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非線形最適化の基礎〜KKT条件〜
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miruca
March 19, 2019
Science
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4.8k
非線形最適化の基礎〜KKT条件〜
非線形最適化問題に対する最も代表的な最適性の必要条件(KKT条件)に関するスライド
miruca
March 19, 2019
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Transcript
ඇઢܗ࠷దԽͷجૅ – KKT condition – miruca Graduate School of Informatics,
Kyoto University March 19, 2019
͜ͷεϥΠυͷత ʰඇઢܗ࠷దԽͷجૅʱ(ౡ, 2001) ͷୈ 3 ষʹؔͯ͠ • ๏ઢਲ਼Λ༻͍ͨ࠷దੑ݅ʹ͍ͭͯཧղ͢Δ • ෆ੍ࣜΛؚΉ࠷దԽʹର͢Δ
KKT ݅Λཧղ͢Δ • KKT ݅ͷԾఆͰ͋Δ੍ఆʹ͍ͭͯཧղ͢Δ ˞ҙ • ຊεϥΠυͷఆཧͷ൪߸ʰඇઢܗ࠷దԽͷجૅʱʹ४ͣΔ • ਤͳ͍ͷͰదٓखΛಈ͔͠ͳ͕Βཧղ͢Δ͜ͱΛਪ • (ԋश) ͱॻ͍ͨͷʹղΛͨ͠
ਲ਼ͱ࠷దੑ݅ KKT ݅ ੍ఆ Today’s Topic 1. ਲ਼ͱ࠷దੑ݅ 2. KKT
݅ 3. ੍ఆ 3 / 21
1. ਲ਼ͱ࠷దੑ݅ 2. KKT ݅ 3. ੍ఆ
ਲ਼ͱ࠷దੑ݅ KKT ݅ ੍ఆ ࠷దԽ ࣍ͷ࠷దԽΛߟ͑Δɿ minimize x∈Rn f(x) subject
to x ∈ S. (1) ͜͜ʹɼؔ f : Rn → R ͱू߹ S ⫅ Rn ॴ༩Ͱ͋Δɽ • ੍݅ x ∈ S Λຬͨ͢ϕΫτϧ x Λ࣮ޮՄೳղͱ͍͍ɼ࣮ ޮՄೳղશମͷू߹Λ࣮ޮՄೳྖҬͱ͍͏ɽ • S = Rn ͷ߹ɼ (1) ੍ͳ͠࠷దԽͱݺΕΔɽ • ؔ f ͕ತؔͰɼू߹ S ͕ತू߹Ͱ͋Δͱ͖ɼ (1) ತ ܭը (convex programming problem) ͱݺΕΔɽ 5 / 21
ਲ਼ͱ࠷దੑ݅ KKT ݅ ੍ఆ ࠷దղͷछྨ • ࣮ޮՄೳղ x ∈ S
