SlideShare a Scribd company logo
1 of 28
Download to read offline
PFI




2011   3   3
•
    •          GLn (R), GLn (C)
        •      GLn (R), O(n)
                  {(        )             }
                       a, b
    • SL2 (R) =               |ab − cd = 1
                       c, d
        • R4                  ab − cd = 1
• Mn (R) :        n
• Mn (R) :            n
• GLn (R)                 :
  = {g ∈ Mn (R) | detg ̸= 0}
• SLn (R)                 = {g ∈ Mn (R) | detg = 1}
• SLn (C)                 = {g ∈ Mn (C) | detg = 1}
• O(n) :         = {g ∈ Mn (R) | gt g = In }
• SO(n) : = {g ∈ Mn (R) | gt g = In , detg = 1}
• U(n) :              = {g ∈ Mn (C) | g∗ g = In }
• SU(n) :                  = {g ∈ Mn (C) | g∗ g = In , detg = 1}
2
• Mn (R)   Rn
            2
• Mn (C)   Cn
                      2   2
    • →             Rn , Cn
•
{                                               }
                                           ∑
                                           n+1
  n
• S =       1        n+1
        (x , . . . , x     )∈R   n+1
                                       |          i 2
                                                 (x ) = 1
                                           i=1
1
             {(                 )}
                cos θ sin θ
• SO(2) =                             = S1
               − sin θ cos θ
                         {                                          }
                                                     ∑
                                                     n+1
• O(n + 1)/O(n) = S =  n          1       n+1
                               (x , . . . x     )|          i 2
                                                           (x ) = 1
                                               i=1
                                    
                                   0
                                  .
    • O(n) ∋ A → 
                          A       .
                                   .  ∈ O(n + 1)                 O(n)
                                  0
                       0 ···     0 1
        O(n + 1)
• O(3, R)    R3 /O(2, R)       {(0, 0, z)} = S1 × R
    •
• SL2 (R) = S1 × R2
2(1/2)



•                        H = {a + bi + cj + dk|a, b, c, d ∈ R}
     •          i = j = k2 = −1, ij = k, jk = i, ki = j, ji = −k,
                2    2

       kj = −i, ik = −j
     •                  (e.g. ij ̸= ji)
• z = a + bi + cj + dk ∈ H
    •      ¯ = a − bi − cj − dk
           z
    •        |z|2 = z¯
                     z
• S3 = {(a, b, c, d) ∈ R4 | z = a + bi + cj + dk           |z| = 1}
2(2/2)
•                              (                 )
                               a + bi   c + di
    • z = a + bi + cj + dk ↔
                               −c + di −a + bi
• → S3    H    SU(2)




              Figure:
• S3   4                          →




           Figure:   Dimensions
1




• f : R2 → R f(x, y) = x2 − y3
     • f −1 (0) : x = 0
• {(x, y) ∈ R2 |x2/3 + y2/3 = 1}
• http://www-history.mcs.st-and.ac.uk/Curves/Curves.html
f : Rr → R            a ∈ Rr
 ∂f
     (a)(1 ≤ i ≤ r)            0
∂xi
      f −1 (f(a))
1




    • f : R2 → R f(x, y) = x2 − y2
         • ∂x f = 2x, ∂y f = −2y
         • ∂x f = ∂y f = 0       (x, y) = (0, 0)
    • f −1 (0) :                2
    •   f −1 (c)   (c ̸= 0) :
2




    • det : Mn (R) → R ; g → detg
        • SLn (R) = det−1 (1)       SLn (R)
2




•
    • (Rn or Cn     )                  R   R

• S1 = {(x, y) ∈ R2 | x2 + y2 = 1},
•          D = {(x, y) ∈ R2 | y ≥ 0}
∑
                                                    n
• f : Mn (R) → R ; g → Tr(t gg)        f(g) =                2
                                                            gij
                                                    i,j=1
    • g ∈ O(n)                                −1
                    f(g) = n √     O(n) ⊂ f        (n)
      O(n)    Mn (R)           n
• O(p, q) = {g ∈ Mn (R) | t gIp,q g = Ip,q }(p + q = n)                     q>0
                                                              
