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Raffi Hovasapian

Eigenvalues and Eigenvectors

Slide Duration:Table of Contents

I. Linear Equations and Matrices

Linear Systems

39m 3s

- Intro0:00
- Linear Systems1:20
- Introduction to Linear Systems1:21
- Examples10:35
- Example 110:36
- Example 213:44
- Example 316:12
- Example 423:48
- Example 528:23
- Example 632:32
- Number of Solutions35:08
- One Solution, No Solution, Infinitely Many Solutions35:09
- Method of Elimination36:57
- Method of Elimination36:58

Matrices

30m 34s

- Intro0:00
- Matrices0:47
- Definition and Example of Matrices0:48
- Square Matrix7:55
- Diagonal Matrix9:31
- Operations with Matrices10:35
- Matrix Addition10:36
- Scalar Multiplication15:01
- Transpose of a Matrix17:51
- Matrix Types23:17
- Regular: m x n Matrix of m Rows and n Column23:18
- Square: n x n Matrix With an Equal Number of Rows and Columns23:44
- Diagonal: A Square Matrix Where All Entries OFF the Main Diagonal are '0'24:07
- Matrix Operations24:37
- Matrix Operations24:38
- Example25:55
- Example25:56

Dot Product & Matrix Multiplication

41m 42s

- Intro0:00
- Dot Product1:04
- Example of Dot Product1:05
- Matrix Multiplication7:05
- Definition7:06
- Example 112:26
- Example 217:38
- Matrices and Linear Systems21:24
- Matrices and Linear Systems21:25
- Example 129:56
- Example 232:30
- Summary33:56
- Dot Product of Two Vectors and Matrix Multiplication33:57
- Summary, cont.35:06
- Matrix Representations of Linear Systems35:07
- Examples35:34
- Examples35:35

Properties of Matrix Operation

43m 17s

- Intro0:00
- Properties of Addition1:11
- Properties of Addition: A1:12
- Properties of Addition: B2:30
- Properties of Addition: C2:57
- Properties of Addition: D4:20
- Properties of Addition5:22
- Properties of Addition5:23
- Properties of Multiplication6:47
- Properties of Multiplication: A7:46
- Properties of Multiplication: B8:13
- Properties of Multiplication: C9:18
- Example: Properties of Multiplication9:35
- Definitions and Properties (Multiplication)14:02
- Identity Matrix: n x n matrix14:03
- Let A Be a Matrix of m x n15:23
- Definitions and Properties (Multiplication)18:36
- Definitions and Properties (Multiplication)18:37
- Properties of Scalar Multiplication22:54
- Properties of Scalar Multiplication: A23:39
- Properties of Scalar Multiplication: B24:04
- Properties of Scalar Multiplication: C24:29
- Properties of Scalar Multiplication: D24:48
- Properties of the Transpose25:30
- Properties of the Transpose25:31
- Properties of the Transpose30:28
- Example30:29
- Properties of Matrix Addition33:25
- Let A, B, C, and D Be m x n Matrices33:26
- There is a Unique m x n Matrix, 0, Such That…33:48
- Unique Matrix D34:17
- Properties of Matrix Multiplication34:58
- Let A, B, and C Be Matrices of the Appropriate Size34:59
- Let A Be Square Matrix (n x n)35:44
- Properties of Scalar Multiplication36:35
- Let r and s Be Real Numbers, and A and B Matrices36:36
- Properties of the Transpose37:10
- Let r Be a Scalar, and A and B Matrices37:12
- Example37:58
- Example37:59

Solutions of Linear Systems, Part 1

38m 14s

- Intro0:00
- Reduced Row Echelon Form0:29
- An m x n Matrix is in Reduced Row Echelon Form If:0:30
- Reduced Row Echelon Form2:58
- Example: Reduced Row Echelon Form2:59
- Theorem8:30
- Every m x n Matrix is Row-Equivalent to a UNIQUE Matrix in Reduced Row Echelon Form8:31
- Systematic and Careful Example10:02
- Step 110:54
- Step 211:33
- Step 312:50
- Step 414:02
- Step 515:31
- Step 617:28
- Example30:39
- Find the Reduced Row Echelon Form of a Given m x n Matrix30:40

