SlideShare a Scribd company logo
1 of 8
Ch 4.4: Variation of Parameters
The variation of parameters method can be used to find a
particular solution of the nonhomogeneous nth order linear
differential equation
provided g is continuous.
As with 2nd
order equations, begin by assuming y1, y2 …, yn are
fundamental solutions to homogeneous equation.
Next, assume the particular solution Y has the form
where u1, u2,… un are functions to be solved for.
In order to find these n functions, we need n equations.
[ ] ),()()()( 1
)1(
1
)(
tgytpytpytpyyL nn
nn
=+′+++= −
−

)()()()()()()( 2211 tytutytutytutY nn+++= 
Variation of Parameters Derivation (2 of 5)
First, consider the derivatives of Y:
If we require
then
Thus we next require
Continuing in this way, we require
and hence
( ) ( )nnnn yuyuyuyuyuyuY ′++′+′+′++′+′=′  22112211
02211 =′++′+′ nn yuyuyu 
( ) ( )nnnn yuyuyuyuyuyuY ′′++′′+′′+′′++′′+′′=′′  22112211
02211 =′′++′′+′′ nn yuyuyu 
1,,1,0)1()1(
2
)1(
11 2
−==′++′+′ −−−
nkyuyuyu k
n
kk
n

1,,1,0,)()(
11
)(
−=++= nkyuyuY k
nn
kk

Variation of Parameters Derivation (3 of 5)
From the previous slide,
Finally,
Next, substitute these derivatives into our equation
Recalling that y1, y2 …, yn are solutions to homogeneous
equation, and after rearranging terms, we obtain
( ) ( ))()(
11
)1()1(
11
)( n
nn
nn
nn
nn
yuyuyuyuY +++′++′= −−

1,,1,0,)()(
11
)(
−=++= nkyuyuY k
nn
kk

gyuyu n
nn
n
=′++′ −− )1()1(
11 
)()()()( 1
)1(
1
)(
tgytpytpytpy nn
nn
=+′+++ −
−

Variation of Parameters Derivation (4 of 5)
The n equations needed in order to find the n functions
u1, u2,… un are
Using Cramer’s Rule, for each k = 1, …, n,
and Wk is determinant obtained by replacing kth column of
W with (0, 0, …, 1).
gyuyu
yuyu
yuyu
n
nn
n
nn
n
=′++′
=′′++′′
=′++′
−− )1()1(
11
11
111
0
0




))(,,()(where,
)(
)()(
)( 1 tyyWtW
tW
tWtg
tu n
k
k ==′
Variation of Parameters Derivation (5 of 5)
From the previous slide,
Integrate to obtain u1, u2,… un:
Thus, a particular solution Y is given by
where t0 is arbitrary.
nk
tW
tWtg
tu k
k ,,1,
)(
)()(
)( ==′
nkds
sW
sWsg
tu
t
t
k
k ,,1,
)(
)()(
)(
0
== ∫
∑ ∫=






=
n
k
k
t
t
k
tyds
sW
sWsg
tY
1
)(
)(
)()(
)(
0
Example (1 of 3)
Consider the equation below, along with the given solutions
of corresponding homogeneous solutions y1, y2, y3:
Then a particular solution of this ODE is given by
It can be shown that
tttt
etytetyetyeyyyy −
====+′−′′−′′′ )(,)(,)(, 321
2
∑ ∫=






=
3
1
2
)(
)(
)(
)(
0
k
k
t
t
k
s
tyds
sW
sWe
tY
( )
( )
t
ttt
ttt
ttt
e
eete
eete
etee
tW 4
2
1)( =
+
−+=
−
−
−
Example (2 of 3)
Also,
( )
( )
( )
( )
t
tt
tt
tt
tt
tt
tt
tt
tt
tt
e
ete
ete
tee
tW
ee
ee
ee
tW
t
eet
eet
ete
tW
=
+
+=
=−=
−−=
+
−+=
−
−
−
−
−
−
12
01
0
)(
2
1
0
0
)(
12
21
10
0
)(
3
2
1
Example (3 of 3)
Thus a particular solution is
Choosing t0 = 0, we obtain
More simply,
( )
( ) ∫∫∫
∫∫∫
∑ ∫
−
−
=
+++−=
++
−−
=