                  1
                      ..                                      
                           .                                  
                                                              
                                   1                          
    • Ip,q   =
              
                                                               
                                                               
                                        −1                    
                                                ..            
                                                     .        
                      {(                         )        −1            }
                           cosh θ       sinh θ
    • O(1, 1) =                                       ∈ R |0 ≤ θ < 2π
                                                          4
                           sinh θ       cosh θ
    • O(3, 1)
3


•




    Figure:
1




    • On (R)               −1     1
               (= SO(n))
        • π0 (On (R)) = Z2

        • dimZ π0 (X) = #{X/ ∼}
            • x∼y⇔x y
(2/2)

  •               g          g′                               On (R)
        g    g′                    c                                   c

        • c : [−1, 1] → On (R) ; c(−1) = g, c(1) = g′
  •               det ◦ c
        • (det ◦ c)(−1) = det(g) = −1
        • (det ◦ c)(1) = det(g′ ) = 1
  •                                    (det ◦ c)(a) = 0   a ∈ (−1, 1)

  •                         c(a) ̸∈ On (R)                c
2




                      Figure:

    • π0 (   ) = Z2
4




•
• π1 (X, x0 ) = {f : [0, 1] → X|f(0) = f(1) = x0 }/ ∼
     • f∼g⇔f         g
•


•        http://www.slideshare.net/KentaOono/pf-iseminar
• π1 (X, x0 ) = 0 ⇒                            1
•                                            X
      1            1             I→X
    p = I(0) q = I(1)




                                       ···
• www.math.sci.hokudai.ac.jp/
                            ˜
    ishikawa/isoukika/isoukika5.pdf
• πn (X, x0 ) = {f : [0, 1]n → X|f(a) = x0 ∀a ∈ ∂([0, 1]n )}/ ∼
• f∼g↔f         g
•
•   :π1 (SOn (R)) = Z/2Z = {0, 1}   (if n ≥ 3)
(1/2)

• su(n) =
    = {X ∈ Mn (C)|X + ∗ X = 0}
• su(2)                   3                         R3

• g ∈ SU(2), X ∈ su(2)            Adg (X) = gXg−1
    gXg−1   ∈ su(2)
• Adg                                   su(2)

•            Adg ∈ SO(3)
      • SU(2) → SO(3) ; g → Adg
•                     2    1                X = Adg      g
              X             2
(2/2)




• Spin(n) → SO(n) :2
•                                           (dimR Mn (R) = n2 )
•
    •            (Sn        )
    •
              X → πn (X), Hn (X)
    •
        • C∞ (M) = {f : M → R|f         }
        •                f:M→R
    •
        • f : [−1, 1] → M           =
        • f : S1 → M,           =

More Related Content

What's hot

5 marks scheme for add maths paper 2 trial spm
5 marks scheme for add maths paper 2 trial spm5 marks scheme for add maths paper 2 trial spm
5 marks scheme for add maths paper 2 trial spm
zabidah awang
 
Datamining 6th svm
Datamining 6th svmDatamining 6th svm
Datamining 6th svm
sesejun
 
009 solid geometry
009 solid geometry009 solid geometry
009 solid geometry
physics101
 
Formulario de calculo
Formulario de calculoFormulario de calculo
Formulario de calculo
Henry Romero
 
ตัวอย่างข้อสอบเก่า วิชาคณิตศาสตร์ ม.6 ปีการศึกษา 2553
ตัวอย่างข้อสอบเก่า วิชาคณิตศาสตร์ ม.6 ปีการศึกษา 2553ตัวอย่างข้อสอบเก่า วิชาคณิตศาสตร์ ม.6 ปีการศึกษา 2553
ตัวอย่างข้อสอบเก่า วิชาคณิตศาสตร์ ม.6 ปีการศึกษา 2553
Destiny Nooppynuchy
 