Solutions of Linear Systems, Part II

28m 54s

- Intro0:00
- Solutions of Linear Systems0:11
- Solutions of Linear Systems0:13
- Example I3:25
- Solve the Linear System 13:26
- Solve the Linear System 214:31
- Example II17:41
- Solve the Linear System 317:42
- Solve the Linear System 420:17
- Homogeneous Systems21:54
- Homogeneous Systems Overview21:55
- Theorem and Example24:01

Inverse of a Matrix

40m 10s

- Intro0:00
- Finding the Inverse of a Matrix0:41
- Finding the Inverse of a Matrix0:42
- Properties of Non-Singular Matrices6:38
- Practical Procedure9:15
- Step19:16
- Step 210:10
- Step 310:46
- Example: Finding Inverse12:50
- Linear Systems and Inverses17:01
- Linear Systems and Inverses17:02
- Theorem and Example21:15
- Theorem26:32
- Theorem26:33
- List of Non-Singular Equivalences28:37
- Example: Does the Following System Have a Non-trivial Solution?30:13
- Example: Inverse of a Matrix36:16

II. Determinants

Determinants

21m 25s

- Intro0:00
- Determinants0:37
- Introduction to Determinants0:38
- Example6:12
- Properties9:00
- Properties 1-59:01
- Example10:14
- Properties, cont.12:28
- Properties 6 & 712:29
- Example14:14
- Properties, cont.18:34
- Properties 8 & 918:35
- Example19:21

Cofactor Expansions

59m 31s

- Intro0:00
- Cofactor Expansions and Their Application0:42
- Cofactor Expansions and Their Application0:43
- Example 13:52
- Example 27:08
- Evaluation of Determinants by Cofactor9:38
- Theorem9:40
- Example 111:41
- Inverse of a Matrix by Cofactor22:42
- Inverse of a Matrix by Cofactor and Example22:43
- More Example36:22
- List of Non-Singular Equivalences43:07
- List of Non-Singular Equivalences43:08
- Example44:38
- Cramer's Rule52:22
- Introduction to Cramer's Rule and Example52:23

III. Vectors in Rn

Vectors in the Plane

46m 54s

- Intro0:00
- Vectors in the Plane0:38
- Vectors in the Plane0:39
- Example 18:25
- Example 215:23
- Vector Addition and Scalar Multiplication19:33
- Vector Addition19:34
- Scalar Multiplication24:08
- Example26:25
- The Angle Between Two Vectors29:33
- The Angle Between Two Vectors29:34
- Example33:54
- Properties of the Dot Product and Unit Vectors38:17
- Properties of the Dot Product and Unit Vectors38:18
- Defining Unit Vectors40:01
- 2 Very Important Unit Vectors41:56

n-Vector

52m 44s

- Intro0:00
- n-Vectors0:58
- 4-Vector0:59
- 7-Vector1:50
- Vector Addition2:43
- Scalar Multiplication3:37
- Theorem: Part 14:24
- Theorem: Part 211:38
- Right and Left Handed Coordinate System14:19
- Projection of a Point Onto a Coordinate Line/Plane17:20
- Example21:27
- Cauchy-Schwarz Inequality24:56
- Triangle Inequality36:29
- Unit Vector40:34
- Vectors and Dot Products44:23
- Orthogonal Vectors44:24
- Cauchy-Schwarz Inequality45:04
- Triangle Inequality45:21
- Example 145:40
- Example 248:16

Linear Transformation

48m 53s

- Intro0:00
- Introduction to Linear Transformations0:44
- Introduction to Linear Transformations0:45
- Example 19:01
- Example 211:33
- Definition of Linear Mapping14:13
- Example 322:31
- Example 426:07
- Example 530:36
- Examples36:12
- Projection Mapping36:13
- Images, Range, and Linear Mapping38:33
- Example of Linear Transformation42:02

