WikiDer > Gauss-Zeydel usuli

Yilda raqamli chiziqli algebra, Gauss-Zeydel usuli, deb ham tanilgan Liebmann usuli yoki ketma-ket siljish usuli, bu takroriy usul hal qilish uchun ishlatiladi chiziqli tenglamalar tizimi. Uning nomi bilan nomlangan Nemis matematiklar Karl Fridrix Gauss va Filipp Lyudvig fon Zeydelva shunga o'xshash Jakobi usuli. Diagonallarda nolga teng bo'lmagan elementlarga ega bo'lgan har qanday matritsaga tatbiq etilishi mumkin bo'lsa-da, yaqinlashuv faqat matritsa yoki qat'iy diagonal ustunlik qiladi,^[1] yoki nosimmetrik va ijobiy aniq. Bu faqat Gaussning shogirdiga yozgan shaxsiy maktubida aytilgan Gerling 1823 yilda.^[2] Zaydel tomonidan 1874 yilgacha nashr qilinmagan.

Tavsif

Gauss-Zeydel usuli - bu takroriy texnika ning kvadrat tizimini echish uchun n noma'lum bo'lgan chiziqli tenglamalar x:

${ displaystyle A mathbf {x} = mathbf {b}}$.

Bu takrorlash bilan belgilanadi

${ displaystyle L _ {*} mathbf {x} ^ {(k + 1)} = mathbf {b} -U mathbf {x} ^ {(k)},}$

qayerda ${ displaystyle mathbf {x} ^ {(k)}}$ bo'ladi kning yaqinlashishi yoki takrorlanishi ${ displaystyle mathbf {x}, , mathbf {x} ^ {(k + 1)}}$ keyingi yoki k + 1 takrorlash ${ displaystyle mathbf {x}}$va matritsa A a ga ajraladi pastki uchburchak komponent ${ displaystyle L _ {*}}$va a qat'iy yuqori uchburchak komponent U: ${ displaystyle A = L _ {*} + U}$.^[3]

Batafsilroq yozing A, x va b ularning tarkibiy qismlarida:

${ displaystyle A = { begin {bmatrix} a_ {11} & a_ {12} & cdots & a_ {1n} a_ {21} & a_ {22} & cdots & a_ {2n} vdots & vdots & ddots & vdots a_ {n1} & a_ {n2} & cdots & a_ {nn} end {bmatrix}}, qquad mathbf {x} = { begin {bmatrix} x_ {1} x_ {2} vdots x_ {n} end {bmatrix}}, qquad mathbf {b} = { begin {bmatrix} b_ {1} b_ {2} vdots b_ {n} end {bmatrix}}.}$

Keyin. Ning parchalanishi A uning pastki uchburchak qismiga va uning yuqori yuqori uchburchagi komponentiga quyidagilar kiradi:

${ displaystyle A = L _ {*} + U qquad { text {where}} qquad L _ {*} = { begin {bmatrix} a_ {11} & 0 & cdots & 0 a_ {21} & a_ {22 } & cdots & 0 vdots & vdots & ddots & vdots a_ {n1} & a_ {n2} & cdots & a_ {nn} end {bmatrix}}, quad U = { begin { bmatrix} 0 & a_ {12} & cdots & a_ {1n} 0 & 0 & cdots & a_ {2n} vdots & vdots & ddots & vdots 0 & 0 & cdots & 0 end {bmatrix}}.}$

Lineer tenglamalar tizimi quyidagicha yozilishi mumkin:

${ displaystyle L _ {*} mathbf {x} = mathbf {b} -U mathbf {x}}$

Gauss-Zeydel usuli endi ushbu ifodaning chap tomonini hal qiladi x, oldingi qiymatidan foydalanib x o'ng tomonda. Analitik ravishda, bu quyidagicha yozilishi mumkin:

${ displaystyle mathbf {x} ^ {(k + 1)} = L _ {*} ^ {- 1} ( mathbf {b} -U mathbf {x} ^ {(k)}).}$

Biroq, ning uchburchak shaklidan foydalangan holda ${ displaystyle L _ {*}}$, ning elementlari x^(k+1) yordamida ketma-ket hisoblash mumkin oldinga almashtirish:

${ displaystyle x_ {i} ^ {(k + 1)} = { frac {1} {a_ {ii}}} chap (b_ {i} - sum _ {j = 1} ^ {i-1 } a_ {ij} x_ {j} ^ {(k + 1)} - sum _ {j = i + 1} ^ {n} a_ {ij} x_ {j} ^ {(k)} o'ng), quad i = 1,2, nuqta, n.}$ ^[4]

Odatda protsedura takrorlanish bilan kiritilgan o'zgarishlar biroz tolerantlik darajasidan past bo'lguncha davom ettiriladi, masalan, etarlicha kichik qoldiq.

