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ベイズ深層学習(2.2~2.4)
「ベイズ深層学習」(著 須山 敦志)2.2節から2.4節のスライド
RNNの逆伝播の計算は以下でまとめています.
https://drive.google.com/file/d/1QkGb-Ra_E5PRSzq2YOzhax524GtBIRgO/view?usp=sharing
catla
January 17, 2020
Transcript
27 slides
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ॱܕχϡʔϥϧωοτϫʔΫ ͷॱܕχϡʔϥϧωοτҎԼͷΑ͏ʹϞσϧԽग़དྷΔɽ yn = W(2)Φ(W(1)xn ) + ϵn yn ∈
ℝD xn ∈ ℝH0 ϵn ∈ ℝD W(1) ∈ ℝH1 ×H0 W(2) ∈ ℝD×H1 Φ( ⋅ ) ೖྗɿ ϥϕϧɿ ϊΠζɿ ͷύϥϝʔλɿ ͷύϥϝʔλɿ ඇઢܗؔ׆ੑԽؔɿ z = Φ(W(1)xn ) ͱ͢Δͨ͠ͱ͖ɼ ɹ ΛӅΕϢχοτͱݺͿɽ·ͨɼ ӅΕϢχοτೖྗʹରͯ͠૯Λऔͬͨ ͷΛ׆ੑͱ͍͏ɽ zn,h1 ∈ ℝ -
ॱܕχϡʔϥϧωοτϫʔΫ Φ( ⋅ ) ಛʹ׆ੑԽؔͱݺΕɼओʹඇઢܗ͕ؔ༻͍ΒΕΔɽ ʲ׆ੑԽؔͷհʳ wγάϞΠυؔ wۂઢਖ਼ؔ 5BOI
wྦྷੵؔ wΨεͷޡࠩؔ wϥϯϓؔʢ3F-6ʣ wࢦઢܗؔʢ&-6ʣ wFUDʜ -
ॱܕχϡʔϥϧωοτϫʔΫ ීวੑఆཧʢVOJWFSTBMBQQSPYJNBUJPOUIFPSFNʣ ɹʢӅΕ͕ͭʣͷॱܕχϡʔϥϧωοτʹ͓͍ͯɼӅΕϢχοτͷ Λ૿͢͜ͱʹΑͬͯɼҙͷ࿈ଓؔʹۙࣅͰ͖Δɽ yn = W(L)Φ(W(L−1)⋯Φ(W(1)xn )⋯) + ϵn
ෳΛͭॱܕχϡʔϥϧωοτϫʔΫ ɹ͕Ҏ্ͱͳΔΑ͏ͳਂ͍ωοτϫʔΫߏΛͭϞσϧΛҰൠతʹਂֶशͱ ݺͿɽʢຊॻͰɼͷχϡʔϥϧωοτϫʔΫਂֶशϞσϧͷͭͱͯ͠ߟ͑Δɽʣ ͷχϡʔϥϧωοτʹ͓͍ͯɼ ͷཁૉΛશͯ ʹݻఆ͢ΔͱɼϞσϧҰൠઢܗϞσ ϧʢ(-.ʣͱҰக͢Δɽ͜ͷ߹ɼϦϯΫؔ׆ੑԽؔͷٯؔʹ֘ɽ ͭ·ΓɼଟߏΛ࣋ͭχϡʔϥϧωοτϫʔΫϞσϧɼ(-.ʹ͓͚Δඇઢܗͳɹ ɹมΛ܁Γฦ͠ద༻ͨ͠ϞσϧͱղऍՄೳɽ W(2) 1 ⟹ -
ޯ߱Լ๏ͱχϡʔτϯɾϥϑιϯ๏ ɹॱܕχϡʔϥϧωοτϫʔΫɼඇઢܗؔͷதʹֶशରͷύϥϝʔλ͕͋Δ͜ͱ Ͱղੳతʹ࠷খղ͕ٻ·Βͳ͍ɽ w w w w w w w
w ղܾࡦ ɹܭࢉػΛ༻ͯ͠ɼతʹ࠷খΛٻΊΔ࠷దԽख๏Λಋೖɽ ࠷Α͘ΘΕΔ࠷దԽख๏͕ɹޯ߱Լ๏ɹ ࣍ݩͷύϥϝʔλ ΛͭϞσϧʹର͢ΔޡࠩؔΛ ͱ͠ɼޯΛҎԼͱ͢Δɽ M w E(w) ∇w E(w) = ∂E(w) ∂w = ( ∂E(w) ∂w1 , ∂E(w) ∂w2 , …, ∂E(w) ∂wM ) T ͜Εɼޡ͕ࠩؔϢʔΫϦουڑͷۙͰ࠷ٸʹ૿Ճ͢ΔํੑΛද͍ͯ͠Δɽ ޯ߱Լ๏Ͱɼύϥϝʔλ ʹରͯ͠దͳॳظΛ༩͑ɼޯͱٯํʹύϥϝʔλΛ ಈ͔͢͜ͱΛ܁Γฦͯ͠࠷దԽΛߦ͏ɽ w wnew = wold − α∇w E(w)| w=wold (α > 0) -