Linear Transformations, Part II

34m 8s

- Intro0:00
- Linear Transformations1:29
- Linear Transformations1:30
- Theorem 17:15
- Theorem 29:20
- Example 1: Find L (-3, 4, 2)11:17
- Example 2: Is It Linear?17:11
- Theorem 325:57
- Example 3: Finding the Standard Matrix29:09

Lines and Planes

37m 54s

- Intro0:00
- Lines and Plane0:36
- Example 10:37
- Example 27:07
- Lines in IR39:53
- Parametric Equations14:58
- Example 317:26
- Example 420:11
- Planes in IR325:19
- Example 531:12
- Example 634:18

IV. Real Vector Spaces

Vector Spaces

42m 19s

- Intro0:00
- Vector Spaces3:43
- Definition of Vector Spaces3:44
- Vector Spaces 15:19
- Vector Spaces 29:34
- Real Vector Space and Complex Vector Space14:01
- Example 115:59
- Example 218:42
- Examples26:22
- More Examples26:23
- Properties of Vector Spaces32:53
- Properties of Vector Spaces Overview32:54
- Property A34:31
- Property B36:09
- Property C36:38
- Property D37:54
- Property F39:00

Subspaces

43m 37s

- Intro0:00
- Subspaces0:47
- Defining Subspaces0:48
- Example 13:08
- Example 23:49
- Theorem7:26
- Example 39:11
- Example 412:30
- Example 516:05
- Linear Combinations23:27
- Definition 123:28
- Example 125:24
- Definition 229:49
- Example 231:34
- Theorem32:42
- Example 334:00

Spanning Set for a Vector Space

33m 15s

- Intro0:00
- A Spanning Set for a Vector Space1:10
- A Spanning Set for a Vector Space1:11
- Procedure to Check if a Set of Vectors Spans a Vector Space3:38
- Example 16:50
- Example 214:28
- Example 321:06
- Example 422:15

Linear Independence

17m 20s

- Intro0:00
- Linear Independence0:32
- Definition0:39
- Meaning3:00
- Procedure for Determining if a Given List of Vectors is Linear Independence or Linear Dependence5:00
- Example 17:21
- Example 210:20

Basis & Dimension

31m 20s

- Intro0:00
- Basis and Dimension0:23
- Definition0:24
- Example 13:30
- Example 2: Part A4:00
- Example 2: Part B6:53
- Theorem 19:40
- Theorem 211:32
- Procedure for Finding a Subset of S that is a Basis for Span S14:20
- Example 316:38
- Theorem 321:08
- Example 425:27

Homogeneous Systems

24m 45s

- Intro0:00
- Homogeneous Systems0:51
- Homogeneous Systems0:52
- Procedure for Finding a Basis for the Null Space of Ax = 02:56
- Example 17:39
- Example 218:03
- Relationship Between Homogeneous and Non-Homogeneous Systems19:47

Rank of a Matrix, Part I

35m 3s

- Intro0:00
- Rank of a Matrix1:47
- Definition1:48
- Theorem 18:14
- Example 19:38
- Defining Row and Column Rank16:53
- If We Want a Basis for Span S Consisting of Vectors From S22:00
- If We want a Basis for Span S Consisting of Vectors Not Necessarily in S24:07
- Example 2: Part A26:44
- Example 2: Part B32:10

Rank of a Matrix, Part II

29m 26s

- Intro0:00
- Rank of a Matrix0:17
- Example 1: Part A0:18
- Example 1: Part B5:58
- Rank of a Matrix Review: Rows, Columns, and Row Rank8:22
- Procedure for Computing the Rank of a Matrix14:36
- Theorem 1: Rank + Nullity = n16:19
- Example 217:48
- Rank & Singularity20:09
- Example 321:08
- Theorem 223:25
- List of Non-Singular Equivalences24:24
- List of Non-Singular Equivalences24:25

Coordinates of a Vector

27m 3s

- Intro0:00
- Coordinates of a Vector1:07
- Coordinates of a Vector1:08
- Example 18:35
- Example 215:28
- Example 3: Part A19:15
- Example 3: Part B22:26