Munozara

Gauss-Zeydel uslubining elementar formulasi juda o'xshash Jakobi usuli.

Hisoblash x^(k+1) ning elementlaridan foydalanadi x^(k+1) allaqachon hisoblab chiqilgan va faqat elementlari x^(k) k + 1 takrorlanishida hisoblanmagan. Bu shuni anglatadiki, Jakobi usulidan farqli o'laroq, faqat bitta saqlash vektori talab qilinadi, chunki elementlarni hisoblashda ularni ustiga yozish mumkin, bu juda katta muammolar uchun foydali bo'lishi mumkin.

Biroq, Jakobi uslubidan farqli o'laroq, har bir element uchun hisob-kitoblarni bajarish mumkin emas parallel. Bundan tashqari, har bir takrorlashdagi qiymatlar asl tenglamalarning tartibiga bog'liq.

Gauss-Zaydel xuddi shunday SOR (ketma-ket ortiqcha bo'shashish) bilan ${ displaystyle omega = 1}$.

Yaqinlashish

Gauss-Zeydel usulining yaqinlashish xususiyatlari matritsaga bog'liq A. Ya'ni, protsedura birlashishi ma'lum, agar:

• A nosimmetrikdir ijobiy-aniq,^[5] yoki
• A qat'iy yoki qisqartirilmaydi diagonal ravishda dominant.^[6]

Gauss-Zaydel usuli ba'zan bu shartlar bajarilmasa ham yaqinlashadi.

Algoritm

Elementlar ushbu algoritmda hisoblab chiqilganligi sababli yozilishi mumkin bo'lganligi sababli, faqat bitta saqlash vektori kerak bo'ladi va vektor indeksatsiyasi qoldiriladi. Algoritm quyidagicha:

Kirish: A, bChiqish: ${ displaystyle  phi}$Dastlabki taxminni tanlang ${ displaystyle  phi}$ hal qilish uchuntakrorlang yaqinlashguncha uchun men dan 1 qadar n qil        ${ displaystyle  sigma  leftarrow 0}$        uchun j dan 1 qadar n qil            agar j ≠ men keyin                ${ displaystyle  sigma  leftarrow  sigma + a_ {ij}  phi _ {j}}$            tugatish agar        oxiri (j(ilmoq) ${ displaystyle  phi _ {i}  leftarrow { frac {1} {a_ {ii}}} (b_ {i} -  sigma)}$    oxiri (men-loop) yaqinlashuvga erishilganligini tekshiringoxiri (takrorlash)

Misollar

Matritsa versiyasiga misol

Sifatida ko'rsatilgan chiziqli tizim ${ displaystyle A mathbf {x} = mathbf {b}}$ tomonidan berilgan:

${ displaystyle A = { begin {bmatrix} 16 & 3 7 & -11 end {bmatrix}}}$ va ${ displaystyle b = { begin {bmatrix} 11 13 end {bmatrix}}.}$

Biz tenglamadan foydalanmoqchimiz

${ displaystyle mathbf {x} ^ {(k + 1)} = L _ {*} ^ {- 1} ( mathbf {b} -U mathbf {x} ^ {(k)})}$

shaklida

${ displaystyle mathbf {x} ^ {(k + 1)} = T mathbf {x} ^ {(k)} + C}$

qaerda:

${ displaystyle T = -L _ {*} ^ {- 1} U}$ va ${ displaystyle C = L _ {*} ^ {- 1} mathbf {b}.}$

Biz parchalanishimiz kerak ${ displaystyle A _ {} ^ {}}$ pastki uchburchak komponentining yig'indisiga ${ displaystyle L _ {*} ^ {}}$ va qat'iy yuqori uchburchak komponent ${ displaystyle U _ {} ^ {}}$:

${ displaystyle L _ {*} = { begin {bmatrix} 16 & 0 7 & -11 end {bmatrix}}}$ va ${ displaystyle U = { begin {bmatrix} 0 & 3 0 & 0 end {bmatrix}}.}$

Ning teskari tomoni ${ displaystyle L _ {*} ^ {}}$ bu:

${ displaystyle L _ {*} ^ {- 1} = { begin {bmatrix} 16 & 0 7 & -11 end {bmatrix}} ^ {- 1} = { begin {bmatrix} 0.0625 & 0.0000 0.0398 & -0.0909 end {bmatrix}}}$.

