The document discusses linear transformations and their properties. It defines key concepts such as the kernel, range, rank, and nullity of a linear transformation. The kernel is the set of all vectors that map to the zero vector, and is a subspace of the domain. The range is the set of images of all vectors under the transformation. The rank is the dimension of the range, and the nullity is the dimension of the kernel. A linear transformation is one-to-one if different vectors always map to different outputs, and onto if its range is equal to the codomain. An isomorphism is a linear transformation that is both one-to-one and onto, and maps spaces to spaces of the same dimension.
linear transformation and rank nullity theorem Manthan Chavda
In these notes, I will present everything we know so far about linear transformations.
This material comes from sections in the book, and supplemental that
I talk about in class.
Linear Combination, Span And Linearly Independent, Dependent SetDhaval Shukla
In this presentation, the topic of Linear Combination, Span and Linearly Independent and Linearly Dependent Sets have been discussed. The sums for each topic have been given to understand the concept clearly for viewers.
Classification of signals
Deterministic and Random signals
Continuous time and discrete time signal
Even (symmetric) and Odd (Anti-symmetric) signal
Periodic and Aperiodic signal
Energy and Power signal
Causal and Non-causal signal
laplace transform and inverse laplace, properties, Inverse Laplace Calculatio...Waqas Afzal
Laplace Transform
-Proof of common function
-properties
-Initial Value and Final Value Problems
Inverse Laplace Calculations
-by identification
-Partial fraction
Solution of Ordinary differential using Laplace and inverse Laplace
Representation of signals & Operation on signals
(Time Reversal, Time Shifting , Time Scaling, Amplitude scaling, Signal addition, Signal Multiplication)
linear transformation and rank nullity theorem Manthan Chavda
In these notes, I will present everything we know so far about linear transformations.
This material comes from sections in the book, and supplemental that
I talk about in class.
Linear Combination, Span And Linearly Independent, Dependent SetDhaval Shukla
In this presentation, the topic of Linear Combination, Span and Linearly Independent and Linearly Dependent Sets have been discussed. The sums for each topic have been given to understand the concept clearly for viewers.
Classification of signals
Deterministic and Random signals
Continuous time and discrete time signal
Even (symmetric) and Odd (Anti-symmetric) signal
Periodic and Aperiodic signal
Energy and Power signal
Causal and Non-causal signal
laplace transform and inverse laplace, properties, Inverse Laplace Calculatio...Waqas Afzal
Laplace Transform
-Proof of common function
-properties
-Initial Value and Final Value Problems
Inverse Laplace Calculations
-by identification
-Partial fraction
Solution of Ordinary differential using Laplace and inverse Laplace
Representation of signals & Operation on signals
(Time Reversal, Time Shifting , Time Scaling, Amplitude scaling, Signal addition, Signal Multiplication)
Research Inventy : International Journal of Engineering and Scienceresearchinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
This chapter provides complete solution of different circuits using Laplace transform method and also provides information about applications of Laplace transforms.
state space representation,State Space Model Controllability and Observabilit...Waqas Afzal
State Variables of a Dynamical System
State Variable Equation
Why State space approach
Block Diagram Representation Of State Space Model
Controllability and Observability
Derive Transfer Function from State Space Equation
Time Response and State Transition Matrix
Eigen Value
On an Optimal control Problem for Parabolic Equationsijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Combustion Chamber for Compression Ignition EnginesKaushal Patel
Description of various types of combustion chambers for compression ignition engines, various types of swirls, primary combustion considerations, advantages and disadvantages of various types of swirls and combustion chambers.
describe how elliptical trammel work and mathematics behind it, some calculations. you can make your own elliptical trammel some define range by using mathematics for controlling shape of ellipse.
Giving description about time response, what are the inputs supplied to system, steady state response, effect of input on steady state error, Effect of Open Loop Transfer Function on Steady State Error, type 0,1 & 2 system subjected to step, ramp & parabolic input, transient response, analysis of first and second order system and transient response specifications
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
2. Linear Transformation
Zero transformation:
VWVT vu,,:
VT vv ,0)(
Identity transformation:
VVT : VT vvv ,)(
Properties of linear transformations
WVT :
00 )((1)T
)()((2) vv TT
)()()((3) vuvu TTT
)()()(
)()(Then
If(4)
2211
2211
2211
nn
nn
nn
vTcvTcvTc
vcvcvcTT
vcvcvc
v
v
2
3. The Kernel and Range of a Linear Transformation
› Kernel of a linear transformation T:
Let be a linear transformationWVT :
Then the set of all vectors v in V that satisfy is called
the kernel of T and is denoted by ker(T).
