$$ %&$ ' ()* +,-% ' #). +,-%'% / ' # # #$ %&&&'( %)* +'(#$ #$ %&&&'( ++,-). +'(#$ #$ /'( + /0)- +'(#$ %&&&'(

More information

3.ごみの減量方法.PDF

3.ごみの減量方法.PDF - 7 - - 8 - - 9 - - 10 - - 11 - - 12 - ( 100 ( 100 - 13-123,550,846 111,195,762 92,663,135 ( 12 25 37 49.2 16 33 49 65.6 15 30 44 59.0 2.5kg) ( 5kg) ( 7.5kg) ( k ( 123,550,846 111,195,762 92,663,135 (

More information

index calculus

index calculus index calculus 2008 3 8 1 generalized Weil descent p :, E/F p 3 : Y 2 = f(x), where f(x) = X 3 + AX + B, A F p, B F p 3 E(F p 3) 3 : Generalized Weil descent E(F p 4) 2 Index calculus Plain version Double-large-prime

More information

untitled

untitled 280 200 5 7,800 6 8,600 28 1 1 18 7 8 2 ( 31 ) 7 42 2 / / / / / / / / / / 1 3 (1) 4 5 3 1 1 1 A B C D 6 (1) -----) (2) -- ()) (3) ----(). ()() () ( )( )( )( ) ( ) ( )( )( )( ) () (). () ()() 7 () ( ) 1

More information

Step1 Step2 Step3 Step4 Step5 COLUMN.1 Step1 Step2 Step3 Step4 Step5 Step6 Step7 Step8 COLUMN.2 Step1 Step2 Step3 Step4 Step5 COLUMN.3 Step1 Step2 Ste

Step1 Step2 Step3 Step4 Step5 COLUMN.1 Step1 Step2 Step3 Step4 Step5 Step6 Step7 Step8 COLUMN.2 Step1 Step2 Step3 Step4 Step5 COLUMN.3 Step1 Step2 Ste 2 0 1 2 C A L E N D A R 7 8 9 SUN MON TUE WED THU FRI SAT SUN MON TUE WED THU FRI SAT SUN MON TUE WED THU FRI SAT 1 2 3 4 5 6 7 1 2 3 4 8 9 10 11 12 13 14 5 6 7 8 9 10 11 15 16 17 18 19 20 21 12 13 14

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

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

…p…^†[…fiflF”¯ Pattern Recognition

…p…^†[…fiflF”¯   Pattern Recognition Pattern Recognition Shin ichi Satoh National Institute of Informatics June 11, 2019 (Support Vector Machines) (Support Vector Machines: SVM) SVM Vladimir N. Vapnik and Alexey Ya. Chervonenkis 1963 SVM

More information

n 第1章 章立ての部分は、書式(PC入門大見出し)を使います

n 第1章 章立ての部分は、書式(PC入門大見出し)を使います FORTRAN FORTRAN FORTRAN ) DO DO IF IF FORTRAN FORTRAN(FORmula TRANslator)1956 IBM FORTRAN IV FORTRAN77 Fortran90 FORTRAN77 FORTRAN FORTARN IF, DO C UNIX FORTRAN PASCAL COBOL PL/I BASIC Lisp PROLOG Lisp

More information

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

Emacs ML let start ::= exp (1) exp ::= (2) fn id exp (3) ::= (4) (5) ::= id (6) const (7) (exp) (8) let val id = exp in

Emacs ML let start ::= exp (1) exp ::= (2) fn id exp (3) ::= (4) (5) ::= id (6) const (7) (exp) (8) let val id = exp in Emacs, {l06050,sasano}@sic.shibaura-it.ac.jp Eclipse Visual Studio Standard ML Haskell Emacs 1 Eclipse Visual Studio variable not found LR(1) let Emacs Emacs Emacs Java Emacs JDEE [3] JDEE Emacs Java 2

More information

Solution Report

Solution Report CGE 3 GAMS * Date: 2018/07/24, Version 1.1 1 2 2 GAMSIDE 3 2.1 GAMS................................. 3 2.2 GAMSIDE................................ 3 2.3 GAMSIDE............................. 7 3 GAMS 11

More information

IT 1. IT 2. 2.1. IT 2.2. SKYSEA Client View Government License Light Edition Sky 1500 28 2 15 12 3. 4. 28 3 25 1 5. 5.1. (1) 28 4 1 (2) (3) (4) ISO27001 P (5) ISO/IEC20000 (6) USB 1 (7) OS (8) 1 CPU 4