ʹରͯ͠ɼ͋Δ ε > 0 ͕ଘࡏͯ͠ f(x) ≧ f(x) (∀x ∈ S ∩ B(x, ε)) (2) ཱ͕͢Δͱ͖ɼx Λ (1) ͷہॴత࠷దղͱ͍͏ *1)ɽ • ҙͷ ε > 0 ʹରͯࣜ͠ (2) ཱ͕͢Δɼ͢ͳΘͪ f(x) ≧ f(x⋆) (∀x ∈ ε) (3) Ͱ͋Δͱ͖ɼx⋆ ΛେҬత࠷దղͱ͍͏ɽ ˞ େҬత࠷దղ ⇒ ہॴత࠷దղ (ٯඞͣ͠Γཱͨͳ͍) *1)த৺͕ x ∈ Rn Ͱܘ͕ r > 0 ͷٿΛ B(x, r) = {y ∈ Rn | ∥y − x∥ < r} ͱॻ͖ɼ ։ٿͱݺͿɽ 6 / 21
ਲ਼ͱ࠷దੑ݅ KKT ݅ ੍ఆ ತܭըʹ͓͚Δ࠷దղ ʮہॴత࠷దղ ⇒ େҬత࠷దղʯΛอূͰ͖Δ߹͕͋Δɽ ఆཧ 3.1
࠷దԽ (1) ʹ͓͍ͯɼf ತؔɼS ತू߹ͱ͢Δɽͦͷͱ ͖ɼ (1) ͷҙͷہॴత࠷దղେҬత࠷దղͰ͋Δɽ • ূ໌ɼہॴత࠷దղͰ͋Δ͕େҬత࠷దղͰͳ͍Α͏ͳ x ∈ S ͷଘࡏੑΛԾఆͯ͠ໃ६Λಋ͚Α͍ɽ(ԋश) • ࠷దղશମͷू߹͕ತू߹Ͱ͋Δ͜ͱࣔ͢͜ͱ͕Ͱ͖Δɽ • ತܭըͰͳ͍߹ɼҰൠʹ͍ͭ͘ͷہॴత࠷దղ͕ଘࡏ ͢ΔͷͰɽେҬత࠷దղΛٻղ͢Δ͜ͱࠔͰ͋Δɽ → ತੑΛԾఆ͠ͳ͍ʹ͓͍ͯɼہॴత࠷దղ͕ղੳͷରͱ ͳΔ߹͕΄ͱΜͲͰ͋Δɽ 7 / 21
ਲ਼ͱ࠷దੑ݅ KKT ݅ ੍ఆ ਲ਼ (1) ʹର͢Δ࠷దੑ݅Λಋͨ͘ΊʹඞཁͱͳΔ֓೦Λड़Δɽ ఆٛ: ਲ਼
(tangent cone) x ∈ S ʹऩଋ͢Δྻ {xk} ⫅ S Λߟ͑Δɽ͜ͷͱ͖ɼ͋Δඇෛ ྻ {αk} Λ༻͍ͯఆٛ͞ΕΔྻ {αk(xk − x)} ͕ऩଋ͠ɼͦͷ ۃݶ͕ y ∈ Rn ͱͳΔͱ͖ɼy Λू߹ S ͷ x ʹ͓͚ΔϕΫτϧ (tangent vector) ͱݺͿɽ·ͨɼS ͷ x ʹ͓͚ΔϕΫτϧશମ ͷू߹Λ Ts(x) ͱද͠ɼू߹ S ͷ x ʹ͓͚Δਲ਼ (tangent cone) ͱݺͿɽ • ਲ਼ Ts(x) ྻΛ༻͍ͯ࣍ͷΑ͏ʹදݱ͞ΕΔ: Ts(x) := { y ∈ Rn | lim k→∞ αk(xk − x) = y, lim k→∞ xk = x, xk ∈ S, αk ≧ 0 (k = 1, 2, . . .) } . 8 / 21
ਲ਼ͱ࠷దੑ݅ KKT ݅ ੍ఆ ๏ઢਲ਼ ਲ਼ͷۃਲ਼ʹ͍ͭͯߟ͑Δɽ ఆٛ: ๏ઢਲ਼ (normal cone)
ਲ਼ Ts(x) ͷۃਲ਼ Ts(x)⋆ Λ S ͷ x ʹ͓͚Δ๏ઢਲ਼ (normal cone) ͱݺͼɼNs(x) ͱද͢ɽNs(x) ʹଐ͢ΔϕΫτϧΛ x ʹ͓͚Δ S ͷ๏ઢϕΫτϧ (normal vector) ͱ͍͏ɽ • ๏ઢਲ਼࣍ͷΑ͏ʹදݱ͞ΕΔ: Ns(x) = {z ∈ Rn | ⟨z, y⟩ ≦ 0 (∀y ∈ Ts(x))} (4) • ಛʹɼू߹ S ͕ತू߹Ͱ͋Δͱ͖࣍ͷΑ͏ʹදݱ͞ΕΔ: Ns(x) = {z ∈ Rn | ⟨z, x − x⟩ ≦ 0 (∀x ∈ S)} (5) • ๏ઢਲ਼ Ns(x) ۭͰͳ͍ดತਲ਼Ͱ͋Δ *2)ɽ *2)ҙͷਲ਼ C ʹର͢Δۃਲ਼ C⋆ ดತਲ਼Ͱ͋ΔͨΊ (ఆཧ 2.12)ɽ 9 / 21