=
t
t
s
t
t
t
s
t
t
t
s
t
t
t s
ss
t
t
t s
s
t
t
t s
s
t
k
k
t
t
k
s
dse
e
dse
te
dsse
e
ds
e
ee
eds
e
e
teds
e
se
e
tyds
sW
sWe
tY
000
000
0
4
2222
3
1
2
42
12
4
44
2
4
12
)(
)(
)(
)(
tttt
eeteetY 2
3
1
12
1
2
1
4
1
)( +−−−= −
t
etY 2
3
1
)( =

More Related Content

What's hot

The two dimensional wave equation
The two dimensional wave equationThe two dimensional wave equation
The two dimensional wave equationGermán Ceballos
 
OrthogonalFunctionsPaper
OrthogonalFunctionsPaperOrthogonalFunctionsPaper
OrthogonalFunctionsPaperTyler Otto
 
Erin catto numericalmethods
Erin catto numericalmethodsErin catto numericalmethods
Erin catto numericalmethodsoscarbg
 
Fixed point theorem of discontinuity and weak compatibility in non complete n...
Fixed point theorem of discontinuity and weak compatibility in non complete n...Fixed point theorem of discontinuity and weak compatibility in non complete n...
Fixed point theorem of discontinuity and weak compatibility in non complete n...Alexander Decker
 
11.fixed point theorem of discontinuity and weak compatibility in non complet...
11.fixed point theorem of discontinuity and weak compatibility in non complet...11.fixed point theorem of discontinuity and weak compatibility in non complet...
11.fixed point theorem of discontinuity and weak compatibility in non complet...Alexander Decker
 
First order linear differential equation
First order linear differential equationFirst order linear differential equation
First order linear differential equationNofal Umair
 
Partial Differential Equations, 3 simple examples
Partial Differential Equations, 3 simple examplesPartial Differential Equations, 3 simple examples
Partial Differential Equations, 3 simple examplesEnrique Valderrama
 
Quantum phase estimation
Quantum phase estimationQuantum phase estimation
Quantum phase estimationtakumitano
 
Estimation and Prediction of Complex Systems: Progress in Weather and Climate
Estimation and Prediction of Complex Systems: Progress in Weather and ClimateEstimation and Prediction of Complex Systems: Progress in Weather and Climate
Estimation and Prediction of Complex Systems: Progress in Weather and Climatemodons
 
Random Matrix Theory and Machine Learning - Part 2
Random Matrix Theory and Machine Learning - Part 2Random Matrix Theory and Machine Learning - Part 2
Random Matrix Theory and Machine Learning - Part 2Fabian Pedregosa
 
Bayesian neural networks increasingly sparsify their units with depth
Bayesian neural networks increasingly sparsify their units with depthBayesian neural networks increasingly sparsify their units with depth
Bayesian neural networks increasingly sparsify their units with depthJulyan Arbel
 
Introduction to FEM
Introduction to FEMIntroduction to FEM
Introduction to FEMmezkurra
 
Partial Differential Equation - Notes
Partial Differential Equation - NotesPartial Differential Equation - Notes
Partial Differential Equation - NotesDr. Nirav Vyas
 
Transformation of random variables
Transformation of random variablesTransformation of random variables
Transformation of random variablesTarun Gehlot
 
Finite elements for 2‐d problems
Finite elements  for 2‐d problemsFinite elements  for 2‐d problems
Finite elements for 2‐d problemsTarun Gehlot
 
Final Present Pap1on relibility
Final Present Pap1on relibilityFinal Present Pap1on relibility
Final Present Pap1on relibilityketan gajjar
 
2003 Ames.Models
2003 Ames.Models2003 Ames.Models
2003 Ames.Modelspinchung
 

What's hot (20)

The two dimensional wave equation
The two dimensional wave equationThe two dimensional wave equation
The two dimensional wave equation
 
OrthogonalFunctionsPaper
OrthogonalFunctionsPaperOrthogonalFunctionsPaper
OrthogonalFunctionsPaper
 
Erin catto numericalmethods
Erin catto numericalmethodsErin catto numericalmethods
Erin catto numericalmethods
 
Fixed point theorem of discontinuity and weak compatibility in non complete n...
Fixed point theorem of discontinuity and weak compatibility in non complete n...Fixed point theorem of discontinuity and weak compatibility in non complete n...
Fixed point theorem of discontinuity and weak compatibility in non complete n...
 
