Taylor Y Bisecion Numerico.docx

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Metodoo de Taylor E1) Use el polinomio de Taylor de orden 4 para 𝒂 = 𝟏 al calcular el valor aproximado de 𝑳𝒏 (𝟎. πŸ–) y dΓ© una estimaciΓ³n del mΓ‘ximo error cometido. Es necesario las cinco primeras derivadas de 𝑓(π‘₯) 𝑓(π‘₯) = 𝐿𝑛 π‘₯

⟹ 𝑓(1) = 𝐿𝑛 1

1

𝑓 β€² (π‘₯) = π‘₯

1

𝑓 β€²β€² (π‘₯) = βˆ’ π‘₯ 2 𝑓 β€²β€²β€² (π‘₯) =

2 π‘₯3

𝑓 ′𝑣 (π‘₯) = βˆ’ 𝑓 𝑣 (π‘₯) =

6π‘₯ 2 π‘₯6

24π‘₯ 3 π‘₯8

⟹ 𝑓(1) = 0

1

⟹

𝑓 β€² (1) = 1

⟹

𝑓 β€²β€² (1) = βˆ’ 12

⟹

𝑓 β€²β€²β€² (1) =

⟹

𝑓 ′𝑣 (1) = βˆ’ (1)4

⟹

⟹ 𝑓 β€² (1) = 1 1

2 13

⟹ 𝑓 β€²β€²β€² (1) = 2 6

24

𝑓 𝑣 (𝑐) = (𝑐)5

⟹ 𝑓 β€²β€² (1) = βˆ’1

⟹ 𝑓 ′𝑣 (1) = βˆ’6 ⟹ 𝑓 𝑣 (𝑐) =

24 𝑐5

Entonces por la fΓ³rmula de Taylor: 𝐿𝑛 π‘₯ = 𝑓(1) + 𝑓 β€² (1)(π‘₯ βˆ’ 1) + 𝑓 β€²β€² (1)

𝐿𝑛 π‘₯ = 0 + 1(π‘₯ βˆ’ 1) +

𝐿𝑛 π‘₯ = (π‘₯ βˆ’ 1) βˆ’

(π‘₯ βˆ’ 1)2 (π‘₯ βˆ’ 1)3 (π‘₯ βˆ’ 1)4 + 𝑓 β€²β€²β€² (1) + 𝑓 ′𝑣 (1) + 𝑅4 (π‘₯) 2! 3! 4!

(βˆ’1)(π‘₯ βˆ’ 1)2 2 (π‘₯ βˆ’ 1)3 (βˆ’ 6)(π‘₯ βˆ’ 1)4 + + + 𝑅4 (π‘₯) 2! 3! 4!

(π‘₯ βˆ’ 1)2 2 (π‘₯ βˆ’ 1)3 6(π‘₯ βˆ’ 1)4 + βˆ’ + 𝑅4 (π‘₯) 2 3Γ—2Γ—1 4Γ—3Γ—2Γ—1

(π‘₯ βˆ’ 1)2 (π‘₯ βˆ’ 1)3 (π‘₯ βˆ’ 1)4 𝐿𝑛 π‘₯ = (π‘₯ βˆ’ 1) βˆ’ + βˆ’ + 𝑅4 (π‘₯) 2 3 4 𝐿𝑛 (0.8) = (π‘₯ βˆ’ 1) βˆ’

(π‘₯ βˆ’ 1)2 (π‘₯ βˆ’ 1)3 (π‘₯ βˆ’ 1)4 + βˆ’ + 𝑅4 (π‘₯) 2 3 4

𝐿𝑛 (0.8) = (0.8 βˆ’ 1) βˆ’

βˆ’ 0.22314 = βˆ’0.2 βˆ’

(0.8 βˆ’ 1)2 (0.8 βˆ’ 1)3 (0.8 βˆ’ 1)4 + βˆ’ + 𝑅4 (0.8) 2 3 4

(βˆ’0.2)2 (βˆ’0.2)3 (βˆ’0.2)4 + βˆ’ + 𝑅4 (0.8) 2 3 4

βˆ’ 0.22314 = βˆ’0.22306 + 𝑅4 (0.8) Sabemos que : 𝑅𝑛 (π‘₯) = 𝑓 (𝑛+1) (𝑐)

(π‘₯ βˆ’ π‘Ž)𝑛+1 (𝑛 + 1)!