ਲ਼ͱ࠷దੑ݅ KKT ݅ ੍ఆ ࠷దੑ݅ ๏ઢਲ਼Λ༻͍Δ͜ͱʹΑΓɼ (1) ʹର͢Δ࠷جຊతͳ࠷దੑ ݅Λ༩͑Δ͜ͱ͕Ͱ͖Δɽ ఆཧ
3.3 ؔ f : Rn → R x ∈ S ʹ͓͍ͯඍՄೳͱ͢Δɽͦͷͱ͖ɼ x ͕ (1) ͷہॴత࠷దղͳΒ࣍ͷ͕ؔΓཱͭɿ − ∇f(x) ∈ Ns(x). (6) • ࣜ (6) Λຬͨ͢ (1) ͷఀཹ (stationary point) ͱݺ ΕΔɽ • ࣜ (6) x ͕ (1) ͷہॴత࠷దղͰ͋ΔͨΊͷඞཁ݅ Ͱ͋Δ͕े݅Ͱͳ͍ɽ • ತܭըͷ߹ɼࣜ (6) ͕࠷దੑͷඞཁे݅ͱͳΔɽ 10 / 21
ਲ਼ͱ࠷దੑ݅ KKT ݅ ੍ఆ ತܭըʹ͓͚Δ࠷దੑ݅ ఆཧ 3.4 S ⫅ Rn
ۭͰͳ͍ತू߹ɼf : Rn → R x ∈ S ʹ͓͍ͯඍ Մೳͳತؔͱ͢Δɽ͜ͷͱ͖ɼࣜ (6) x ͕ (1) ͷେҬత࠷ దղͰ͋ΔͨΊͷඞཁे݅Ͱ͋Δɽ ূ໌ ඞཁੑ໌Β͔ͳͷͰेੑ͚ͩࣔͤΑ͍ɽ(ԋश) 11 / 21
ਲ਼ͱ࠷దੑ݅ KKT ݅ ੍ఆ ತܭըʹ͓͚Δ࠷దੑ݅ ఆཧ 3.4 ΑΓ࣍ͷܥ͕Γཱͭɽ ܥ 3.1
ू߹ S ⫅ Rn ͷ෦ۭͰͳ͘ɼؔ f : Rn → R x ∈ int S*3) ʹ͓͍ͯඍՄೳͱ͢Δɽͦͷͱ͖ɼx ͕ (1) ͷہ ॴత࠷దղͳΒ ∇f(x) = 0 ཱ͕͢Δɽ͞Βʹɼf ͕ತؔɼS ͕ತू߹ͳΒɼ∇f(x) = 0 x ͕ (1) ͷେҬత࠷దղͰ͋ ΔͨΊͷඞཁे݅Ͱ͋Δɽ ূ໌ ɹ x ∈ int S Ͱ͋Δͱ͖ɼTs(x) = Rn Ͱ͋Δ͔Βɼ Ns(x) = {0} ͱͳΔɽΑͬͯɼࣜ (6) ∇f(x) = 0 ʹؼண͞ΕΔɽ *3)ू߹ S ⫅ Rn ͱ x ∈ Rn ʹରͯ͠ɼB(x, r) ⫅ S ͱͳΔΑ͏ͳ r > 0 ͕ଘࡏ͢Δͱ ͖ɼx Λ S ͷͱ͍͍ɼS ͷશମͷू߹Λ S ͷ෦ͱ͍͍ɼint S Ͱද͢ɽ 12 / 21
1. ਲ਼ͱ࠷దੑ݅ 2. KKT ݅ 3. ੍ఆ
ਲ਼ͱ࠷దੑ݅ KKT ݅ ੍ఆ ෆࣜΛؚΉ࠷దԽ ࣍ͷ࠷దԽΛߟ͑Δɿ minimize x∈Rn f(x) subject