Endi biz quyidagilarni topamiz:

${ displaystyle T = - { begin {bmatrix} 0.0625 & 0.0000 0.0398 & -0.0909 end {bmatrix}} times { begin {bmatrix} 0 & 3 0 & 0 end {bmatrix}} = { begin {bmatrix} 0.000 & -0.1875 0.000 & -0.1194 end {bmatrix}},}$

${ displaystyle C = { begin {bmatrix} 0.0625 & 0.0000 0.0398 & -0.0909 end {bmatrix}} times { begin {bmatrix} 11 13 end {bmatrix}} = { begin { bmatrix} 0.6875 - 0.7439 end {bmatrix}}.}$

Endi bizda ${ displaystyle T _ {} ^ {}}$ va ${ displaystyle C _ {} ^ {}}$ va biz ularni vektorlarni olish uchun ishlatishimiz mumkin ${ displaystyle mathbf {x}}$ takroriy ravishda.

Avvalo, biz tanlashimiz kerak ${ displaystyle mathbf {x} ^ {(0)}}$: biz faqat taxmin qilishimiz mumkin. Taxmin qanchalik yaxshi bo'lsa, algoritm tezroq bajariladi.

Biz taxmin qilamiz:

${ displaystyle x ^ {(0)} = { begin {bmatrix} 1.0 1.0 end {bmatrix}}.}$

Keyin hisoblashimiz mumkin:

${ displaystyle x ^ {(1)} = { begin {bmatrix} 0.000 & -0.1875 0.000 & -0.1193 end {bmatrix}} times { begin {bmatrix} 1.0 1.0 end {bmatrix} } + { begin {bmatrix} 0.6875 - 0.7443 end {bmatrix}} = { begin {bmatrix} 0.5000 - 0.8636 end {bmatrix}}.}$

${ displaystyle x ^ {(2)} = { begin {bmatrix} 0.000 & -0.1875 0.000 & -0.1193 end {bmatrix}} times { begin {bmatrix} 0.5000 - 0.8636 end {bmatrix }} + { begin {bmatrix} 0.6875 - 0.7443 end {bmatrix}} = { begin {bmatrix} 0.8494 - 0.6413 end {bmatrix}}.}$

${ displaystyle x ^ {(3)} = { begin {bmatrix} 0.000 & -0.1875 0.000 & -0.1193 end {bmatrix}} times { begin {bmatrix} 0.8494 - 0.6413 end {bmatrix}} + { begin {bmatrix} 0.6875 - 0.7443 end {bmatrix}} = { begin {bmatrix} 0.8077 - 0.6678 end {bmatrix}}.}$

${ displaystyle x ^ {(4)} = { begin {bmatrix} 0.000 & -0.1875 0.000 & -0.1193 end {bmatrix}} times { begin {bmatrix} 0.8077 - 0.6678 end {bmatrix }} + { begin {bmatrix} 0.6875 - 0.7443 end {bmatrix}} = { begin {bmatrix} 0.8127 - 0.6646 end {bmatrix}}.}$

${ displaystyle x ^ {(5)} = { begin {bmatrix} 0.000 & -0.1875 0.000 & -0.1193 end {bmatrix}} times { begin {bmatrix} 0.8127 - 0.6646 end {bmatrix }} + { begin {bmatrix} 0.6875 - 0.7443 end {bmatrix}} = { begin {bmatrix} 0.8121 - 0.6650 end {bmatrix}}.}$

${ displaystyle x ^ {(6)} = { begin {bmatrix} 0.000 & -0.1875 0.000 & -0.1193 end {bmatrix}} times { begin {bmatrix} 0.8121 - 0.6650 end {bmatrix }} + { begin {bmatrix} 0.6875 - 0.7443 end {bmatrix}} = { begin {bmatrix} 0.8122 - 0.6650 end {bmatrix}}.}$

${ displaystyle x ^ {(7)} = { begin {bmatrix} 0.000 & -0.1875 0.000 & -0.1193 end {bmatrix}} times { begin {bmatrix} 0.8122 - 0.6650 end {bmatrix }} + { begin {bmatrix} 0.6875 - 0.7443 end {bmatrix}} = { begin {bmatrix} 0.8122 - 0.6650 end {bmatrix}}.}$

Kutilganidek, algoritm aniq echimga aylanadi:

${ displaystyle mathbf {x} = A ^ {- 1} mathbf {b} taxminan { begin {bmatrix} 0.8122 - 0.6650 end {bmatrix}}.}$

Aslida, A matritsa mutlaqo diagonal ravishda dominant (ammo ijobiy aniq emas).