110218 [아꿈사발표자료] taocp#1 1.2.9. 생성함수
110218 [아꿈사발표자료] taocp#1 1.2.9. 생성함수110218 [아꿈사발표자료] taocp#1 1.2.9. 생성함수
110218 [아꿈사발표자료] taocp#1 1.2.9. 생성함수
Youngman Choe
 
Den5200 ps1
Den5200 ps1Den5200 ps1
Den5200 ps1
jogerpow
 
F2004 formulas final
F2004 formulas finalF2004 formulas final
F2004 formulas final
Abraham Prado
 
2 senarai rumus add maths k2 trial spm sbp 2010
2 senarai rumus add maths k2 trial spm sbp 20102 senarai rumus add maths k2 trial spm sbp 2010
2 senarai rumus add maths k2 trial spm sbp 2010
zabidah awang
 
2 senarai rumus add maths k1 trial spm sbp 2010
2 senarai rumus add maths k1 trial spm sbp 20102 senarai rumus add maths k1 trial spm sbp 2010
2 senarai rumus add maths k1 trial spm sbp 2010
zabidah awang
 
Emat 213 midterm 2 winter 2006
Emat 213 midterm 2 winter 2006Emat 213 midterm 2 winter 2006
Emat 213 midterm 2 winter 2006
akabaka12
 
WE4.L09 - MEAN-SHIFT AND HIERARCHICAL CLUSTERING FOR TEXTURED POLARIMETRIC SA...
WE4.L09 - MEAN-SHIFT AND HIERARCHICAL CLUSTERING FOR TEXTURED POLARIMETRIC SA...WE4.L09 - MEAN-SHIFT AND HIERARCHICAL CLUSTERING FOR TEXTURED POLARIMETRIC SA...
WE4.L09 - MEAN-SHIFT AND HIERARCHICAL CLUSTERING FOR TEXTURED POLARIMETRIC SA...
grssieee
 

What's hot (20)

5 marks scheme for add maths paper 2 trial spm
5 marks scheme for add maths paper 2 trial spm5 marks scheme for add maths paper 2 trial spm
5 marks scheme for add maths paper 2 trial spm
 
Datamining 6th svm
Datamining 6th svmDatamining 6th svm
Datamining 6th svm
 
集合知プログラミングゼミ第1回
集合知プログラミングゼミ第1回集合知プログラミングゼミ第1回
集合知プログラミングゼミ第1回
 
Preserving Personalized Pagerank in Subgraphs(ICML 2011)
Preserving Personalized Pagerank in Subgraphs(ICML 2011) Preserving Personalized Pagerank in Subgraphs(ICML 2011)
Preserving Personalized Pagerank in Subgraphs(ICML 2011)
 
009 solid geometry
009 solid geometry009 solid geometry
009 solid geometry
 
Formulario de calculo
Formulario de calculoFormulario de calculo
Formulario de calculo
 
ตัวอย่างข้อสอบเก่า วิชาคณิตศาสตร์ ม.6 ปีการศึกษา 2553
ตัวอย่างข้อสอบเก่า วิชาคณิตศาสตร์ ม.6 ปีการศึกษา 2553ตัวอย่างข้อสอบเก่า วิชาคณิตศาสตร์ ม.6 ปีการศึกษา 2553
ตัวอย่างข้อสอบเก่า วิชาคณิตศาสตร์ ม.6 ปีการศึกษา 2553
 
110218 [아꿈사발표자료] taocp#1 1.2.9. 생성함수
110218 [아꿈사발표자료] taocp#1 1.2.9. 생성함수110218 [아꿈사발표자료] taocp#1 1.2.9. 생성함수
110218 [아꿈사발표자료] taocp#1 1.2.9. 생성함수
 
