Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Sign up for free
Menu
Search
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Pricing
Search
Sign in
Sign up for free
関数等式と双対性
Search
Naoya Umezaki
October 20, 2019
Science
900
1
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
関数等式と双対性
ロマンティック数学ナイトプライム@ゼータでの発表
https://mathparty.localinfo.jp/
Naoya Umezaki
October 20, 2019
More Decks by Naoya Umezaki
See All by Naoya Umezaki
証明支援系LEANに入門しよう
unaoya
2
5.1k
ミケル点とべズーの定理
unaoya
0
1.2k
すうがく徒のつどい@オンライン「ラマヌジャンのデルタ」
unaoya
0
840
合同式と幾何学
unaoya
0
2.3k
すうがく徒のつどい@オンライン「ヴェイユ予想とl進層のフーリエ変換」
unaoya
0
1k
Egisonパターンマッチによる彩色
unaoya
1
690
直交多項式と表現論
unaoya
0
1k
導来代数幾何入門
unaoya
0
1.3k
作図と対称性
unaoya
0
310
Other Decks in Science
See All in Science
データベース02: データベースの概念
trycycle
PRO
2
1.4k
大黒市で発生した大規模インシデント の ポストモーテムから読み解く、 記憶媒体消去の大切さ
shucho0103
0
280
Testing the Longevity Bottleneck Hypothesis
chinson03
0
470
Wet Active Matter
rajeshrinet
0
150
データベース14: B+木 & ハッシュ索引
trycycle
PRO
0
920
GKE上でオセロの強化学習やってみた
akasan
0
110
共生概念の整理と AIアライメントの構想
hiroakihamada
0
310
Physical AIを支えるWeights & Biases
olachinkei
1
590
東北地方における過去20年間の降水量の変化
naokimuroki
1
500
データベース03: 関係データモデル
trycycle
PRO
1
830
ゲームと人工知能
miyayou
0
190
データベース12: 正規化(2/2) - データ従属性に基づく正規化
trycycle
PRO
0
1.7k
Featured
See All Featured
Self-Hosted WebAssembly Runtime for Runtime-Neutral Checkpoint/Restore in Edge–Cloud Continuum
chikuwait
0
790
Performance Is Good for Brains [We Love Speed 2024]
tammyeverts
12
1.8k
Side Projects
sachag
456
43k
What does AI have to do with Human Rights?
axbom
PRO
1
2.4k
How to audit for AI Accessibility on your Front & Back End
davetheseo
0
540
Impact Scores and Hybrid Strategies: The future of link building
tamaranovitovic
0
440
Git: the NoSQL Database
bkeepers
PRO
432
67k
JavaScript: Past, Present, and Future - NDC Porto 2020
reverentgeek
52
6.1k
Automating Front-end Workflow
addyosmani
1369
210k
Abbi's Birthday
coloredviolet
4
10k
Tell your own story through comics
letsgokoyo
1
1.1k
Principles of Awesome APIs and How to Build Them.
keavy
128
18k
Transcript
ؔࣜͱରੑ ക࡚@unaoya 2019 10 ݄ 20 ϩϚϯςΟοΫֶφΠτϓϥΠϜˏθʔλ 1
Riemann ζ ζ(s) = ∞ n=1 n−s = p (1