Matritsa versiyasi uchun yana bir misol

Sifatida ko'rsatilgan yana bir chiziqli tizim ${ displaystyle A mathbf {x} = mathbf {b}}$ tomonidan berilgan:

${ displaystyle A = { begin {bmatrix} 2 & 3 5 & 7 end {bmatrix}}}$ va ${ displaystyle b = { begin {bmatrix} 11 13 end {bmatrix}}.}$

Biz tenglamadan foydalanmoqchimiz

${ displaystyle mathbf {x} ^ {(k + 1)} = L _ {*} ^ {- 1} ( mathbf {b} -U mathbf {x} ^ {(k)})}$

shaklida

${ displaystyle mathbf {x} ^ {(k + 1)} = T mathbf {x} ^ {(k)} + C}$

qaerda:

${ displaystyle T = -L _ {*} ^ {- 1} U}$ va ${ displaystyle C = L _ {*} ^ {- 1} mathbf {b}.}$

Biz parchalanishimiz kerak ${ displaystyle A _ {} ^ {}}$ pastki uchburchak komponentining yig'indisiga ${ displaystyle L _ {*} ^ {}}$ va qat'iy yuqori uchburchak komponent ${ displaystyle U _ {} ^ {}}$:

${ displaystyle L _ {*} = { begin {bmatrix} 2 & 0 5 & 7 end {bmatrix}}}$ va ${ displaystyle U = { begin {bmatrix} 0 & 3 0 & 0 end {bmatrix}}.}$

Ning teskari tomoni ${ displaystyle L _ {*} ^ {}}$ bu:

${ displaystyle L _ {*} ^ {- 1} = { begin {bmatrix} 2 & 0 5 & 7 end {bmatrix}} ^ {- 1} = { begin {bmatrix} 0.500 & 0.000 - 0.357 & 0.143 end {bmatrix}}}$.

Endi biz quyidagilarni topamiz:

${ displaystyle T = - { begin {bmatrix} 0.500 & 0.000 - 0.357 & 0.143 end {bmatrix}} times { begin {bmatrix} 0 & 3 0 & 0 end {bmatrix} } = { begin {bmatrix} 0.000 & -1.500 0.000 & 1.071 end {bmatrix}},}$

${ displaystyle C = { begin {bmatrix} 0.500 & 0.000 - 0.357 & 0.143 end {bmatrix}} times { begin {bmatrix} 11 13 end {bmatrix}} = { begin {bmatrix} 5.500 - 2.071 end {bmatrix}}.}$

Endi bizda ${ displaystyle T _ {} ^ {}}$ va ${ displaystyle C _ {} ^ {}}$ va biz ularni vektorlarni olish uchun ishlatishimiz mumkin ${ displaystyle mathbf {x}}$ takroriy ravishda.

Avvalo, biz tanlashimiz kerak ${ displaystyle mathbf {x} ^ {(0)}}$: biz faqat taxmin qilishimiz mumkin. Taxmin qanchalik yaxshi bo'lsa, algoritmni tezroq bajaradi.

Biz taxmin qilamiz:

${ displaystyle x ^ {(0)} = { begin {bmatrix} 1.1 2.3 end {bmatrix}}.}$

Keyin hisoblashimiz mumkin:

${ displaystyle x ^ {(1)} = { begin {bmatrix} 0 & -1.500 0 & 1.071 end {bmatrix}} times { begin {bmatrix} 1.1 2.3 end { bmatrix}} + { begin {bmatrix} 5.500 - 2.071 end {bmatrix}} = { begin {bmatrix} 2.050 0.393 end {bmatrix}}.}$

${ displaystyle x ^ {(2)} = { begin {bmatrix} 0 & -1.500 0 & 1.071 end {bmatrix}} times { begin {bmatrix} 2.050 0.393 end { bmatrix}} + { begin {bmatrix} 5.500 - 2.071 end {bmatrix}} = { begin {bmatrix} 4.911 - 1.651 end {bmatrix}}.}$

${ displaystyle x ^ {(3)} = cdots. ,}$

Agar biz yaqinlashishni sinab ko'rsak, algoritm turlicha bo'lishini aniqlaymiz. Aslida, A matritsa diagonal ravishda ham dominant, ham ijobiy aniq emas, keyin aniq echimga yaqinlashish.

