SlideShare a Scribd company logo
1 of 9
Name:- Dhanraj Vaghela
Branch:- Mechanical
Sem:- 02
Enrollment- 140990119060
From the desk of Dhanraj from SRICT
 Isomorphism:
other.eachtoisomorphicbetosaidare
andthen,tofrommisomorphisanexiststheresuch that
spacesvectorareandifMoreover,m.isomorphisancalledis
ontoandonetooneisthat:nnsformatiolinear traA
WVWV
WV
WVT 
 Thm 6.9: (Isomorphic spaces and dimension)
Pf:
.dimensionhaswhere,toisomorphicisthatAssume nVWV
onto.andonetooneisthat:L.T.aexistsThere WVT 
one-to-oneisT
nnTKerTT
TKer


0))(dim()ofdomaindim()ofrangedim(
0))(dim(
Two finite-dimensional vector space V and W are isomorphic if and only if
they are of the same dimension.
From the desk of Dhanraj from SRICT
.dimensionhavebothandthatAssume nWV
onto.isTnWT  )dim()ofrangedim(
nWV  )dim()dim(Thus
 
  .ofbasisabe,,,let
andV,ofbasisabe,,,Let
21
21
Wwww
vvv
n
n


nnvcvcvc
V
 2211
asdrepresentebecaninvectorarbitraryanThen
v
nnwcwcwcT
WVT


2211)(
follows.as:L.T.adefinecanyouand
v
It can be shown that this L.T. is both 1-1 and onto.
Thus V and W are isomorphic.
From the desk of Dhanraj from SRICT
vector.singleaofconsistsrangein theevery w
ofpreimagetheifone-to-onecalledis:functionA WVT 
 One-to-one:
.thatimplies
)()(inV,vanduallforiffone-to-oneis
vu
vu

 TTT
one-to-one not one-to-one
From the desk of Dhanraj from SRICT
Some important theorems related to one to one transformation
 Thm 1: A linear transformation T : V -> W is one to one if and only if ker(T)
={0}.
 Thm 2: A linear transformation T : V -> W is one to one if and only if
dim(ker(T)) = 0, i.e., nullity (T) = 0.
 Thm 3: A linear transformation T : V -> W is one to one if and only if
rank(T)=dim V.
 Thm 4: If A is an m x n matrix and TA : Rn -> Rn is multiplication by A then
TA is one to one if and only if rank (A)= n.
 Thm 5: If A is an n x n matrix and TA : Rn -> Rn is multiplication by A then
TA is one to one if and only if A is an invertible matrix.
From the desk of Dhanraj from SRICT
inpreimageahasin
elementeveryifontobetosaidis:functionA
V
WVT
w

 Onto:
(T is onto W when W is equal to the range of T.)
Thm 1: A linear transformation T : V -> W is onto if and only if rank (T)
= dim W
Thm 2: If A is an m x n matrix and TA : Rn -> Rm is multiplication by A
then TA is onto if and only if rank (A) = m.
 Let T : V -> W be a linear transformation and let dim V = dim W
(i) If T is one-to-one ,then it is onto.
(ii) If T is onto, then it is one-to-one.
From the desk of Dhanraj from SRICT
 Example :
neither.oronto,one,-to-oneiswhetherdetermineandof
rankandnullitytheFind,)(bygivenis:L.T.The
TT
ATRRT mn
xx 









100
110
021
)( Aa









00
10
21
)( Ab






110
021
)( Ac









000
110
021
)( Ad
Sol:
T:Rn→Rm dim(domain of T) rank(T) nullity(T) 1-1 onto
(a)T:R3→R3 3 3 0 Yes Yes
(b)T:R2→R3 2 2 0 Yes No
(c)T:R3→R2 3 2 1 No Yes
(d)T:R3→R3 3 2 1 No No
From the desk of Dhanraj from SRICT
 (Isomorphic vector spaces)
space-4)( 4
Ra
matrices14allofspace)( 14 Mb
matrices22allofspace)( 22 Mc
lessor3degreeofspolynomialallofspace)()( 3 xPd
)ofsubspace}(numberrealais),0,,,,{()( 5
4321 RxxxxxVe i
The following vector spaces are isomorphic to each other.
From the desk of Dhanraj from SRICT
Thank You
From the desk of Dhanraj from SRICT