− p−s)−1 ˆ ζ(s) = π−s/2Γ( s 2 )ζ(s) ͱ͓͘ͱɺؔࣜ ˆ ζ(s) = ˆ ζ(1 − s) ཱ͕ɻFourier มʢPoisson ެࣜʣΛ༻͍ͯࣔͤΔɻ 2
Dirichlet L ಋख f ͷ Dirhchlet ࢦඪ χ : Z
→ C χ(nm) = χ(n)χ(m)ɺn ͕ f ͱޓ͍ʹૉͳΒ χ(n) = 0ɻ Legendre ه߸ͳͲ͕ྫɻ L(χ, s) = ∞ n=1 χ(n)n−s = p (1 − χ(p)p−s)−1 શͯͷ n Ͱ χ(n) = 1 ͱ͢Δͱ Riemann ζ L(1, s) = ∞ n=1 n−s = p (1 − p−s)−1 3
ؔࣜ ˆ L(χ, s) = f s/2 χ Γ(χ, s)L(s,
χ) ͱ͢Δɻf1 = 1, Γ(s, 1) = π−s/2Γ(s) Ͱ͋Δɻ ˆ L(χ, 1 − s) = W (χ)ˆ L(χ, s) ิਖ਼߲ W (χ) ͕ଘࡏ͢ΔɻFourier มʢPoisson ެࣜʣΛ༻͍ ͯࣔͤΔɻ 4
Dedekind ζ ମ K ʹରͯ͠ɺ ζK (s) = a (NK/Qa)−s
= p (1 − (NK/Qp)−s)−1 K = Q ͷ࣌ɺNQ/Q(p) = p ͳͷͰ ζK (s) = ζ(s) ͱͳΔɻ 5
ؔࣜ ˆ ζK (s) = |DK |s/2ΓK (s)ζK (x) ͱ͢ΔɻDK
K ͷผࣜͰ DQ = 1ɻΓQ(s) = π−s/2Γ( s 2 ) Ͱ ͋Δɻ ˆ ζK (s) = ˆ ζK (1 − s) 6
Hecke L ಋख f ͷ Hecke ࢦඪ χ : AK
→ C×ɻ͜Εͷಛผͳ߹͕ Dirichlet ࢦඪɻ L(χ, s) = p (1 − χ(πp)N(p)−s)−1 ʢѱ͍ૉͰमਖ਼͢Δɻ ʣ 7
ؔࣜ ˆ L(χ, s) = |DK |s/2f s/2 χ Γ(χ,
s)L(χ, s) ͱ͢Δͱɺؔࣜ ˆ L(χ, s) = W (χ)ˆ L(χ, 1 − s) Λຬͨ͢ɻΞσʔϧ্ͷ Fourier มΛ༻͍ͯࣔ͢ɻ 8
߹ಉ ζ ༗ݶମ্ͷଟ༷ମ X/Fq ͍͍ͩͨଟ߲ࣜ f = 0 Ͱఆ·Δਤܗɻ ͜Εͷղͷݸ
|X(Fqm )| Λ͑Δ͜ͱͰɺ Z(X, t) = exp ∞ m=1 |X(Fqm )|tm m ΛఆΊΔɻ d dt log(Z(X, t)) = m |X(Fqm )|tm Ͱ͋Δɻ ζX (s) = x∈X (1 − (Nx)−s)−1 = Z(X, q−s) ͱදࣔͰ͖Δɻ 9
ؔࣜ X ͷίϗϞϩδʔ Hi (X) ͷ Lefschetz ެࣜʹΑΓɺFrobenius ࡞ ༻ͷݻ༗ଟ߲ࣜΛ༻͍ͯ
Z(X, t) Λهड़Ͱ͖Δɻ Z(X, t) = det(1 − Frobt | H1(X)) · · · det(1 − Frobt | H2n−1(X)) det(1 − Frobt | H0(X)) · · · det(1 − Frobt | H2n(X)) ؔࣜ Z(X, 1 qnt ) = ±qnχ(X)/2tχ(X)Z(X, t) ζX (n − s) = ±qnχ(X)/2−χ(X)sζX (s) ཱ͕ɻίϗϞϩδʔͷ Poincare ରੑɻ 10
Hasse-Weil ζ ମ K ্ͷଟ༷ମ X ʹର͠ɺͦͷ i ࣍෦ Hi
(X) ʹରͯ͠ L(Hi (X), s) = p det(1 − Frobpp−s | Hi (X))−1 ʢѱ͍ૉͰमਖ਼͢Δɻ ʣ ˆ L(Hi (X), s) = Ns/2Γ(Hi (X), s)L(Hi (X), s) 11
ؔࣜ ؔࣜʢ༧ʣ ˆ L(Hi (X), s) = ±ˆ L(Hi (X),
i + 1 − s) Q ্ͷପԁۂઢ E Ͱ Wiles ͳͲʹΑΓূ໌͞Εͨɻ อܕܗࣜ fE Ͱ͋ͬͯ L ͕ؔҰக͢ΔͷΛ࡞Δɻอܕܗࣜ fE ͷ L ؔͷؔࣜ Hecke ͳͲʹΑΓ Fourier มͳͲΛ༻͍ ͯূ໌͞Ε͍ͯͨɻ 12
ℓ ਐͷ L X ͕༗ݶମ্ͷଟ༷ମɺF Λ ℓ ਐͱ͢Δɻ L(X, F,
t) = x det(1 − tdeg(x)Fx , F¯ x )−1 = det(1 − Frobt | H1(X, F)) · · · det(1 − Frobt | H2n−1(X, F)) det(1 − Frobt | H0(X, F)) · · · det(1 − Frobt | H2n(X, F)) F ͕ఆ Λ ͷͱ͖ɺ߹ಉθʔλɻ ۂઢ X ্ͷ f : Y → X ʹରͯ͠ɺF = Hi (Yx ) ℓ ਐͷྫɻ ؔࣜ L(X, F, t) = ε(X, F)t−χ(X,F)L(X, D(F), t−1) 13
ذͱ ε Ҽࢠ ѱ͍ૉͰͷ༷ࢠɺผࣜɺಋखɺؔࣜʹݱΕΔิਖ਼߲ͳͲ ͷใ͕ॏཁɻ ʢෆมྔͱͯ͠ڧྗɻ ʣ ذͷزԿతͳෆมྔͱͯ͠ಛੑαΠΫϧͱ͍͏ͷ͕͋Δɻಛ ੑαΠΫϧݩʑඍํఔࣜʢD Ճ܈ʣͷཧͰߟ͑ΒΕͨ
ͷͰɺذͷ༷ࢠΛهड़͢Δɻ ؔࣜͷ ε(X, F) ͱಛੑαΠΫϧͷؔ ఆཧ (U.-Yang-Zhao) det ρ(−ccX F) = ε(X, F ⊗ ρ) ε(X, F)dim ρ 14