Den5200 ps1
Den5200 ps1Den5200 ps1
Den5200 ps1
 
F2004 formulas final
F2004 formulas finalF2004 formulas final
F2004 formulas final
 
Ch33 11
Ch33 11Ch33 11
Ch33 11
 
2 senarai rumus add maths k2 trial spm sbp 2010
2 senarai rumus add maths k2 trial spm sbp 20102 senarai rumus add maths k2 trial spm sbp 2010
2 senarai rumus add maths k2 trial spm sbp 2010
 
2 senarai rumus add maths k1 trial spm sbp 2010
2 senarai rumus add maths k1 trial spm sbp 20102 senarai rumus add maths k1 trial spm sbp 2010
2 senarai rumus add maths k1 trial spm sbp 2010
 
Chapter 07
Chapter 07Chapter 07
Chapter 07
 
Emat 213 midterm 2 winter 2006
Emat 213 midterm 2 winter 2006Emat 213 midterm 2 winter 2006
Emat 213 midterm 2 winter 2006
 
Query Suggestion @ tokyotextmining#2
Query Suggestion @ tokyotextmining#2Query Suggestion @ tokyotextmining#2
Query Suggestion @ tokyotextmining#2
 
International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research(IJCER)International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research(IJCER)
 
Add Maths 2
Add Maths 2Add Maths 2
Add Maths 2
 
Formulas
FormulasFormulas
Formulas
 
WE4.L09 - MEAN-SHIFT AND HIERARCHICAL CLUSTERING FOR TEXTURED POLARIMETRIC SA...
WE4.L09 - MEAN-SHIFT AND HIERARCHICAL CLUSTERING FOR TEXTURED POLARIMETRIC SA...WE4.L09 - MEAN-SHIFT AND HIERARCHICAL CLUSTERING FOR TEXTURED POLARIMETRIC SA...
WE4.L09 - MEAN-SHIFT AND HIERARCHICAL CLUSTERING FOR TEXTURED POLARIMETRIC SA...
 

Similar to One way to see higher dimensional surface

Formulario de matematicas
Formulario de matematicasFormulario de matematicas
Formulario de matematicas
Carlos
 
Datamining 7th kmeans
Datamining 7th kmeansDatamining 7th kmeans
Datamining 7th kmeans
sesejun
 
An Introduction to Hidden Markov Model
An Introduction to Hidden Markov ModelAn Introduction to Hidden Markov Model
An Introduction to Hidden Markov Model
Shih-Hsiang Lin
 

Similar to One way to see higher dimensional surface (20)

Pattern6 4
Pattern6 4Pattern6 4
Pattern6 4
 
Number theory lecture (part 1)
Number theory lecture (part 1)Number theory lecture (part 1)
Number theory lecture (part 1)
 
Formulario de matematicas
Formulario de matematicasFormulario de matematicas
Formulario de matematicas
 
1_Asymptotic_Notation_pptx.pptx
1_Asymptotic_Notation_pptx.pptx1_Asymptotic_Notation_pptx.pptx
1_Asymptotic_Notation_pptx.pptx
 
AsymptoticAnalysis.ppt
AsymptoticAnalysis.pptAsymptoticAnalysis.ppt
AsymptoticAnalysis.ppt
 
Datamining 7th kmeans
Datamining 7th kmeansDatamining 7th kmeans
Datamining 7th kmeans
 
A family of implicit higher order methods for the numerical integration of se...
A family of implicit higher order methods for the numerical integration of se...A family of implicit higher order methods for the numerical integration of se...
A family of implicit higher order methods for the numerical integration of se...
 