11.fixed point theorem of discontinuity and weak compatibility in non complet...
11.fixed point theorem of discontinuity and weak compatibility in non complet...11.fixed point theorem of discontinuity and weak compatibility in non complet...
11.fixed point theorem of discontinuity and weak compatibility in non complet...
 
Math 497#w14
Math 497#w14Math 497#w14
Math 497#w14
 
First order linear differential equation
First order linear differential equationFirst order linear differential equation
First order linear differential equation
 
Partial Differential Equations, 3 simple examples
Partial Differential Equations, 3 simple examplesPartial Differential Equations, 3 simple examples
Partial Differential Equations, 3 simple examples
 
Quantum phase estimation
Quantum phase estimationQuantum phase estimation
Quantum phase estimation
 
Estimation and Prediction of Complex Systems: Progress in Weather and Climate
Estimation and Prediction of Complex Systems: Progress in Weather and ClimateEstimation and Prediction of Complex Systems: Progress in Weather and Climate
Estimation and Prediction of Complex Systems: Progress in Weather and Climate
 
Random Matrix Theory and Machine Learning - Part 2
Random Matrix Theory and Machine Learning - Part 2Random Matrix Theory and Machine Learning - Part 2
Random Matrix Theory and Machine Learning - Part 2
 
Ch07 6
Ch07 6Ch07 6
Ch07 6
 
Bayesian neural networks increasingly sparsify their units with depth
Bayesian neural networks increasingly sparsify their units with depthBayesian neural networks increasingly sparsify their units with depth
Bayesian neural networks increasingly sparsify their units with depth
 
Introduction to FEM
Introduction to FEMIntroduction to FEM
Introduction to FEM
 
Partial Differential Equation - Notes
Partial Differential Equation - NotesPartial Differential Equation - Notes
Partial Differential Equation - Notes
 
Transformation of random variables
Transformation of random variablesTransformation of random variables
Transformation of random variables
 
Finite elements for 2‐d problems
Finite elements  for 2‐d problemsFinite elements  for 2‐d problems
Finite elements for 2‐d problems
 
Final Present Pap1on relibility
Final Present Pap1on relibilityFinal Present Pap1on relibility
Final Present Pap1on relibility
 
Section3 stochastic
Section3 stochasticSection3 stochastic
Section3 stochastic
 
2003 Ames.Models
2003 Ames.Models2003 Ames.Models
2003 Ames.Models
 

Viewers also liked

IBP-Medi Care Movables
IBP-Medi Care MovablesIBP-Medi Care Movables
IBP-Medi Care MovablesBenson Josy
 
Daily production schedule steampunk productions
Daily production schedule steampunk productionsDaily production schedule steampunk productions
Daily production schedule steampunk productionsCarrie Deans
 
Social Media más que una herramienta de Marketing
Social Media más que una herramienta de MarketingSocial Media más que una herramienta de Marketing
Social Media más que una herramienta de MarketingMarcelo Cedamanos
 
CAE Banner_Library
CAE Banner_LibraryCAE Banner_Library
CAE Banner_LibraryDebra Brady
 
Movitext web2 sms
Movitext web2 smsMovitext web2 sms
Movitext web2 smsMovitext
 
filosofia contemporanea
filosofia contemporaneafilosofia contemporanea
filosofia contemporaneayolandamotta
 
Agentes modeladores do relevo externos
Agentes modeladores do relevo externosAgentes modeladores do relevo externos
Agentes modeladores do relevo externosFernanda Lopes
 

Viewers also liked (14)

Nuevo aviso
Nuevo avisoNuevo aviso
Nuevo aviso
 
IBP-Medi Care Movables
IBP-Medi Care MovablesIBP-Medi Care Movables
IBP-Medi Care Movables
 
Daily production schedule steampunk productions
Daily production schedule steampunk productionsDaily production schedule steampunk productions
Daily production schedule steampunk productions
 
Love 38
Love 38Love 38
Love 38
 
Los seres vivos
Los seres vivosLos seres vivos
Los seres vivos
 
Social Media más que una herramienta de Marketing
Social Media más que una herramienta de MarketingSocial Media más que una herramienta de Marketing
Social Media más que una herramienta de Marketing
 