More information

L P y P y + ɛ, ɛ y P y I P y,, y P y + I P y, 3 ŷ β 0 β y β 0 β y β β 0, β y x x, x,, x, y y, y,, y x x y y x x, y y, x x y y {}}{,,, / / L P / / y, P

L P y P y + ɛ, ɛ y P y I P y,, y P y + I P y, 3 ŷ β 0 β y β 0 β y β β 0, β y x x, x,, x, y y, y,, y x x y y x x, y y, x x y y {}}{,,, / / L P / / y, P 005 5 6 y β + ɛ {x, x,, x p } y, {x, x,, x p }, β, ɛ E ɛ 0 V ɛ σ I 3 rak p 4 ɛ i N 0, σ ɛ ɛ y β y β y y β y + β β, ɛ β y + β 0, β y β y ɛ ɛ β ɛ y β mi L y y ŷ β y β y β β L P y P y + ɛ, ɛ y P y I P y,,

More information

nakayama15icm01_l7filter.pptx

nakayama15icm01_l7filter.pptx Layer-7 SDN SDN NFV 50 % 3 MVNO 1 2 ICM @ 2015/01/16 2 1 1 2 2 1 2 2 ICM @ 2015/01/16 3 2 Service Dependent Management (SDM) SDM Simple Management of Access-Restriction Translator Gateway (SMART-GW) ICM

More information

[email protected] No1 No2 OS Wintel Intel x86 CPU No3 No4 8bit=2 8 =256(Byte) 16bit=2 16 =65,536(Byte)=64KB= 6 5 32bit=2 32 =4,294,967,296(Byte)=4GB= 43 64bit=2 64 =18,446,744,073,709,551,615(Byte)=16EB

More information

広報ひめじ2013年6月号

広報ひめじ2013年6月号 黒 田 官 兵 衛 人 と 生 涯 2 黒 田 官 兵 衛 人 と 生 涯 2 黒 田 官 兵 衛 人 と 生 涯 2 織 田 に 味 方 せ よ 信 長 か ら 名 刀 授 か る 織 田 に 味 方 せ よ 信 長 か ら 名 刀 授 か る 織 田 に 味 方 せ よ 信 長 か ら 名 刀 授 か る Mon Tue Wed Thu Fri Sat Sun 3

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

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 (bit ) ( ) PC WS CPU IEEE754 standard ( 24bit) ( 53bit)

1 (bit ) ( ) PC WS CPU IEEE754 standard ( 24bit) ( 53bit) GNU MP BNCpack [email protected] 2002 9 20 ( ) Linux Conference 2002 1 1 (bit ) ( ) PC WS CPU IEEE754 standard ( 24bit) ( 53bit) 10 2 2 3 4 5768:9:; = %? @BADCEGFH-I:JLKNMNOQP R )TSVU!" # %$ & " #

More information

,,.,,., II,,,.,,.,.,,,.,,,.,, II i

,,.,,., II,,,.,,.,.,,,.,,,.,, II i 12 Load Dispersion Methods in Thin Client Systems 1010405 2001 2 5 ,,.,,., II,,,.,,.,.,,,.,,,.,, II i Abstract Load Dispersion Methods in Thin Client Systems Noritaka TAKEUCHI Server Based Computing by

More information

SmartLMSユーザーズガイド<講師編>

SmartLMSユーザーズガイド<講師編> SmartLearning Management System SmartLMS (1) (2) (3) (4) (3) (5) Microsoft MS PowerPoint DirectX Windows Windows NT Windows Media Microsoft Corporation Intel Pentium Intel Corporation NEC 2003-2004 NEC

More information

konicaminolta.co.jp PageScope Net Care

konicaminolta.co.jp PageScope Net Care konicaminolta.co.jp PageScope Net Care KONICA MINOLTA PageScope Net Care KONICA MINOLTA PageScope Net Care Web KONICA MINOLTA PageScope Net Care SNMP KONICA MINOLTA Printer-MIB KONICA MINOLTA PageScope

More information

▼ RealSecure Desktop Protector 7

▼ RealSecure Desktop Protector 7 System Scanner / Assuria Auditor 4.x システム要件 2006 年 9 月 8 日 System Scanner / Assuria Auditor 4.x システム要件... 1 System Scanner Console... 1 System Scanner 4.2.5 Console... 1 System Scanner 4.2 Console... 2

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