Number theory lecture (part 2)
Number theory lecture (part 2)Number theory lecture (part 2)
Number theory lecture (part 2)
 
Bessel functionsoffractionalorder1
Bessel functionsoffractionalorder1Bessel functionsoffractionalorder1
Bessel functionsoffractionalorder1
 
Dual Gravitons in AdS4/CFT3 and the Holographic Cotton Tensor
Dual Gravitons in AdS4/CFT3 and the Holographic Cotton TensorDual Gravitons in AdS4/CFT3 and the Holographic Cotton Tensor
Dual Gravitons in AdS4/CFT3 and the Holographic Cotton Tensor
 
An Introduction to Hidden Markov Model
An Introduction to Hidden Markov ModelAn Introduction to Hidden Markov Model
An Introduction to Hidden Markov Model
 
Lesson 7: Vector-valued functions
Lesson 7: Vector-valued functionsLesson 7: Vector-valued functions
Lesson 7: Vector-valued functions
 
Hw5sols
Hw5solsHw5sols
Hw5sols
 
Dsp lecture vol 2 dft & fft
Dsp lecture vol 2 dft & fftDsp lecture vol 2 dft & fft
Dsp lecture vol 2 dft & fft
 
S 7
S 7S 7
S 7
 
Sol7
Sol7Sol7
Sol7
 
rinko2011-agh
rinko2011-aghrinko2011-agh
rinko2011-agh
 
Chapter 2 sequencess and series
Chapter 2 sequencess and seriesChapter 2 sequencess and series
Chapter 2 sequencess and series
 
Math report
Math reportMath report
Math report
 
Asymptotic analysis
Asymptotic analysisAsymptotic analysis
Asymptotic analysis
 

More from Kenta Oono

提供AMIについて
提供AMIについて提供AMIについて
提供AMIについて
Kenta Oono
 

More from Kenta Oono (20)

Minimax statistical learning with Wasserstein distances (NeurIPS2018 Reading ...
Minimax statistical learning with Wasserstein distances (NeurIPS2018 Reading ...Minimax statistical learning with Wasserstein distances (NeurIPS2018 Reading ...
Minimax statistical learning with Wasserstein distances (NeurIPS2018 Reading ...
 
Deep learning for molecules, introduction to chainer chemistry
Deep learning for molecules, introduction to chainer chemistryDeep learning for molecules, introduction to chainer chemistry
Deep learning for molecules, introduction to chainer chemistry
 
Overview of Machine Learning for Molecules and Materials Workshop @ NIPS2017
Overview of Machine Learning for Molecules and Materials Workshop @ NIPS2017Overview of Machine Learning for Molecules and Materials Workshop @ NIPS2017
Overview of Machine Learning for Molecules and Materials Workshop @ NIPS2017
 
Comparison of deep learning frameworks from a viewpoint of double backpropaga...
Comparison of deep learning frameworks from a viewpoint of double backpropaga...Comparison of deep learning frameworks from a viewpoint of double backpropaga...
Comparison of deep learning frameworks from a viewpoint of double backpropaga...
 
深層学習フレームワーク概要とChainerの事例紹介
深層学習フレームワーク概要とChainerの事例紹介深層学習フレームワーク概要とChainerの事例紹介
深層学習フレームワーク概要とChainerの事例紹介
 
20170422 数学カフェ Part2
20170422 数学カフェ Part220170422 数学カフェ Part2
20170422 数学カフェ Part2
 
20170422 数学カフェ Part1
20170422 数学カフェ Part120170422 数学カフェ Part1
20170422 数学カフェ Part1
 
情報幾何学の基礎、第7章発表ノート
情報幾何学の基礎、第7章発表ノート情報幾何学の基礎、第7章発表ノート
情報幾何学の基礎、第7章発表ノート
 
GTC Japan 2016 Chainer feature introduction
GTC Japan 2016 Chainer feature introductionGTC Japan 2016 Chainer feature introduction
GTC Japan 2016 Chainer feature introduction
 
On the benchmark of Chainer
On the benchmark of ChainerOn the benchmark of Chainer
On the benchmark of Chainer
 
Tokyo Webmining Talk1
Tokyo Webmining Talk1Tokyo Webmining Talk1
Tokyo Webmining Talk1
 