More Related Content

What's hot

Math: Distance Formula
Math: Distance FormulaMath: Distance Formula
Math: Distance FormulaPadme Amidala
 
5.3 Graphs of Polynomial Functions
5.3 Graphs of Polynomial Functions5.3 Graphs of Polynomial Functions
5.3 Graphs of Polynomial Functionssmiller5
 
Rectangular coordinate system
Rectangular coordinate systemRectangular coordinate system
Rectangular coordinate systemCathy Francisco
 
Set theory
Set theorySet theory
Set theoryGaditek
 
Truth, deduction, computation lecture i (last one)
Truth, deduction, computation   lecture i (last one)Truth, deduction, computation   lecture i (last one)
Truth, deduction, computation lecture i (last one)Vlad Patryshev
 
Percentile
PercentilePercentile
PercentileDMCI
 
Measures of Central Tendency: Ungrouped and Grouped
Measures of Central Tendency: Ungrouped and GroupedMeasures of Central Tendency: Ungrouped and Grouped
Measures of Central Tendency: Ungrouped and GroupedMaryGraceRecaaAgusti
 
Function or not function
Function or not functionFunction or not function
Function or not functionMartinGeraldine
 
1.1 ss factoring the difference of two squares
1.1 ss factoring the difference of two squares1.1 ss factoring the difference of two squares
1.1 ss factoring the difference of two squaresJessebelBautista
 
Counting
CountingCounting
Countingrfant
 
Discrete mathematic
Discrete mathematicDiscrete mathematic
Discrete mathematicNaralaswapna
 
MATH-8 WEEKS 7 Q3.pptx
MATH-8 WEEKS 7 Q3.pptxMATH-8 WEEKS 7 Q3.pptx
MATH-8 WEEKS 7 Q3.pptxLARRYAZARIAS1
 
systems of linear equations & matrices
systems of linear equations & matricessystems of linear equations & matrices
systems of linear equations & matricesStudent
 
Real Analysis II (Measure Theory) Notes
Real Analysis II (Measure Theory) NotesReal Analysis II (Measure Theory) Notes
Real Analysis II (Measure Theory) NotesPrakash Dabhi
 

What's hot (20)

Math1.2
Math1.2Math1.2
Math1.2
 
Math: Distance Formula
Math: Distance FormulaMath: Distance Formula
Math: Distance Formula
 
Modern Geometry Topics
Modern Geometry TopicsModern Geometry Topics
Modern Geometry Topics
 
5.3 Graphs of Polynomial Functions
5.3 Graphs of Polynomial Functions5.3 Graphs of Polynomial Functions
5.3 Graphs of Polynomial Functions
 
Rectangular coordinate system
Rectangular coordinate systemRectangular coordinate system
Rectangular coordinate system
 
Set theory
Set theorySet theory
Set theory
 
Polygons
PolygonsPolygons
Polygons
 
Truth, deduction, computation lecture i (last one)
Truth, deduction, computation   lecture i (last one)Truth, deduction, computation   lecture i (last one)
Truth, deduction, computation lecture i (last one)
 
Percentile
PercentilePercentile
Percentile
 
Measures of Central Tendency: Ungrouped and Grouped
Measures of Central Tendency: Ungrouped and GroupedMeasures of Central Tendency: Ungrouped and Grouped
Measures of Central Tendency: Ungrouped and Grouped
 
Function or not function
Function or not functionFunction or not function
Function or not function
 
1.1 ss factoring the difference of two squares
1.1 ss factoring the difference of two squares1.1 ss factoring the difference of two squares
1.1 ss factoring the difference of two squares
 