${ displaystyle mathbf {x} = A ^ {- 1} mathbf {b} = { begin {bmatrix} -38 29 end {bmatrix}}}$

kafolatlanmagan va, bu holda, bo'lmaydi.

Tenglama versiyasiga misol

Deylik, berilgan k tenglamalar qaerda x_n bu tenglamalarning vektorlari va boshlang'ich nuqtasi x₀.Birinchi tenglamadan eching x₁ xususida ${ displaystyle x_ {n + 1}, x_ {n + 2}, dots, x_ {n}.}$ Keyingi tenglamalar uchun oldingi qiymatlarni almashtiringxs.

Aniq qilish uchun bir misolni ko'rib chiqing.

${ displaystyle { begin {array} {rrrrl} 10x_ {1} & - x_ {2} & + 2x_ {3} && = 6, - x_ {1} & + 11x_ {2} & - x_ {3 } & + 3x_ {4} & = 25, 2x_ {1} & - x_ {2} & + 10x_ {3} & - x_ {4} & = - 11, & 3x_ {2} & - x_ { 3} & + 8x_ {4} & = 15. end {array}}}$

Uchun hal qilish ${ displaystyle x_ {1}, x_ {2}, x_ {3}}$ va ${ displaystyle x_ {4}}$ beradi:

${ displaystyle { begin {aligned} x_ {1} & = x_ {2} / 10-x_ {3} / 5 + 3/5, x_ {2} & = x_ {1} / 11 + x_ { 3} / 11-3x_ {4} / 11 + 25/11, x_ {3} & = - x_ {1} / 5 + x_ {2} / 10 + x_ {4} / 10-11 / 10, x_ {4} & = - 3x_ {2} / 8 + x_ {3} /8+15/8.end {aligned}}}$

Aytaylik, biz tanladik (0, 0, 0, 0) boshlang'ich yaqinlashuvi sifatida birinchi taxminiy yechim tomonidan berilgan

${ displaystyle { begin {aligned} x_ {1} & = 3/5 = 0.6, x_ {2} & = (3/5) /11+25/11=3/55+25/11=2.3272 , x_ {3} & = - (3/5) / 5 + (2.3272) /10-11/10=-3/25+0.23272-1.1=-0.9873, x_ {4} & = - 3 (2.3272) / 8 + (- 0.9873) /8+15/8=0.8789.end {hizalanmış}}}$

Olingan taxminlardan foydalanib, kerakli aniqlikka erishilgunga qadar takroriy protsedura takrorlanadi. Quyida to'rtta takrorlashdan so'ng taxminiy echimlar keltirilgan.

${ displaystyle { begin {array} {llll} x_ {1} & x_ {2} & x_ {3} & x_ {4} hline 0.6 & 2.32727 & -0.987273 & 0.878864 1.03018 & 2.03694 & -1.01446 & 0 .984341 1.00659 & 2.00356 & -1.00253 & 0.998351 1.00086 & 2.0003 & -1.00031 & 0.99985 end {array}}}$

Tizimning aniq echimi (1, 2, −1, 1).

Python va NumPy dan foydalanadigan misol

Eritma vektorini ishlab chiqarish uchun quyidagi raqamli protsedura oddiygina takrorlanadi.