ޯ߱Լ๏ͱχϡʔτϯɾϥϑιϯ๏ wnew = wold − α∇w E(w)| w=wold ֶशͱݺΕɼճͷߋ৽ͰύϥϝʔλΛಈ͔͢ྔΛࢦఆ͢Δɽ
α ֶशͷτϨʔυΦϑ ֶश େ͖͍ খ͍͞ ֶशͷ ऩଋͷ҆ఆੑ ◯ × × ◯ -
ޯ߱Լ๏ͱχϡʔτϯɾϥϑιϯ๏ ύϥϝʔλ ͕ଟ͘ͳ͍߹ɼޡࠩؔͷ֊ඍΛར༻ͯ͠࠷దԽΛޮԽ͢Δ͜ͱ Մೳɽ ɹχϡʔτϯɾϥϑιϯ๏ʢ/FXUPO3BQITPONFUIPEʣ͕දྫɽ M → ࠷খԽ͍ͨ͠ޡࠩؔΛ͋Δ ·ΘΓͷςʔϥʔల։ʹΑΓೋ࣍ۙࣅ͢Δͱɼ ¯
w E(w) ≈ ˜ E(w) = E( ¯ w) + ∇w E(w)|T w= ¯ w (w − ¯ w) + 1 2 (w − ¯ w)T ∇2 w E(w)| w= ¯ w (w − ¯ w) ޡࠩؔ ʹର͢Δϔοηߦྻʢ)FTTJBONBUSJYʣͰ͋Δɽ͢ͳΘͪɼରশߦྻɽ ∇2 w E E H = ∇2 w E(w) = ∂2E(w) ∂w2 1 ⋯ ∂2E(w) ∂w1 ∂wM ⋮ ⋱ ⋮ ∂2E(w) ∂wM ∂w1 ⋯ ∂2E(w) ∂w1 ∂w2 M -
ޯ߱Լ๏ͱχϡʔτϯɾϥϑιϯ๏ E(w) ≈ ˜ E(w) = E( ¯ w) +
∇w E(w)|T w= ¯ w (w − ¯ w) + 1 2 (w − ¯ w)T ∇2 w E(w)| w= ¯ w (w − ¯ w) ˜ E(w) = E( ¯ w) + ∇w E(w)|T w= ¯ w (w − ¯ w) + 1 2 (w − ¯ w)T ∇2 w E(w)| w= ¯ w (w − ¯ w) ɼ ͱ͠ɼ ͷޯ Λܭࢉ͢Δͱɼ A = ∇w E(w)| w= ¯ w B = ∇2 w E(w)| w= ¯ w ˜ E(w) ∇ ˜ E(w) = ∂ ˜ E(w) ∂w ˜ E(w) = E( ¯ w) + AT(w − ¯ w) + 1 2 (w − ¯ w)TB(w − ¯ w) ∂ ˜ E(w) ∂w = ∂E( ¯ w) ∂w + ∂AT(w − ¯ w) ∂(w − ¯ w) ⋅ ∂(w − ¯ w) ∂w + 1 2 ⋅ ∂(w − ¯ w)TB(w − ¯ w) ∂(w − ¯ w) ⋅ ∂(w − ¯ w) ∂w ͕ରশߦྻͷͱ͖ɼ A ∂xTAx ∂x = 2Ax = 0 + (AT)T + 1 2 ⋅ 2B(w − ¯ w) = A + B(w − ¯ w) ͜ΕΛ ͱ͓͍ͯ ʹؔͯ͠ղ͚ɼ ∇ ˜ E(w) = 0 w ∇ ˜ E(w) = A + B(w − ¯ w) = A + Bw − B ¯ w = 0 ⇔ Bw = B ¯ w − A w = B−1B ¯ w − B−1A = ¯ w − B−1A ਖ਼ଇߦྻͱԾఆ B w = ¯ w − {∇2 w E(w)| w= ¯ w } −1 ∇w E(w)| w= ¯ w ͕ٻ·Δɽ -