VAE-type Deep Generative Models
VAE-type Deep Generative ModelsVAE-type Deep Generative Models
VAE-type Deep Generative Models
 
Common Design of Deep Learning Frameworks
Common Design of Deep Learning FrameworksCommon Design of Deep Learning Frameworks
Common Design of Deep Learning Frameworks
 
Introduction to Chainer and CuPy
Introduction to Chainer and CuPyIntroduction to Chainer and CuPy
Introduction to Chainer and CuPy
 
Stochastic Gradient MCMC
Stochastic Gradient MCMCStochastic Gradient MCMC
Stochastic Gradient MCMC
 
Chainer Contribution Guide
Chainer Contribution GuideChainer Contribution Guide
Chainer Contribution Guide
 
2015年9月18日 (GTC Japan 2015) 深層学習フレームワークChainerの導入と化合物活性予測への応用
2015年9月18日 (GTC Japan 2015) 深層学習フレームワークChainerの導入と化合物活性予測への応用 2015年9月18日 (GTC Japan 2015) 深層学習フレームワークChainerの導入と化合物活性予測への応用
2015年9月18日 (GTC Japan 2015) 深層学習フレームワークChainerの導入と化合物活性予測への応用
 
Introduction to Chainer (LL Ring Recursive)
Introduction to Chainer (LL Ring Recursive)Introduction to Chainer (LL Ring Recursive)
Introduction to Chainer (LL Ring Recursive)
 
日本神経回路学会セミナー「DeepLearningを使ってみよう!」資料
日本神経回路学会セミナー「DeepLearningを使ってみよう!」資料日本神経回路学会セミナー「DeepLearningを使ってみよう!」資料
日本神経回路学会セミナー「DeepLearningを使ってみよう!」資料
 