to gi(x) ≦ 0 (i = 1, . . . , m). (7) ͜͜Ͱɼؔ f ͓Αͼ gi (i = 1, . . . , m) ඍՄೳͰ͋Δͱ͢Δɽ • (7) ͷ੍݅ɼ (1) ͷ࣮ޮՄೳྖҬ S ͕ S = {x ∈ Rn | gi(x) ≦ 0 (i = 1, . . . , m)} (8) ͱද͞ΕΔ߹ʹଞͳΒͳ͍ɽ • (7) ͷ࣮ޮՄೳղ x ʹ͓͍ͯɼgi(x) = 0 ͕Γ੍ཱͭ ݅Λ༗ޮ੍݅ͱݺͼɼͦͷఴࣈू߹ΛҎԼͰද͢ɿ I(x) = {i ∈ N | gi(x) = 0} ⫅ {1, 2, . . . , m}. 14 / 21
ਲ਼ͱ࠷దੑ݅ KKT ݅ ੍ఆ ઢܗԽਲ਼ ࣮ޮՄೳྖҬ S ͕ࣜ (8) Ͱ༩͑ΒΕΔͱ͖ɼਲ਼ʹมΘΔ֓೦ͱ͠
ͯઢܗԽਲ਼ͱݺΕΔਲ਼Λߟ͑Δ͜ͱ͕Ͱ͖Δɽ ఆٛ: ઢܗԽਲ਼ (linearizing cone) ू߹ S ͕ࣜ (8) Ͱ༩͑ΒΕ͍ͯΔͱ͖ɼ x ∈ S ʹ͓͚Δ༗ޮ੍ ݅ʹରԠ͢Δ੍ؔͷޯ ∇gi(x) (i ∈ I(x)) ͱ 90◦ Ҏ্ͷ֯ Λͳ͢ϕΫτϧશମͷू߹ΛઢܗԽਲ਼ͱݺͼɼCs(x) Ͱද͢ɽ • ઢܗԽਲ਼ Cs(x) ࣍ͷΑ͏ʹද͞ΕΔ: Cs(x) := {y ∈ Rn | ⟨∇gi(x), y⟩ ≦ 0 (∀i ∈ I(x))} (9) • ਲ਼ Ts(x) ू߹ S ͔Βఆٛ͞ΕΔͷʹର͠ɼઢܗԽਲ਼ Cs(x) ؔ gi ʹґଘͯ͠ఆ·Δ͜ͱʹҙ͢Δɽ • ਲ਼ͱઢܗԽਲ਼ඞͣ͠Ұக͢ΔͱݶΒͳ͍͕ɼแؚؔ Ts(x) ⫅ Cs(x) ৗʹཱ͢Δɽ(ิ 3.3) 15 / 21
ਲ਼ͱ࠷దੑ݅ KKT ݅ ੍ఆ Lagrange ؔ Lagrange ؔͱݺΕΔؔΛఆٛ͢Δɽ ఆٛ: Lagrange
ؔ (Lagragian) (7) ʹରͯ͠ɼ࣍ࣜͰఆٛ͞ΕΔؔ L0 : Rn+m → [−∞, ∞] Λ Lagrange ؔͱ͍͏ɽ L0(x, λ) = f(x) + m ∑ i=1 λigi(x) (λ ≧ 0) −∞ (λ ≧̸ 0) (10) ͜͜ʹɼλ = (λ1, . . . , λm)⊤ ∈ Rm Λ Lagrange ͱݺͿɽ • ࣜ (10) ʹ͓͍ͯɼλ ≧̸ 0 ͷͱ͖ L0(x, λ) = −∞ ͱఆٛͨ͠ ͷɼରΛఆٛ͢Δࡍʹ߹͕Α͍ͨΊͰ͋Δɽ • Lagrange ؔʹΑͬͯ (7) ʹର͢Δ࠷దੑͷඞཁ݅Λ ༩͑Δ͜ͱ͕Ͱ͖Δɽ 16 / 21
ਲ਼ͱ࠷దੑ݅ KKT ݅ ੍ఆ KKT ݅: ࠷దੑͷඞཁ݅ (7) ʹର͢Δ࠷దੑͷඞཁ݅ʹ͍ͭͯड़Δɽ