ޯ߱Լ๏ͱχϡʔτϯɾϥϑιϯ๏ ɹʹରͯ͠ɼ ͱ͠ɼ࣍ͷΑ͏ʹ܁Γฦ͠ߋ৽ ͢Δ͜ͱͰ Λ࠷খԽͰ͖Δɽ w = ¯ w
− {∇2 w E(w)| w= ¯ w } −1 ∇w E(w)| w= ¯ w wold = ¯ w E(w) wnew = wold − {∇2 w E(w)| w=wold } −1 ∇w E(w)| w=wold ޯ߱Լ๏ʹΑΔ࠷దԽͱൺΔͱɼ ޯ߱Լ๏ɹɹɹɹɹɹɹɿɹ χϡʔτϯɾϥϑιϯ๏ɹɿɹ wnew = wold −α∇w E(w)| w=wold wnew = wold −{∇2 w E(w)| w=wold } −1 ∇w E(w)| w=wold ޯ߱Լ๏ʹ͓͚Δֶश ͕ϔοηߦྻͷٯߦྻ ʹରԠ͍ͯ͠Δɽ α {∇2 w E(w)| w=wold } −1 -
ޡࠩٯ๏ ɹޡࠩٯ๏ʢFSSPSCBDLQSPQBHBUJPONFUIPEʣͱɼॱܕχϡʔϥϧωοτʹ ͓͍ͯද͞ΕΔֶश๏ͷͭɽ ɹ ͷॱܕχϡʔϥϧωοτϫʔΫΛҎԼͷΑ͏ʹදݱ͢Δɽ L yn = W(L)ϕ(W(L−1)⋯ϕ(W(1)xn )⋯)
+ ϵn yn,d = HL−1 ∑ hL−1 =1 w(L) d,hL−1 ϕ HL−2 ∑ hL−2 =1 w(L−1) hL−1 ,hL−2 ⋯ϕ H0 ∑ h0 =1 w(1) h1 ,h0 xn,h0 + ϵn,d yn,d = a(L) n,d + ϵn,d a(L) n,d = HL−1 ∑ hL−1 =1 w(L) d,hL−1 z(L−1) n,hL−1 z(L−1) n,hL−1 = ϕ(a(L−1) n,hL−1 ) a(l) n,hl = Hl−1 ∑ hl−1 =1 w(l) hl ,hl−1 z(l−1) n,hl−1 z(l−1) n,hl−1 = ϕ(a(l−1) n,hl−1 ) a(1) n,h1 = H0 ∑ h0 =1 w(1) h1 ,h0 z(0) n,h0 z(0) n,h0 = xn,h0 ⋮ ⋮ -
ॱ ٯ ޡࠩٯ๏ ɹઌʹ͋͛ͨχϡʔϥϧωοτͷύϥϝʔλू߹Λ ɼֶशσʔλΛ ͱͨ͠߹ͷ ޡࠩؔΛ ͷΑ͏ʹఆٛ͢Δɽ ʹؔͯ͠ඍ͢Δɽ
W N E(W) = N ∑ n=1 En (W) = N ∑ n=1 ( 1 2 D ∑ d=1 (yn,d − a(L) n,d )2 ) En (W) a(L) n,d = HL−1 ∑ hL−1 =1 w(L) d,hL−1 z(L−1) n,hL−1 = HL−1 ∑ hL−1 =1 w(L) d,hL−1 ϕ(a(L−1) n,hL−1 ) z(L−1) n,hL−1 = ϕ(a(L−1) n,hL−1 ) ∂En ∂w(L) d,hL−1 = ∂En ∂a(L) n,d ∂a(L) n,d ∂w(L) d,hL−1 En (W) = 1 2 D ∑ d=1 (yn,d − a(L) n,d )2 a(L−1) n,hL−1 = HL−2 ∑ hL−2 =1 w(L−1) hL−1 ,hL−2 z(L−2) n,hL−2 = (an,d − yn,d )z(L−1) n,hL−1 = δ(L) n,d z(L−1) n,hL−1 L L − 1 ∂En ∂w(L−1) hL−1,hL−2 = D ∑ d=1 ∂En ∂a(L) n,d ∂a(L) n,d ∂a(L−1) n,hL−1 ∂a(L−1) n,hL−1 ∂w(L−1) hL−1,hL−2 ∂a(L) n,d ∂a(L−1) n,hL−1 = ∂ ∂a(L−1) n,hL−1 ( HL−1 ∑ h=1 w(L) d,h ϕ(a(L−1) n,h ) ) = w(L) d,hL−1 ϕ′(a(L−1) n,hL−1 ) ∂a(L−1) n,hL−1 ∂w(L−1) hL−1,hL−2 = ∂ ∂w(L−1) hL−1,hL−2 HL−2 ∑ h=1 w(L−1) hL−1,h z(L−2) n,h = z(L−2) n,hL−2 = D ∑ d=1 δ(L) n,d (w(L) d,hL−1 ϕ′(a(L−1) n,hL−1 ))z(L−2) n,hL−2 = ϕ′(a(L−1) n,hL−1 ) ( D ∑ d=1 δ(L) n,d w(L) d,hL−1) z(L−2) n,hL−2 = δ(L−1) n,hL−1 z(L−2) n,hL−2 a(l) n,hl = Hl−1 ∑ hl−1 =1 w(l) hl ,hl−1 z(l−1) n,hl−1 z(l) n,hl = ϕ(a(l) n,hl ) l δ(l) n,hl = a(L) n,hl − yn,hl , if l = L ϕ′(a(l) n,hl )∑Hl+1 h=1 δ(l+1) n,h w(l+1) h,hl if l ≠ L ∂En ∂w(l) hl,hl−1 = δ(l) n,hl z(l−1) n,hl−1 ͷಋؔɽ ϕ′ ϕ -
ޡࠩٯ๏ ޡࠩٯ๏ͷΞϧΰϦζϜ ॱɿ E(W) = f(W(L)ϕ(W(L−1)⋯ϕ(W(1)xn )⋯), y) ٯɿ δ(l)
n,hl = a(L) n,hl − yn,hl , if l = L ϕ′(a(l) n,hl )∑Hl+1 h=1 δ(l+1) n,h w(l+1) h,hl if l ≠ L ޯܭࢉɿ ∂En ∂w(l) hl ,hl−1 = δ(l) n,hl z(l−1) n,hl−1 ύϥϝʔλͷߋ৽ɿ wnew = wold − α∇w E(w)| w=wold -
ޡࠩٯ๏ ɹ࣮ࡍͷޯܭࢉɼඍͷ࿈ʢDIBJOSVMFʣΛద༻͍ͯ͠Δ͚ͩɽ ଟ͘ͷ࣮ɼࣗಈඍʢBVUPNBUJDEJ⒎FSFOUJBUJPOʣʹΑΔޯܭࢉ͞Ε͍ͯΔɽ ɹ௨ৗͷॱܕχϡʔϥϧωοτϫʔΫύϥϝʔλ͕ଟ͍ͷͰɼաద߹Λى͜͢ Մೳੑ͕͋Δɽ ɹϦοδճؼͷΑ͏ͳ-ਖ਼ଇԽ߲ͳͲΛಋೖɽ ⟹ J(W) =