CAE Banner_Library
CAE Banner_LibraryCAE Banner_Library
CAE Banner_Library
 
Jessie resume copy 2
Jessie resume copy 2Jessie resume copy 2
Jessie resume copy 2
 
Web 2.0
Web 2.0Web 2.0
Web 2.0
 
Movitext web2 sms
Movitext web2 smsMovitext web2 sms
Movitext web2 sms
 
filosofia contemporanea
filosofia contemporaneafilosofia contemporanea
filosofia contemporanea
 
F License Certificate US Soccer
F License Certificate US SoccerF License Certificate US Soccer
F License Certificate US Soccer
 
Flashcards as grandes navegações
Flashcards as grandes navegaçõesFlashcards as grandes navegações
Flashcards as grandes navegações
 
Agentes modeladores do relevo externos
Agentes modeladores do relevo externosAgentes modeladores do relevo externos
Agentes modeladores do relevo externos
 

Similar to Ch04 4 (20)

Ch03 2
Ch03 2Ch03 2
Ch03 2
 
Ch04 2
Ch04 2Ch04 2
Ch04 2
 
Ma2002 1.14 rm
Ma2002 1.14 rmMa2002 1.14 rm
Ma2002 1.14 rm
 
Ch07 4
Ch07 4Ch07 4
Ch07 4
 
Ch07 9
Ch07 9Ch07 9
Ch07 9
 
E041046051
E041046051E041046051
E041046051
 
legendre.pptx
legendre.pptxlegendre.pptx
legendre.pptx
 
Ch13
Ch13Ch13
Ch13
 
doc
docdoc
doc
 
Imc2016 day2-solutions
Imc2016 day2-solutionsImc2016 day2-solutions
Imc2016 day2-solutions
 
Term paper inna_tarasyan
Term paper inna_tarasyanTerm paper inna_tarasyan
Term paper inna_tarasyan
 
Ch03 1
Ch03 1Ch03 1
Ch03 1
 
Control chap10
Control chap10Control chap10
Control chap10
 
Ch05 6
Ch05 6Ch05 6
Ch05 6
 
Ma2002 1.19 rm
Ma2002 1.19 rmMa2002 1.19 rm
Ma2002 1.19 rm
 
Slides erasmus
Slides erasmusSlides erasmus
Slides erasmus
 
Manual solucoes ex_extras
Manual solucoes ex_extrasManual solucoes ex_extras
Manual solucoes ex_extras
 
Manual solucoes ex_extras
Manual solucoes ex_extrasManual solucoes ex_extras
Manual solucoes ex_extras
 
Manual solucoes ex_extras
Manual solucoes ex_extrasManual solucoes ex_extras
Manual solucoes ex_extras
 
Discrete control2 converted
Discrete control2 convertedDiscrete control2 converted
Discrete control2 converted
 

More from Rendy Robert (20)