ఆཧ 3.5 x Λ (7) ͷہॴత࠷దղͱ͢Δɽͦͷͱ͖ɼแؚؔ Cs(x) ⫅ co Ts(x) ͕ΓཱͭͳΒɼ࣍ࣜ: ∇xL0(x, λ) = ∇f(x) + m ∑ i=1 λi∇gi(x) = 0 λi ≧ 0, gi(x) ≧ 0, λigi(x) = 0 (i = 1, . . . , m) (11) Λຬ͢Δ Lagrange λ ∈ Rm ͕ଘࡏ͢Δɽ • ࣜ (11) Ұൠʹ KKT ݅ (KKT condition) ͱݺΕΔɽ • ఆཧ 3.5 x ͕ (7) ͷہॴత࠷దղͰ͋ΔͨΊͷे ݅Ͱ͋Δ͜ͱอূ͍ͯ͠ͳ͍ɽ 17 / 21
ਲ਼ͱ࠷దੑ݅ KKT ݅ ੍ఆ ࠷దੑͷे݅ ತܭըʹ͓͍ͯɼKKT ͕݅࠷దੑͷे݅ʹͳΔɽ ఆཧ 3.6
(7) ʹ͓͍ͯɼతؔ f ͱ੍ؔ gi ඍՄೳͳತؔͱ ͢Δɽͦͷͱ͖ɼ͋Δ x ∈ Rn ͱ λ ͕ࣜ (11) Λຬ͢ΔͳΒɼx (7) ͷେҬత࠷దղͰ͋Δɽ • ఆཧ 3.5 ͱఆཧ 3.6 ΑΓɼತܭըͷͱ͖ KKT ͕݅େ Ҭత࠷దੑͷඞཁे݅ͱͳΔɽͭ·Γɼ ∃ (x, λ) s.t. ࣜ (11) ⇔ x (7) ͷେҬత࠷దղ • େҬత࠷దղͰ͋Δ͜ͱΛอূͰ͖Δͷɼತܭըʹ͓͍ ͯʮہॴత࠷దղͳΒେҬత࠷దղʯ͕ΓཱͭͨΊͰ͋ Δɽ(ఆཧ 3.1) • ূ໌Ұܦݧ͓ͯ͘͠ͱΑ͍ɽ(ԋश) 18 / 21
1. ਲ਼ͱ࠷దੑ݅ 2. KKT ݅ 3. ੍ఆ
੍ఆ (7) ʹର͢Δදతͳ੍ఆͱͯ͠ҎԼͷͷ͕͋Δɽ ओͳ੍ఆ • Ұ࣍ಠ੍ཱఆ: ϕΫτϧ ∇gi(x) (∀i
∈ I(x)) Ұ࣍ಠཱͰ ͋Δɽ • Slater ੍ఆ: ؔ gi (∀i ∈ I(x)) ತؔͰ͋Γɼ gi(x) < 0 (i = 1, . . . , m) ͳΔ x0 ͕ଘࡏ͢Δɽ • Cottle ੍ఆ: ⟨∇g(x), y⟩ < 0 (∀i ∈ I(x)) Λຬͨ͢ϕΫτ ϧ y ∈ Rn ͕ଘࡏ͢Δɽ • Abadie ੍ఆ: Cs(x) ⫅ Ts(x) • Guignard ੍ఆ: Cs(x) ⫅ co Ts(x) • Guignard ੍ఆఆཧ 3.5 ͰԾఆ੍ͨ͠ఆͰ͋Δɽ
੍ఆͷ૬ޓؔ ੍֤ఆʹ͍ͭͯ࣍ͷ͕ؔΓཱͭɽ ఆཧ • Ұ࣍ಠ੍ཱఆ ⇒ Cottle ੍ఆ • Slater
੍ఆ ⇒ Cottle ੍ఆ • Cottle ੍ఆ ⇒ Abadie ੍ఆ • Abadie ੍ఆ ⇒ Guignard ੍ఆ • 5 छྨͷ੍ఆͷ͏ͪ Guignard ੍ఆ͕࠷ऑ͍ԾఆͰ ͋Δ͕ɼ༩͑ΒΕͨ࠷దԽʹରͯ͠ Cs(x) ⫅ co Ts(x) Ͱ ͋Δ͜ͱΛఆ͢Δ͜ͱࠔͰ͋Γɼ࣮༻తͰͳ͍ɽ • Ұ੍࣍ఆɼSlater ੍ఆɼCottle ੍ఆݕূ͕ൺֱ త༰қͰ͋ΔͨΊɼ࣮ࡍʹΑ͘ΘΕΔɽ
4.