E(W)+λΩL2 (W) ɹ Λ࠷খԽ͢ΔΑ͏ʹ࠷దԽΛߦ͏Α͏ʹ͢ΔɽχϡʔϥϧωοτϫʔΫͷͰɼ ͜ΕΛɹॏΈݮਰʢXFJHIUEFDBZʣɹͱݺͿɽ J(W) -
ϔοηߦྻΛར༻ֶͨ͠श ɹύϥϝʔλΛ ͱͨ͠ͱ͖ɼϔοηߦྻͷܭࢉྔ ʹͳΓ ͷ͕େ͖͍ͱ͖ɼ ܭࢉ͕࣌ؒେɽ ɹϔοηߦྻΛޯΛͬͯۙࣅ͢ΔͱߴԽͰ͖Δɽʢ४χϡʔτϯ๏ʣ ྫɿɹ֤ೖྗ ʹର͢Δग़ྗ
ͷޯΛͬͯɼ M O(M2) M ⟹ xn a(L) n H ≈ N ∑ n=1 (∇w a(L) n )(∇w a(L) n )T ˞13.-্רQdʹϔοηߦྻͷߴԽʹ͍ͭͯॻ͔Ε͍ͯΔɽ -
ೋྨ ଟྨ Ϋϥε % ग़ྗͷ࣍ݩ % ग़ྗ ग़ྗʹର͢Δ
׆ੑԽؔ ޡࠩؔ ྨϞσϧͷֶश ɹࠓ·Ͱͷઆ໌ʹ͓͚ΔॱܕχϡʔϥϧωοτϫʔΫճؼʹద༻͞ΕΔɽ Ͱɼ\ࣝผ ྨ^ʹରͯ͠ɾɾɾʁ a(L) n ∈ ℝ a(L) n ∈ ℝD E(W) = − N ∑ n=1 {yn log μ + (1 − yn )log(1 − μn )} γάϞΠυؔɿɹ μn = Sig(a(L) n ) ∈ (0,1) ιϑτϚοΫεؔɿɹ πd (a(L) n ) D ∑ d=1 πd (a(L) n ) = 1 E(W) = − N ∑ n=1 D ∑ d=1 yn,d log πd (a(L) n ) ɹ͜ΕΒͷޡࠩؔɹަࠩΤϯτϩϐʔޡࠩؔɹͱݺΕΔɽ -
֬తޯ߱Լ๏ ɹઌ΄Ͳͷޯ߱Լ๏ͷΑ͏ͳɼͯ͢ͷֶशσʔλΛҰʹ༻ͯ͠ޯΛܭࢉ͢Δํ๏ ɹόονֶशɹͱݺΕΔɽશσʔλΛ͏ͷͰܭࢉޮ͕ѱ͍ɽ ɹαϯϓϧબʹΑͬͯޮԽ ɹֶशσʔλͷೖग़ྗσʔλͷͱͦͷΛͦΕͧΕ ɼ ͱ͢Δɽ ɹαϯϓϧબͰɼֶशσʔλ͔ΒϥϯμϜʹ Λબ͢ΔɽαϯϓϧʹΑͬͯ
બΕͨͷΠϯσοΫεू߹Λ ͱ͢ΔͱɼબΕͨ෦ू߹ɹ ɹ ͱදͤΔɽ ɹޡࠩؔΛɹ ɹͱͯ͠ύϥϝʔλΛߋ৽͢Δɽ ɹ্هͷΑ͏ͳํ๏Λɹ֬తޯ߱Լ๏ɹͱݺͿɽ·ͨɼ෦ू߹ Λɹϛχόονɹ ͱݺͿɽɹҰ༷ͰϥϯμϜબ͞ΕΔͱظόονֶश࣌ͱՁɽ ⟹ N M( < N) = {xn , yn }n∈ E (W) = N M ∑ n∈ En (W) -
֬తޯ߱Լ๏ ɹճͷύϥϝʔλߋ৽ʹ༻͍ΒΕΔֶशΛ ͱͨ͠ͱ͖ɼ ͷͱ͖ɼ֬Ͱϛχόονֶश͕ऩଋ͢Δɽ ɹ͜ΕΛ༻͍ͯɼϛχόονͷֶशʹΑΓσʔλશମʹ͓͚Δଛࣦ͕ؔ࠷খͱͳΔύ ϥϝʔλΛ୳ࡧ͢Δํ๏ΛɹϩϏϯεɾϞϯϩʔΞϧΰϦζϜɹͱ͍͏ɽ i αi
∞ ∑ i=1 αi = ∞, ∞ ∑ i=1 α2 i < ∞ ɹ֬తޯ߱Լ๏ΛޮԽ͢ΔͨΊʹɼύϥϝʔλͷߋ৽ʹϕΫτϧΛಋೖɽ ɹϞʔϝϯλϜ๏ʢNPNFOUVNNFUIPEʣ աڈͷޯͷӨڹΛௐ͢Δύϥϝʔλɽ ⟹ pnew = βpold − α∇w E(w)| w=wold wnew = wold + pnew β ∈ [0,1) -