0)( vT
},0)(|{)ker( VTT vvv
Ex 1: (Finding the kernel of a linear transformation)
):()( 3223 MMTAAT T
Sol:
00
00
00
)ker(T
3
4. The kernel is a subspace of V
The kernel of a linear transformation is a
subspace of the domain V.
)16.Theorem(0)0( TPf:
VT ofsubsetnonemptyais)ker(
then.ofkernelin thevectorsbeandLet Tvu
000)()()( vuvu TTT
00)()( ccTcT uu )ker(Tc u
)ker(T vu
.ofsubspaceais)ker(Thus, VT
Note:
The kernel of T is sometimes called the nullspace of T.
WVT :
4
5. Ex 6: (Finding a basis for the kernel)
82000
10201
01312
11021
andRiniswhere,)(bydefinedbe:Let 545
A
ATRRT xxx
Find a basis for ker(T) as a subspace of R5.
5
7. Range of a linear transformation T:
)(bydenotedisandTofrangethecalledisVin
vectorofimagesarein W thatwvectorsallofsetThen the
L.T.abe:Let
Trange
WVT
}|)({)( VTTrange vv
7
8. .:Tnnsformatiolinear traaofrangeThe WWV foecapsbusasi
The range of T is a subspace of W
Pf:
)1Thm.6.(0)0( T
WTrange ofsubsetnonemptyais)(
TTT ofrangein thevectorbe)(and)(Let vu
)()()()( TrangeTTT vuvu
)()()( TrangecTcT uu
),( VVV vuvu
)( VcV uu
.subspaceis)(Therefore, WTrange
8
10. Ex 7: (Finding a basis for the range of a linear transformation)
82000
10201
01312
11021
andiswhere,)(bydefinedbe:Let 545
A
RATRRT xxx
Find a basis for the range of T.
10
12. Rank of a linear transformation T:V→W:
TTrank ofrangetheofdimensionthe)(
Nullity of a linear transformation T:V→W:
TTnullity ofkerneltheofdimensionthe)(
Note:
)()(
)()(
then,)(bygivenL.T.thebe:Let
AnullityTnullity
ArankTrank
ATRRT mn
xx
Rank and Nullity of Linear Transformation
12
13. then.spacevectorainto
spacevectorldimensiona-nanformL.T.abe:Let
W
VWVT
Sum of rank and nullity
Pf:
AmatrixnmT anbydrepresenteisLet
)ofdomaindim()ofkerneldim()ofrangedim(
)()(
TTT
nTnullityTrank
rArank )(Assume
rArank
ATTrank
)(
)ofspacecolumndim()ofrangedim()((1)
nrnrTnullityTrank )()()(
rn
ATTnullity
)ofspacesolutiondim()ofkerneldim()()2(
13
14. Ex 8: (Finding the rank and nullity of a linear transformation)
000
110
201
bydefine:L.T.theofnullityandranktheFind 33
A
RRT
Sol:
123)()ofdomaindim()(
2)()(
TrankTTnullity
ArankTrank
14
15. Ex 9: (Finding the rank and nullity of a linear transformation)
}0{)(ifofranktheFind)(
4isofnullitytheifofranktheFind)(
2israngetheof
dimensiontheifofkerneltheofdimensiontheFind)(
n.nsformatiolinear traabe:Let 75
TKerTc
TTb
Ta
RRT
Sol:
325)ofrangedim()ofkerneldim(
5)ofdomaindim()(
TnT
Ta
145)()()( TnullitynTrankb
505)()()( TnullitynTrankc
15
19. Ex 10: (One-to-one and not one-to-one linear transformation)
one.-to-oneis
)(bygiven:L.T.The)( T
mnnm AATMMTa
matrix.zeroonly theofconsistskernelitsBecause nm
one.-to-onenotis:ationtransformzeroThe)( 33
RRTb
.ofalliskernelitsBecause 3
R
19
23. .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.
23
24. Inverse linear Transformation
ineveryfors.t.L.T.are:and:If 21
nnnnn
RRRTRRT v
))((and))(( 2112 vvvv TTTT
invertiblebetosaidisandofinversethecalledisThen 112 TTT
Note:
If the transformation T is invertible, then the inverse is
unique and denoted by T–1 .
24
25. Existence of an inverse transformation
.equivalentareconditionfollowingThen the
,matrixstandardwithL.T.abe:Let ARRT nn
Note:
If T is invertible with standard matrix A, then the standard
matrix for T–1 is A–1 .
(1) T is invertible.
(2) T is an isomorphism.
(3) A is invertible.