Import achchiq kabi npITERATION_LIMIT = 1000# matritsani boshlangA = np.qator([[10., -1., 2., 0.],              [-1., 11., -1., 3.],              [2., -1., 10., -1.],              [0., 3., -1., 8.]])# RHS vektorini ishga tushirishb = np.qator([6.0, 25.0, -11.0, 15.0])chop etish("Tenglamalar tizimi:")uchun men yilda oralig'i(A.shakli[0]):    qator = ["{0: 3g}* x{1}".format(A[men, j], j + 1) uchun j yilda oralig'i(A.shakli[1])]    chop etish("[{0}] = [{1: 3g}]".format(" + ".qo'shilish(qator), b[men]))x = np.zeros_like(b)uchun it_count yilda oralig'i(1, ITERATION_LIMIT):    x_new = np.zeros_like(x)    chop etish("Takrorlash {0}: {1}".format(it_count, x))    uchun men yilda oralig'i(A.shakli[0]):        s1 = np.nuqta(A[men, :men], x_new[:men])        s2 = np.nuqta(A[men, men + 1 :], x[men + 1 :])        x_new[men] = (b[men] - s1 - s2) / A[men, men]    agar np.allclose(x, x_new, rtol=1e-8):        tanaffus    x = x_newchop etish("Qaror: {0}".format(x))xato = np.nuqta(A, x) - bchop etish("Xato: {0}".format(xato))

Chiqarishni ishlab chiqaradi:

Tizim ning tenglamalar:[ 10*x1 +  -1*x2 +   2*x3 +   0*x4] = [  6][ -1*x1 +  11*x2 +  -1*x3 +   3*x4] = [ 25][  2*x1 +  -1*x2 +  10*x3 +  -1*x4] = [-11][  0*x1 +   3*x2 +  -1*x3 +   8*x4] = [ 15]Takrorlash 1: [ 0.  0.  0.  0.]Takrorlash 2: [ 0.6         2.32727273 -0.98727273  0.87886364]Takrorlash 3: [ 1.03018182  2.03693802 -1.0144562   0.98434122]Takrorlash 4: [ 1.00658504  2.00355502 -1.00252738  0.99835095]Takrorlash 5: [ 1.00086098  2.00029825 -1.00030728  0.99984975]Takrorlash 6: [ 1.00009128  2.00002134 -1.00003115  0.9999881 ]Takrorlash 7: [ 1.00000836  2.00000117 -1.00000275  0.99999922]Takrorlash 8: [ 1.00000067  2.00000002 -1.00000021  0.99999996]Takrorlash 9: [ 1.00000004  1.99999999 -1.00000001  1.        ]Takrorlash 10: [ 1.  2. -1.  1.]Qaror: [ 1.  2. -1.  1.]Xato: [  2.06480930e-08  -1.25551054e-08   3.61417563e-11   0.00000000e + 00]

O'zboshimchalik bilan echish uchun dastur. Matlab yordamida tenglamalar

Quyidagi kod formuladan foydalanadi${ displaystyle x_ {i} ^ {(k + 1)} = { frac {1} {a_ {ii}}} chap (b_ {i} - sum _ {j i} a_ {ij} x_ {j} ^ {(k)} right), quad i, j = 1,2, ldots , n}$

funktsiyax =sherzod_(A, b, x, takrorlar)uchun men = 1:iters        uchun j = 1:hajmi(A,1)            x(j) = (1/A(j,j)) * (b(j) - A(j,:)*x + A(j,j)*x(j));        oxiri    oxirioxiri

Shuningdek qarang

• Ketma-ket ortiqcha bo'shashish
• Kaczmarz usuli ("qatorga yo'naltirilgan" usul, Gauss-Zeydel "ustunlarga yo'naltirilgan". Masalan, qarang. Masalan ushbu qog'oz.)
• Takrorlash usuli. Lineer tizimlar
• Gauss e'tiqodini targ'ib qilish
• Matritsani ajratish
• Richardsonning takrorlanishi

Izohlar

Adabiyotlar

• Gauss, Karl Fridrix (1903), Werke (nemis tilida), 9, Göttingen: Köninglichen Gesellschaft der Wissenschaften.
• Golub, Gen H.; Van Loan, Charlz F. (1996), Matritsali hisoblashlar (3-nashr), Baltimor: Jons Xopkins, ISBN 978-0-8018-5414-9.
• Qora, Noel va Mur, Shirli. "Gauss-Zaydel usuli". MathWorld.

Ushbu maqola maqoladagi matnni o'z ichiga oladi Gauss-Zeydel_moduli kuni CFD-Wiki bu ostida GFDL litsenziya.

Tashqi havolalar

• "Zeydel usuli", Matematika entsiklopediyasi, EMS Press, 2001 [1994]
• Gauss-Zaydel www.math-linux.com saytidan
• Gauss-Zaydel Holistik raqamli usullar institutidan
• Www.geocities.com saytidan Gauss Siedel takrorlanishi
• Gauss-Zaydel usuli
• Bikson
• Matlab kodi
• C kodi misoli