提供AMIについて
提供AMIについて提供AMIについて
提供AMIについて
 

One way to see higher dimensional surface

  • 1. PFI 2011 3 3
  • 2. • GLn (R), GLn (C) • GLn (R), O(n) {( ) } a, b • SL2 (R) = |ab − cd = 1 c, d • R4 ab − cd = 1
  • 3. • Mn (R) : n • Mn (R) : n • GLn (R) : = {g ∈ Mn (R) | detg ̸= 0} • SLn (R) = {g ∈ Mn (R) | detg = 1} • SLn (C) = {g ∈ Mn (C) | detg = 1} • O(n) : = {g ∈ Mn (R) | gt g = In } • SO(n) : = {g ∈ Mn (R) | gt g = In , detg = 1} • U(n) : = {g ∈ Mn (C) | g∗ g = In } • SU(n) : = {g ∈ Mn (C) | g∗ g = In , detg = 1}
  • 4. 2 • Mn (R) Rn 2 • Mn (C) Cn 2 2 • → Rn , Cn •
  • 5. { } ∑ n+1 n • S = 1 n+1 (x , . . . , x )∈R n+1 | i 2 (x ) = 1 i=1
  • 6. 1 {( )} cos θ sin θ • SO(2) = = S1 − sin θ cos θ { } ∑ n+1 • O(n + 1)/O(n) = S = n 1 n+1 (x , . . . x )| i 2 (x ) = 1 i=1   0  . • O(n) ∋ A →   A . .  ∈ O(n + 1) O(n)  0 0 ··· 0 1 O(n + 1) • O(3, R) R3 /O(2, R) {(0, 0, z)} = S1 × R • • SL2 (R) = S1 × R2
  • 7. 2(1/2) • H = {a + bi + cj + dk|a, b, c, d ∈ R} • i = j = k2 = −1, ij = k, jk = i, ki = j, ji = −k, 2 2 kj = −i, ik = −j • (e.g. ij ̸= ji) • z = a + bi + cj + dk ∈ H • ¯ = a − bi − cj − dk z • |z|2 = z¯ z • S3 = {(a, b, c, d) ∈ R4 | z = a + bi + cj + dk |z| = 1}
  • 8. 2(2/2) • ( ) a + bi c + di • z = a + bi + cj + dk ↔ −c + di −a + bi • → S3 H SU(2) Figure:
  • 9. • S3 4 → Figure: Dimensions
  • 10. 1 • f : R2 → R f(x, y) = x2 − y3 • f −1 (0) : x = 0 • {(x, y) ∈ R2 |x2/3 + y2/3 = 1} • http://www-history.mcs.st-and.ac.uk/Curves/Curves.html
  • 11. f : Rr → R a ∈ Rr ∂f (a)(1 ≤ i ≤ r) 0 ∂xi f −1 (f(a))
  • 12. 1 • f : R2 → R f(x, y) = x2 − y2 • ∂x f = 2x, ∂y f = −2y • ∂x f = ∂y f = 0 (x, y) = (0, 0) • f −1 (0) : 2 • f −1 (c) (c ̸= 0) :
  • 13. 2 • det : Mn (R) → R ; g → detg • SLn (R) = det−1 (1) SLn (R)
  • 14. 2 • • (Rn or Cn ) R R • S1 = {(x, y) ∈ R2 | x2 + y2 = 1}, • D = {(x, y) ∈ R2 | y ≥ 0}
  • 15. n • f : Mn (R) → R ; g → Tr(t gg) f(g) = 2 gij i,j=1 • g ∈ O(n) −1 f(g) = n √ O(n) ⊂ f (n) O(n) Mn (R) n
  • 16. • O(p, q) = {g ∈ Mn (R) | t gIp,q g = Ip,q }(p + q = n) q>0   1  ..   .     1  • Ip,q =     −1   ..   .  {( ) −1 } cosh θ sinh θ • O(1, 1) = ∈ R |0 ≤ θ < 2π 4 sinh θ cosh θ • O(3, 1)
  • 17. 3 • Figure:
  • 18. 1 • On (R) −1 1 (= SO(n)) • π0 (On (R)) = Z2 • dimZ π0 (X) = #{X/ ∼} • x∼y⇔x y
  • 19. (2/2) • g g′ On (R) g g′ c c • c : [−1, 1] → On (R) ; c(−1) = g, c(1) = g′ • det ◦ c • (det ◦ c)(−1) = det(g) = −1 • (det ◦ c)(1) = det(g′ ) = 1 • (det ◦ c)(a) = 0 a ∈ (−1, 1) • c(a) ̸∈ On (R) c
  • 20. 2 Figure: • π0 ( ) = Z2
  • 21. 4 •
  • 22. • π1 (X, x0 ) = {f : [0, 1] → X|f(0) = f(1) = x0 }/ ∼ • f∼g⇔f g • • http://www.slideshare.net/KentaOono/pf-iseminar • π1 (X, x0 ) = 0 ⇒ 1
  • 23. X 1 1 I→X p = I(0) q = I(1) ··· • www.math.sci.hokudai.ac.jp/ ˜ ishikawa/isoukika/isoukika5.pdf
  • 24. • πn (X, x0 ) = {f : [0, 1]n → X|f(a) = x0 ∀a ∈ ∂([0, 1]n )}/ ∼ • f∼g↔f g
  • 25. • • :π1 (SOn (R)) = Z/2Z = {0, 1} (if n ≥ 3)
  • 26. (1/2) • su(n) = = {X ∈ Mn (C)|X + ∗ X = 0} • su(2) 3 R3 • g ∈ SU(2), X ∈ su(2) Adg (X) = gXg−1 gXg−1 ∈ su(2) • Adg su(2) • Adg ∈ SO(3) • SU(2) → SO(3) ; g → Adg • 2 1 X = Adg g X 2
  • 28. (dimR Mn (R) = n2 ) • • (Sn ) • X → πn (X), Hn (X) • • C∞ (M) = {f : M → R|f } • f:M→R • • f : [−1, 1] → M = • f : S1 → M, =