25
26. Finding the inverse of a linear transformation
bydefinedis:L.T.The 33
RRT
)42,33,32(),,( 321321321321 xxxxxxxxxxxxT
Sol:
142
133
132
formatrixstandardThe
A
T
321
321
321
42
33
32
xxx
xxx
xxx
100142
010133
001132
3IA
Show that T is invertible, and find its inverse.
26
33. Matrices for Linear Transformations
)43,23,2(),,()1( 32321321321 xxxxxxxxxxxT
Three reasons for matrix representation of a linear transformation:
3
2
1
430
231
112
)()2(
x
x
x
AT xx
It is simpler to write.
It is simpler to read.
It is more easily adapted for computer use.
Two representations of the linear transformation T:R3→R3 :
33
34. Definition 1: A nonzero vector x is an eigenvector (or characteristic vector)
of a square matrix A if there exists a scalar λ such that Ax = λx. Then λ is an
eigenvalue (or characteristic value) of A.
Note: The zero vector can not be an eigenvector even though A0 = λ0. But λ
= 0 can be an eigenvalue.
Example: Show x
2
1
isaneigenvector for A
2 4
3 6
Solution: Ax
2 4
3 6
2
1
0
0
But for 0, x 0
2
1
0
0
Thus,xisaneigenvectorof A,and 0 isaneigenvalue.
Definitions
34
35. An n×n matrix A multiplied by n×1 vector x results in another n×1
vector y=Ax. Thus A can be considered as a transformation matrix.
In general, a matrix acts on a vector by changing both its magnitude
and its direction. However, a matrix may act on certain vectors by
changing only their magnitude, and leaving their direction
unchanged (or possibly reversing it). These vectors are the
eigenvectors of the matrix.
A matrix acts on an eigenvector by multiplying its magnitude by a
factor, which is positive if its direction is unchanged and negative if
its direction is reversed. This factor is the eigenvalue associated
with that eigenvector.
Geometric interpretation of
Eigenvalues and Eigenvectors
35
36. Let x be an eigenvector of the matrix A. Then there must exist an eigenvalue λ
such that Ax = λx or, equivalently,
Ax - λx = 0 or
(A – λI)x = 0
If we define a new matrix B = A – λI, then
Bx = 0
If B has an inverse then x = B-10 = 0. But an eigenvector cannot be zero.
Thus, it follows that x will be an eigenvector of A if and only if B does not have
an inverse, or equivalently det(B)=0, or
det(A – λI) = 0
This is called the characteristic equation of A. Its roots determine the
eigenvalues of A.
Eigenvalues
36
37. Eigenvalues: examples
Example 1: Find the eigenvalues of
two eigenvalues: 1, 2
Note: The roots of the characteristic equation can be repeated. That is, λ1 = λ2 =…=
λk. If that happens, the eigenvalue is said to be of multiplicity k.
51
122
A
)2)(1(23
12)5)(2(
51
122
2
AI
37
38. Eigenvectors
Example 1 (cont.):
00
41
41
123
)1(:1 AI
0,
1
4
,404
2
1
1
2121
tt
x
x
txtxxx
x
00
31
31
124
)2(:2 AI
0,
1
3
2
1
2
ss
x
x
x
To each distinct eigenvalue of a matrix A there will correspond at least one
eigenvector which can be found by solving the appropriate set of homogenous
equations. If λi is an eigenvalue then the corresponding eigenvector xi is the
solution of (A – λiI)xi = 0
38
39. Example 2 (cont.): Find the eigenvectors of
Recall that λ = 2 is an eigenvector of multiplicity 3.
Solve the homogeneous linear system represented by
Let . The eigenvectors of = 2
are of the form
and t not both zero.
0
0
0
000
000
010
)2(
3
2
1
x
x
x
AI x
txsx 31 ,
,
1
0
0
0
0
1
0
3
2
1
ts
t
s
x
x
x
x
200
020
012
A
39
40. Definition: The trace of a matrix A, designated by tr(A), is the sum of the elements on the
main diagonal.
Property 1: The sum of the eigenvalues of a matrix equals the trace of the matrix.
Property 2: A matrix is singular if and only if it has a zero eigenvalue.
Property 3: The eigenvalues of an upper (or lower) triangular matrix are the elements on
the main diagonal.
Property 4: If λ is an eigenvalue of A and A is invertible, then 1/λ is an eigenvalue of
matrix A-1.
Properties of Eigenvalues and Eigenvectors
40
41. Property 5: If λ is an eigenvalue of A then kλ is an eigenvalue of
kA where k is any arbitrary scalar.
Property 6: If λ is an eigenvalue of A then λk is an eigenvalue of
Ak for any positive integer k.
Property 8: If λ is an eigenvalue of A then λ is an eigenvalue of AT.