24 (βˆ’0.2)5 𝑐5 5! (βˆ’0.2)5 24 𝑅𝑛 (0.8) = 5 𝑐 5Γ—4Γ—3Γ—2Γ—1 𝑅𝑛 (0.8) =

𝑅𝑛 (0.8) =

(βˆ’0.2)5 5 𝑐5

0.8 < 𝑐 < 1 π‘₯<𝑐<π‘Ž Por lo tanto : | 𝑅𝑛 (0.8) | <

(0.2)5 5 𝑐5

| 𝑅𝑛 (0.8) | <

(0.2)5 = βˆ’0.000195313 5 (0.8)5

Podemos concluir que 𝐿𝑛 (0.9) = βˆ’0.22314 con un error de βˆ’0.000195313 Al decir esto suponemos que el error es insignificante o que el error de cΓ‘lculo fue insignificante.

Punto Fijo E2) Utilice el mΓ©todo de Punto Fijo para localizar la raΓ­z de 𝑓(π‘₯) = π‘₯ 3 βˆ’ 10π‘₯ βˆ’ 5 con un valor inicial de X0 = 1, e iterar hasta que el error estimado sea menor o igual a 0.0001. Tenemos que: 3

𝑓(π‘₯) = π‘₯ 3 βˆ’ 10π‘₯ βˆ’ 5 ⟹ 𝑔(π‘₯) = √10π‘₯ + 5 ⟹ 𝑔(π‘₯) = ( 10π‘₯ + 5)/π‘₯ 2

π’Š

π’™π’Š

0

1,00000

1

2,46621

1,46621

2

3,09552

0,62931

3

3,30056

0,20503

4

3,36214

0,06158

5

3,38020

0,01806

6

3,38546

0,00526

7

3,38699

0,00153

8

3,38744

0,00044

9

3,38757

0,00013

10

3.38760

0.00004 ≀ 0.0001

⃒𝒙𝒏+𝟏 βˆ’ 𝒙𝒏 βƒ’

Tenemos 𝑔(π‘₯) = βˆ›(10π‘₯ + 5) : π‘₯0 = 𝑔(π‘₯0 ) = 1 π‘₯1 = 𝑔(π‘₯0 ) = 𝑔(1) = βˆ›(10π‘₯(1) + 5) = 2,46621 π‘₯2 = 𝑔(π‘₯1 ) = 𝑔(2,46621) = βˆ›(10π‘₯(2,46621) + 5) = 3,09552 π‘₯3 = 𝑔(π‘₯2 ) = 𝑔(3,09552) = βˆ›(10π‘₯(3,09552) + 5) = 3,30056 π‘₯4 = 𝑔(π‘₯3 ) = 𝑔(3,30056) = βˆ›(10π‘₯(3,30056) + 5) = 3,36214

Sabemos βƒ’π‘₯𝑛+1 βˆ’ π‘₯𝑛 βƒ’ |π‘₯1 βˆ’ π‘₯0 | = |2,46621 βˆ’ 1| = 1,46621 |π‘₯2 βˆ’ π‘₯1 | = |3,09552 βˆ’ (2,46621)| = 0,62931 |π‘₯3 βˆ’ π‘₯2 | = |3,30056 βˆ’ ( 3,09552)| = 0,20503 |π‘₯4 βˆ’ π‘₯3 | = |3,36214 βˆ’ (3,30056)| = 0,06158

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