Analytical geometry
Analytical geometryAnalytical geometry
Analytical geometry
 
Mean of grouped data
Mean of grouped dataMean of grouped data
Mean of grouped data
 
Counting
CountingCounting
Counting
 
Discrete mathematic
Discrete mathematicDiscrete mathematic
Discrete mathematic
 
MATH-8 WEEKS 7 Q3.pptx
MATH-8 WEEKS 7 Q3.pptxMATH-8 WEEKS 7 Q3.pptx
MATH-8 WEEKS 7 Q3.pptx
 
Sets and relations
Sets and relationsSets and relations
Sets and relations
 
systems of linear equations & matrices
systems of linear equations & matricessystems of linear equations & matrices
systems of linear equations & matrices
 
Real Analysis II (Measure Theory) Notes
Real Analysis II (Measure Theory) NotesReal Analysis II (Measure Theory) Notes
Real Analysis II (Measure Theory) Notes
 

Similar to isomorphism

linear transformation
linear transformationlinear transformation
linear transformationmansi acharya
 
Fourier Transform ,LAPLACE TRANSFORM,ROC and its Properties
Fourier Transform ,LAPLACE TRANSFORM,ROC and its Properties Fourier Transform ,LAPLACE TRANSFORM,ROC and its Properties
Fourier Transform ,LAPLACE TRANSFORM,ROC and its Properties Dr.SHANTHI K.G
 
linear transfermation.pptx
linear transfermation.pptxlinear transfermation.pptx
linear transfermation.pptxUmme habiba
 
Linear transformation vcla (160920107003)
Linear transformation vcla (160920107003)Linear transformation vcla (160920107003)
Linear transformation vcla (160920107003)Prashant odhavani
 
linear tranformation- VC&LA
linear tranformation- VC&LAlinear tranformation- VC&LA
linear tranformation- VC&LAKaushal Patel
 
MATH 200-004 Multivariate Calculus Winter 2014Chapter 12.docx
MATH 200-004 Multivariate Calculus Winter 2014Chapter 12.docxMATH 200-004 Multivariate Calculus Winter 2014Chapter 12.docx
MATH 200-004 Multivariate Calculus Winter 2014Chapter 12.docxandreecapon
 
Study on Impact of Media on Education Using Fuzzy Relational Maps
Study on Impact of Media on Education Using Fuzzy Relational MapsStudy on Impact of Media on Education Using Fuzzy Relational Maps
Study on Impact of Media on Education Using Fuzzy Relational MapsMangaiK4
 
Z transforms and their applications
Z transforms and their applicationsZ transforms and their applications
Z transforms and their applicationsRam Kumar K R
 
dsp dsp by Dr. k Udaya kumar power point
dsp dsp by Dr. k Udaya kumar power pointdsp dsp by Dr. k Udaya kumar power point
dsp dsp by Dr. k Udaya kumar power pointAnujKumar734472
 
Digital Signal Processing and the z-transform
Digital Signal Processing and the  z-transformDigital Signal Processing and the  z-transform
Digital Signal Processing and the z-transformRowenaDulay1
 
2. Power Computations and Analysis Techniques_verstud.pdf
2. Power Computations and Analysis Techniques_verstud.pdf2. Power Computations and Analysis Techniques_verstud.pdf
2. Power Computations and Analysis Techniques_verstud.pdfLIEWHUIFANGUNIMAP
 
Ss important questions
Ss important questionsSs important questions
Ss important questionsSowji Laddu
 
Linear response theory and TDDFT
Linear response theory and TDDFT Linear response theory and TDDFT
Linear response theory and TDDFT Claudio Attaccalite
 
EC8352-Signals and Systems - Laplace transform
EC8352-Signals and Systems - Laplace transformEC8352-Signals and Systems - Laplace transform
EC8352-Signals and Systems - Laplace transformNimithaSoman
 

Similar to isomorphism (20)

Isomorphism
IsomorphismIsomorphism
Isomorphism
 
linear transformation
linear transformationlinear transformation
linear transformation
 