Property 9: The product of the eigenvalues (counting multiplicity)
of a matrix equals the determinant of the matrix.
41
42. Theorem: Eigenvectors corresponding to distinct (that is, different) eigenvalues
are linearly independent.
Theorem: If λ is an eigenvalue of multiplicity k of an n n matrix A then the
number of linearly independent eigenvectors of A associated with λ is given
by m = n - r(A- λI). Furthermore, 1 ≤ m ≤ k.
Example 2 (cont.): The eigenvectors of = 2 are of the form
s and t not both zero.
= 2 has two linearly independent eigenvectors
,
1
0
0
0
0
1
0
3
2
1
ts
t
s
x
x
x
x
Linearly independent eigenvectors
42
43. Diagonalization
Diagonalizable matrix:
A square matrix A is called diagonalizable if there exists an
invertible matrix P such that P-1AP is a diagonal matrix.
(P diagonalizes A)
Notes:
(1) If there exists an invertible matrix P such that ,
then two square matrices A and B are called similar.
(2) The eigenvalue problem is related closely to the
diagonalization problem.
APPB 1
43
46. Condition for Diagonalization
An nn matrix A is diagonalizable if and only if
it has n linearly independent eigenvectors.
Pf:
ablediagonalizis)( A
),,,(and][Let
diagonaliss.t.invertibleanexiststhere
2121
1
nn diagDpppP
APPDP
][
00
00
00
][
2211
2
1
21
nn
n
n
ppp
pppPD
46
47. ][][ 2121 nn
ApApAppppAAP
)ofrseigenvectoareoftorcolumn vecthe..(
,,2,1,
APpei
nipAp
PDAP
i
iii
t.independenlinearlyare,,,invertibleis 21 npppP
rs.eigenvectotindependenlinearlyhas nA
n
npppnA
,,seigenvalueingcorrespondhwit
,,rseigenvectotindependenlinearlyhas)(
21
21
nipAp iii ,,2,1,i.e.
][Let 21 n
pppP
47
49. A matrix that is not diagonalizable
10
21
able.diagonaliznotismatrixfollowingthat theShow
A
Sol: Characteristic equation:
0)1(
10
21
I 2
A
1:Eigenvalue 1
0
1
:rEigenvecto
00
10
~
00
20
I 1pAIA
A does not have two (n=2) linearly independent eigenvectors,
so A is not diagonalizable.
49
50. Steps for diagonalizing an nn square matrix:
n ,,, 21
Step 2: Let ][ 21 npppP
Step 1: Find n linearly independent eigenvectors
for A with corresponding eigenvalues
nppp ,,, 21
Step 3:
nipApDAPP iii
n
,,2,1,where,
00
00
00
2
1
1
Note:
The order of the eigenvalues used to form P will determine
the order in which the eigenvalues appear on the main diagonal of D.
50
54. Notes: k is a positive integer
k
n
k
k
k
n d
d
d
D
d
d
d
D
00
00
00
00
00
00
)1( 2
1
2
1
1
1
1
1111
111
1
1
)()()(
)())((
)(
)2(
PPDA
PAP
APAAP
APPPPPAPPAP
APPAPPAPP
APPD
APPD
kk
k
kk
54
55. Sufficient conditions for Diagonalization
If an nn matrix A has n distinct eigenvalues, then the
corresponding eigenvectors are linearly independent and
A is diagonalizable.
55
56. Determining whether a matrix is diagonalizable
300
100
121
A
Sol: Because A is a triangular matrix,
its eigenvalues are the main diagonal entries.
3,0,1 321
These three values are distinct, so A is diagonalizable. (Thm.7.6)
56
57. Finding a diagonalizing matrix for a linear transformation
diagonal.istorelative
formatrixthesuch thatforbasisaFind
)33()(
bygivennnsformatiolinear trathebeLet
3
321321321321
33
B
TRB
xxx,xx, xxxx,x,xxT
RT:R
Sol:
113
131
111
)()()(
bygivenisformatrixstandardThe
321 eTeTeTA
T
From Ex. 5, there are three distinct eigenvalues
so A is diagonalizable. (Thm. 7.6)
3,2,2 321
57
58.
300
020
002
][][][
][][][
])([)]([])([
332211
321
321
BBB
BBB
BBB
ppp
ApApAp
pTpTpTD
The matrix for T relative to this basis is
)}1,1,1(),4,1,1(),1,0,1{(},,{ 321 pppB
Thus, the three linearly independent eigenvectors found in Ex. 5
can be used to form the basis B. That is
)1,1,1(),4,1,1(),1,0,1( 321 ppp
58