όονਖ਼ଇԽ ɹόονਖ਼ଇԽʢCBUDIOPSNBMJ[BUJPOʣΛಋೖ͢Δ͜ͱͰɼϊΠζʹΑΔਖ਼ଇԽͱ֤ ͷೖྗͷͷ҆ఆੑΛ্ͤ͞Δ͜ͱͰ࠷దԽͷޮԽΛਤΔɽ ͋ΔӅΕϢχοτͷฏۉ ͱࢄ ΛͦΕͧΕٻΊɼඪ४Խ͢Δɽ ୯७ʹೖྗΛਖ਼نԽͯ͠͠·͏ͱɼදݱྗ͕୯७ͳͷʹ੍ݶ͞ΕΔɽ
ઢܗมΛՃ͑Δ͜ͱͰදݱྗΛҡ࣋ɽ z μ σ2 ˜ z = z − μ σ2 + ϵ (ϵ ≪ 1) ⟹ ˜ z′ = γ˜ z + β (γ ∈ ℝ, β ∈ ℝ) -
ΈࠐΈχϡʔϥϧωοτϫʔΫ ɹΈࠐΈχϡʔϥϧωοτϫʔΫʢ$//ʣΈࠐΈʢDPOWPMVUJPOʣΛऔΓೖΕͨ Ϟσϧɽ࣌ܥྻը૾ʹରͯ͠༗ޮɽ ɹΈࠐΈͷܭࢉɼը૾Λೖྗʹఆͯ͠ ͱ͓͖ ॏΈύϥϝʔλʢϑΟϧλʔʣ Λ ͱͨ͠ͱ͖ɼΈࠐΈޙͷը૾ʢಛϚοϓʣ ͷ ൪ͷཁૉɼ
ɹɹɹɹɹɹɹɹ Ͱද͞ΕΔɽ ॱܕχϡʔϥϧωοτϫʔΫ͕શ݁߹ʹରͯ͠ɼ$//ૄ݁߹Ͱ͋ΔͱݴΘΕ͍ͯΔɽ యܕతͳ$//ͰɼΈࠐΈޙϓʔϦϯάʢFH࠷େϓʔϦϯάʣͱݺΕΔඇઢܗؔ ΛڬΉɽ X ∈ ℝH×W W ∈ ℝM×N S i, j Si,j = (W * X)i,j = ∑ n∈N,m∈M Wm,n Xi+m−1,j+n−1 ʢQͷਤΛࢀরʣ -
࠶ؼܕχϡʔϥϧωοτϫʔΫ ɹ࠶ؼܕχϡʔϥϧωοτϫʔΫʢ3//ʣɼσʔλͷܥྻใΛදݱ͞Εͨχϡʔϥϧ ωοτϫʔΫɽ ɹ࣌ࠁ ʹ͓͚ΔӅΕϢχοτΛ ɼೖྗσʔλΛ ͱͨ͠ͱ͖ɼӅΕϢχοτ ɹɹɹɹ Ͱද͞ΕΔɽ·ͨɼύϥϝʔλ
ϞσϧશମͰڞ༗ɽ ཁૉ͝ͱͷඇઢܗؔɽ ࣌ࠁ ʹ͓͚Δग़ྗ ιϑτϚοΫεؔΛ ͱ͢Δͱɼ Ͱܭࢉ͞ΕΔɽΑͬͯɼϞσϧͷύϥϝʔλू߹Λ ͱ͢Δͱɼ࣌ܥྻશମͷޡࠩɼ [ Ͱܭࢉ͞Εɼ͜ΕΛ࠷খԽ͢ΔΑ͏ʹ࠷దԽΛߦ͏ɽ n zn xn zn = ϕ. (Wzx xn + Wzz zn−1 + bz ) Wzx , Wzz , bz ϕ. n πn π πn = π(Wyz zn + by ) Θ E(Θ) = N ∑ n=1 En (Θ) = N ∑ n=1 ( − D ∑ d=1 yn,d log πn,d) QͷਤΛࢀর -
ࣗݾූ߸Խث ɹࣗݾූ߸ԽثʢBVUPFODPEFSʣɼೖྗσʔλʹର͢ΔϥϕϧΛඞཁͱ͠ͳ͍ ڭࢣͳֶ͠शʢVOTVQFSWJTFEMFBSOJOHʣͷͭɽ ڭࢣ͋ΓֶशTVQFSWJTFEMFBSOJOH ɹූ߸ԽثʢFODPEFSʣͱ෮߸ԽثʢEFDPEFSʣͷͭͷχϡʔϥϧωοτϫʔΫΛ༻͠ɼ ؍ଌσʔλ Λ෮߸Խثʹ௨ͯ͠ɼજࡏม ʹม͢ΔɽҰํɼ෮߸Խثೖྗͱͯ͠ɼ
જࡏม Λ༩͑ɼݩͷσʔλ Λ෮ݩ͢Δ͜ͱ͕తɽ ɹ߃ࣸ૾Λආ͚ΔͨΊʹɼજࡏมʹରͯ͠ਖ਼ଇԽ߲ΛՃ͑ͨΓ͢Δɽ ⟺ X Z Z X X &ODPEFS ̂ X %FDPEFS Z X ≈ ̂ X Ұൠతʹ dim xn > dim zn