Fourier Transform ,LAPLACE TRANSFORM,ROC and its Properties
Fourier Transform ,LAPLACE TRANSFORM,ROC and its Properties Fourier Transform ,LAPLACE TRANSFORM,ROC and its Properties
Fourier Transform ,LAPLACE TRANSFORM,ROC and its Properties
 
linear transfermation.pptx
linear transfermation.pptxlinear transfermation.pptx
linear transfermation.pptx
 
Image compression
Image compressionImage compression
Image compression
 
Linear transformation vcla (160920107003)
Linear transformation vcla (160920107003)Linear transformation vcla (160920107003)
Linear transformation vcla (160920107003)
 
linear tranformation- VC&LA
linear tranformation- VC&LAlinear tranformation- VC&LA
linear tranformation- VC&LA
 
Range-NUllity-and-Rank.pptx
Range-NUllity-and-Rank.pptxRange-NUllity-and-Rank.pptx
Range-NUllity-and-Rank.pptx
 
MATH 200-004 Multivariate Calculus Winter 2014Chapter 12.docx
MATH 200-004 Multivariate Calculus Winter 2014Chapter 12.docxMATH 200-004 Multivariate Calculus Winter 2014Chapter 12.docx
MATH 200-004 Multivariate Calculus Winter 2014Chapter 12.docx
 
farrell1966.pdf
farrell1966.pdffarrell1966.pdf
farrell1966.pdf
 
Study on Impact of Media on Education Using Fuzzy Relational Maps
Study on Impact of Media on Education Using Fuzzy Relational MapsStudy on Impact of Media on Education Using Fuzzy Relational Maps
Study on Impact of Media on Education Using Fuzzy Relational Maps
 
Kanal wireless dan propagasi
Kanal wireless dan propagasiKanal wireless dan propagasi
Kanal wireless dan propagasi
 
Z transforms and their applications
Z transforms and their applicationsZ transforms and their applications
Z transforms and their applications
 
dsp dsp by Dr. k Udaya kumar power point
dsp dsp by Dr. k Udaya kumar power pointdsp dsp by Dr. k Udaya kumar power point
dsp dsp by Dr. k Udaya kumar power point
 
Digital Signal Processing and the z-transform
Digital Signal Processing and the  z-transformDigital Signal Processing and the  z-transform
Digital Signal Processing and the z-transform
 
2. Power Computations and Analysis Techniques_verstud.pdf
2. Power Computations and Analysis Techniques_verstud.pdf2. Power Computations and Analysis Techniques_verstud.pdf
2. Power Computations and Analysis Techniques_verstud.pdf
 
Ss important questions
Ss important questionsSs important questions
Ss important questions
 
Linear response theory and TDDFT
Linear response theory and TDDFT Linear response theory and TDDFT
Linear response theory and TDDFT
 
nabil-201-chap-06.ppt
nabil-201-chap-06.pptnabil-201-chap-06.ppt
nabil-201-chap-06.ppt
 
EC8352-Signals and Systems - Laplace transform
EC8352-Signals and Systems - Laplace transformEC8352-Signals and Systems - Laplace transform
EC8352-Signals and Systems - Laplace transform
 

More from Dhanraj Vaghela

More from Dhanraj Vaghela (6)

ultracapacitor
ultracapacitorultracapacitor
ultracapacitor
 
defining purpose of presentation
defining purpose of presentationdefining purpose of presentation
defining purpose of presentation
 
book review i,m ok you,re ok
book review i,m ok you,re okbook review i,m ok you,re ok
book review i,m ok you,re ok
 
string
stringstring
string
 
demostratre trust behavior
demostratre trust behaviordemostratre trust behavior
demostratre trust behavior
 