ఆཧ 3.1 ࠷దԽ (1) ʹ͓͍ͯɼf ತؔɼS ತू߹ͱ͢Δɽͦͷͱ ͖ɼ (1) ͷҙͷہॴత࠷దղେҬత࠷దղͰ͋Δɽ
ূ໌ ہॴత࠷దԽͰ͋Δ͕େҬత࠷దղͰͳ͍Α͏ͳ x ∈ S ͷ ଘࡏΛԾఆ͢Δɽ͢ͳΘͪɼf(y) < f(x) Λຬͨ͢Α͏ͳ y ∈ S ͕ଘࡏ͢Δɽ͍·ɼू߹ S ತؔΑΓҙͷ α ∈ (0, 1) ʹର͠ ͯɼ(1 − α)x + αy ∈ S Ͱ͋Δɽ·ͨɼؔ f ತؔΑΓ f((1 − α)x + αy) ≦ (1 − α)f(x) + αf(y) < (1 − α)f(x) + αf(x) = f(x) ͕Γཱͭɽ্ࣜͰ α → 0 ͷۃݶΛߟ͑Δͱɼx ͷҙͷۙͷத ʹ x ΑΓਅʹখ͍͞తؔΛ࣮ͭޮՄೳղ͕ଘࡏ͢Δ͜ͱ ͕ݴ͑Δɽ͜Εɼx ͕ہॴత࠷దղͰ͋Δ͜ͱʹ͢Δɽ(ূ ໌ऴ)
ఆཧ 3.4 S ⫅ Rn ۭͰͳ͍ತू߹ɼf : Rn → R
x ∈ S ʹ͓͍ͯඍ Մೳͳತؔͱ͢Δɽͦͷͱ͖ɼࣜ (6) x ͕ (1) ͷେҬత࠷ దղͰ͋ΔͨΊͷඞཁे݅Ͱ͋Δɽ ূ໌ ඞཁੑఆཧ 3.3 ΑΓ໌Β͔ͳͷͰेੑͷΈࣔ͢ɽ͍·ɼ −∇f(x) ∈ Ns(x) ΑΓɼҙͷ x ∈ S ʹରͯ͠ ⟨−∇f(x), x − x⟩ ≦ 0 ⇔ ⟨∇f(x), x − x⟩ ≧ 0 (12) ͕Γཱͭ *4)ɽ·ͨɼҙͷ x ∈ S ʹରͯ͠ f(x) ≧ (f ͷತੑ) f(x) + ⟨∇f(x), x − x⟩ ≧ (ࣜ (12)) f(x) ͕ΓཱͭɽΏ͑ʹɼx (1) ͷେҬత࠷దղͰ͋Δɽ(ূ໌ऴ) *4)ू߹ S ͕ತू߹Ͱ͋Δͱ͖๏ઢਲ਼ࣜ (5) Ͱ༩͑ΒΕΔ͜ͱΛ༻͍ͨɽ
ఆཧ 3.6 (7) ʹ͓͍ͯɼతؔ f ͱ੍ؔ gi ඍՄೳͳತؔͱ ͢Δɽͦͷͱ͖ɼ͋Δ
x ∈ Rn ͱ λ ͕ࣜ (11) Λຬ͢ΔͳΒɼx (7) ͷେҬత࠷దղͰ͋Δɽ ূ໌ λ Λݻఆͯ͠ɼؔ ℓ : Rn → R Λ࣍ࣜͰఆٛ͢Δɿ ℓ(x) = f(x) + m ∑ i=1 λigi(x). ͍·ɼf, gi ͱʹತؔͰ λ ≧ 0 Ͱ͋Δ͔Β ℓ ತؔͰ͋ Δ *5)ɽ݅ΑΓɼx ∈ Rn ͱ λ ࣜ (11) Λຬͨ͢ͷͰ ∇f(x) + m ∑ i=1 λi∇gi(x) = 0
͕Γཱͭɽఆཧ 3.4 ΑΓ ℓ x ʹ͓͍ͯେҬతʹ࠷খͱͳΔɽ Αͬͯɼҙͷ x ∈
Rn ʹରͯ͠ɼℓ(x) ≦ ℓ(x), i.e., f(x) + m ∑ i=1 λigi(x) =0 ≦ f(x) + m ∑ i=1 λigi(x) ͕Γཱͭɽ݅ΑΓ λigi(x) = 0 (i = 1, . . . , m) ͔ͭ λ ≧ 0 Ͱ͋ Δ͔Βɼgi(x) ≦ 0 (i = 1, . . . , m) Λຬͨ͢ҙͷ x ʹରͯ͠ *6) f(x) + 0 ≦ f(x) + m ∑ i=1 λigi(x) ≦0 ≦ f(x) ͕Γཱͭɽ͕ͨͬͯ͠ɼx େҬత࠷దղͰ͋Δɽ(ূ໌ऴ) *5)ʰඇઢܗ࠷దԽͷجૅʱఆཧ 2.26 Λࢀরɽ *6)͢ͳΘͪɼ (7) ͷҙͷ࣮ޮՄೳղʹରͯ͠