Ch08 3
Ch08 3Ch08 3
Ch08 3
 
Ch08 1
Ch08 1Ch08 1
Ch08 1
 
Ch07 5
Ch07 5Ch07 5
Ch07 5
 
Ch07 3
Ch07 3Ch07 3
Ch07 3
 
Ch07 2
Ch07 2Ch07 2
Ch07 2
 
Ch07 1
Ch07 1Ch07 1
Ch07 1
 
Ch06 6
Ch06 6Ch06 6
Ch06 6
 
Ch06 5
Ch06 5Ch06 5
Ch06 5
 
Ch06 4
Ch06 4Ch06 4
Ch06 4
 
Ch06 3
Ch06 3Ch06 3
Ch06 3
 
Ch06 2
Ch06 2Ch06 2
Ch06 2
 
Ch06 1
Ch06 1Ch06 1
Ch06 1
 
Ch05 8
Ch05 8Ch05 8
Ch05 8
 
Ch05 7
Ch05 7Ch05 7
Ch05 7
 
Ch05 5
Ch05 5Ch05 5
Ch05 5
 
Ch05 4
Ch05 4Ch05 4
Ch05 4
 
Ch05 3
Ch05 3Ch05 3
Ch05 3
 
Ch05 2
Ch05 2Ch05 2
Ch05 2
 
Ch05 1
Ch05 1Ch05 1
Ch05 1
 
Ch04 3
Ch04 3Ch04 3
Ch04 3
 

Ch04 4

  • 1. Ch 4.4: Variation of Parameters The variation of parameters method can be used to find a particular solution of the nonhomogeneous nth order linear differential equation provided g is continuous. As with 2nd order equations, begin by assuming y1, y2 …, yn are fundamental solutions to homogeneous equation. Next, assume the particular solution Y has the form where u1, u2,… un are functions to be solved for. In order to find these n functions, we need n equations. [ ] ),()()()( 1 )1( 1 )( tgytpytpytpyyL nn nn =+′+++= − −  )()()()()()()( 2211 tytutytutytutY nn+++= 
  • 2. Variation of Parameters Derivation (2 of 5) First, consider the derivatives of Y: If we require then Thus we next require Continuing in this way, we require and hence ( ) ( )nnnn yuyuyuyuyuyuY ′++′+′+′++′+′=′  22112211 02211 =′++′+′ nn yuyuyu  ( ) ( )nnnn yuyuyuyuyuyuY ′′++′′+′′+′′++′′+′′=′′  22112211 02211 =′′++′′+′′ nn yuyuyu  1,,1,0)1()1( 2 )1( 11 2 −==′++′+′ −−− nkyuyuyu k n kk n  1,,1,0,)()( 11 )( −=++= nkyuyuY k nn kk 
  • 3. Variation of Parameters Derivation (3 of 5) From the previous slide, Finally, Next, substitute these derivatives into our equation Recalling that y1, y2 …, yn are solutions to homogeneous equation, and after rearranging terms, we obtain ( ) ( ))()( 11 )1()1( 11 )( n nn nn nn nn yuyuyuyuY +++′++′= −−  1,,1,0,)()( 11 )( −=++= nkyuyuY k nn kk  gyuyu n nn n =′++′ −− )1()1( 11  )()()()( 1 )1( 1 )( tgytpytpytpy nn nn =+′+++ − − 
  • 4. Variation of Parameters Derivation (4 of 5) The n equations needed in order to find the n functions u1, u2,… un are Using Cramer’s Rule, for each k = 1, …, n, and Wk is determinant obtained by replacing kth column of W with (0, 0, …, 1). gyuyu yuyu yuyu n nn n nn n =′++′ =′′++′′ =′++′ −− )1()1( 11 11 111 0 0     ))(,,()(where, )( )()( )( 1 tyyWtW tW tWtg tu n k k ==′
  • 5. Variation of Parameters Derivation (5 of 5) From the previous slide, Integrate to obtain u1, u2,… un: Thus, a particular solution Y is given by where t0 is arbitrary. nk tW tWtg tu k k ,,1, )( )()( )( ==′ nkds sW sWsg tu t t k k ,,1, )( )()( )( 0 == ∫ ∑ ∫=       = n k k t t k tyds sW sWsg tY 1 )( )( )()( )( 0
  • 6. Example (1 of 3) Consider the equation below, along with the given solutions of corresponding homogeneous solutions y1, y2, y3: Then a particular solution of this ODE is given by It can be shown that tttt etytetyetyeyyyy − ====+′−′′−′′′ )(,)(,)(, 321 2 ∑ ∫=       = 3 1 2 )( )( )( )( 0 k k t t k s tyds sW sWe tY ( ) ( ) t ttt ttt ttt e eete eete etee tW 4 2 1)( = + −+= − − −
  • 7. Example (2 of 3) Also, ( ) ( ) ( ) ( ) t tt tt tt tt tt tt tt tt tt e ete ete tee tW ee ee ee tW t eet eet ete tW = + += =−= −−= + −+= − − − − − − 12 01 0 )( 2 1 0 0 )( 12 21 10 0 )( 3 2 1
  • 8. Example (3 of 3) Thus a particular solution is Choosing t0 = 0, we obtain More simply, ( ) ( ) ∫∫∫ ∫∫∫ ∑ ∫ − − = +++−= ++ −− =       = t t s t t t s t t t s t t t s ss t t t s s t t t s s t k k t t k s dse e dse te dsse e ds e ee eds e e teds e se e tyds sW sWe tY 000 000 0 4 2222 3 1 2 42 12 4 44 2 4 12 )( )( )( )( tttt eeteetY 2 3 1 12 1 2 1 4 1 )( +−−−= − t etY 2 3 1 )( =