calculus Ppt
calculus Pptcalculus Ppt
calculus Ppt
 

isomorphism

  • 1. Name:- Dhanraj Vaghela Branch:- Mechanical Sem:- 02 Enrollment- 140990119060 From the desk of Dhanraj from SRICT
  • 2.  Isomorphism: other.eachtoisomorphicbetosaidare andthen,tofrommisomorphisanexiststheresuch that spacesvectorareandifMoreover,m.isomorphisancalledis ontoandonetooneisthat:nnsformatiolinear traA WVWV WV WVT   Thm 6.9: (Isomorphic spaces and dimension) Pf: .dimensionhaswhere,toisomorphicisthatAssume nVWV onto.andonetooneisthat:L.T.aexistsThere WVT  one-to-oneisT nnTKerTT TKer   0))(dim()ofdomaindim()ofrangedim( 0))(dim( Two finite-dimensional vector space V and W are isomorphic if and only if they are of the same dimension. From the desk of Dhanraj from SRICT
  • 3. .dimensionhavebothandthatAssume nWV onto.isTnWT  )dim()ofrangedim( nWV  )dim()dim(Thus     .ofbasisabe,,,let andV,ofbasisabe,,,Let 21 21 Wwww vvv n n   nnvcvcvc V  2211 asdrepresentebecaninvectorarbitraryanThen v nnwcwcwcT WVT   2211)( follows.as:L.T.adefinecanyouand v It can be shown that this L.T. is both 1-1 and onto. Thus V and W are isomorphic. From the desk of Dhanraj from SRICT
  • 4. vector.singleaofconsistsrangein theevery w ofpreimagetheifone-to-onecalledis:functionA WVT   One-to-one: .thatimplies )()(inV,vanduallforiffone-to-oneis vu vu   TTT one-to-one not one-to-one From the desk of Dhanraj from SRICT
  • 5. Some important theorems related to one to one transformation  Thm 1: A linear transformation T : V -> W is one to one if and only if ker(T) ={0}.  Thm 2: A linear transformation T : V -> W is one to one if and only if dim(ker(T)) = 0, i.e., nullity (T) = 0.  Thm 3: A linear transformation T : V -> W is one to one if and only if rank(T)=dim V.  Thm 4: If A is an m x n matrix and TA : Rn -> Rn is multiplication by A then TA is one to one if and only if rank (A)= n.  Thm 5: If A is an n x n matrix and TA : Rn -> Rn is multiplication by A then TA is one to one if and only if A is an invertible matrix. From the desk of Dhanraj from SRICT
  • 6. inpreimageahasin elementeveryifontobetosaidis:functionA V WVT w   Onto: (T is onto W when W is equal to the range of T.) Thm 1: A linear transformation T : V -> W is onto if and only if rank (T) = dim W Thm 2: If A is an m x n matrix and TA : Rn -> Rm is multiplication by A then TA is onto if and only if rank (A) = m.  Let T : V -> W be a linear transformation and let dim V = dim W (i) If T is one-to-one ,then it is onto. (ii) If T is onto, then it is one-to-one. From the desk of Dhanraj from SRICT
  • 7.  Example : neither.oronto,one,-to-oneiswhetherdetermineandof rankandnullitytheFind,)(bygivenis:L.T.The TT ATRRT mn xx           100 110 021 )( Aa          00 10 21 )( Ab       110 021 )( Ac          000 110 021 )( Ad Sol: T:Rn→Rm dim(domain of T) rank(T) nullity(T) 1-1 onto (a)T:R3→R3 3 3 0 Yes Yes (b)T:R2→R3 2 2 0 Yes No (c)T:R3→R2 3 2 1 No Yes (d)T:R3→R3 3 2 1 No No From the desk of Dhanraj from SRICT
  • 8.  (Isomorphic vector spaces) space-4)( 4 Ra matrices14allofspace)( 14 Mb matrices22allofspace)( 22 Mc lessor3degreeofspolynomialallofspace)()( 3 xPd )ofsubspace}(numberrealais),0,,,,{()( 5 4321 RxxxxxVe i The following vector spaces are isomorphic to each other. From the desk of Dhanraj from SRICT
  • 9. Thank You From the desk of Dhanraj from SRICT