Change of Basis & Transition Matrices

33m 47s

- Intro0:00
- Change of Basis & Transition Matrices0:56
- Change of Basis & Transition Matrices0:57
- Example 110:44
- Example 220:44
- Theorem23:37
- Example 3: Part A26:21
- Example 3: Part B32:05

Orthonormal Bases in n-Space

32m 53s

- Intro0:00
- Orthonormal Bases in n-Space1:02
- Orthonormal Bases in n-Space: Definition1:03
- Example 14:31
- Theorem 16:55
- Theorem 28:00
- Theorem 39:04
- Example 210:07
- Theorem 213:54
- Procedure for Constructing an O/N Basis16:11
- Example 321:42

Orthogonal Complements, Part I

21m 27s

- Intro0:00
- Orthogonal Complements0:19
- Definition0:20
- Theorem 15:36
- Example 16:58
- Theorem 213:26
- Theorem 315:06
- Example 218:20

Orthogonal Complements, Part II

33m 49s

- Intro0:00
- Relations Among the Four Fundamental Vector Spaces Associated with a Matrix A2:16
- Four Spaces Associated With A (If A is m x n)2:17
- Theorem4:49
- Example 17:17
- Null Space and Column Space10:48
- Projections and Applications16:50
- Projections and Applications16:51
- Projection Illustration21:00
- Example 123:51
- Projection Illustration Review30:15

V. Eigenvalues and Eigenvectors

Eigenvalues and Eigenvectors

38m 11s

- Intro0:00
- Eigenvalues and Eigenvectors0:38
- Eigenvalues and Eigenvectors0:39
- Definition 13:30
- Example 17:20
- Example 210:19
- Definition 221:15
- Example 323:41
- Theorem 126:32
- Theorem 227:56
- Example 429:14
- Review34:32

Similar Matrices & Diagonalization

29m 55s

- Intro0:00
- Similar Matrices and Diagonalization0:25
- Definition 10:26
- Example 12:00
- Properties3:38
- Definition 24:57
- Theorem 16:12
- Example 39:37
- Theorem 212:40
- Example 419:12
- Example 520:55
- Procedure for Diagonalizing Matrix A: Step 124:21
- Procedure for Diagonalizing Matrix A: Step 225:04
- Procedure for Diagonalizing Matrix A: Step 325:38
- Procedure for Diagonalizing Matrix A: Step 427:02

Diagonalization of Symmetric Matrices

30m 14s

- Intro0:00
- Diagonalization of Symmetric Matrices1:15
- Diagonalization of Symmetric Matrices1:16
- Theorem 12:24
- Theorem 23:27
- Example 14:47
- Definition 16:44
- Example 28:15
- Theorem 310:28
- Theorem 412:31
- Example 318:00

VI. Linear Transformations

Linear Mappings Revisited

24m 5s

- Intro0:00
- Linear Mappings2:08
- Definition2:09
- Linear Operator7:36
- Projection8:48
- Dilation9:40
- Contraction10:07
- Reflection10:26
- Rotation11:06
- Example 113:00
- Theorem 118:16
- Theorem 219:20

Kernel and Range of a Linear Map, Part I

26m 38s

- Intro0:00
- Kernel and Range of a Linear Map0:28
- Definition 10:29
- Example 14:36
- Example 28:12
- Definition 210:34
- Example 313:34
- Theorem 116:01
- Theorem 218:26
- Definition 321:11
- Theorem 324:28

Kernel and Range of a Linear Map, Part II

25m 54s

- Intro0:00
- Kernel and Range of a Linear Map1:39
- Theorem 11:40
- Example 1: Part A2:32
- Example 1: Part B8:12
- Example 1: Part C13:11
- Example 1: Part D14:55
- Theorem 216:50
- Theorem 323:00

Matrix of a Linear Map

33m 21s

- Intro0:00
- Matrix of a Linear Map0:11
- Theorem 11:24
- Procedure for Computing to Matrix: Step 17:10
- Procedure for Computing to Matrix: Step 28:58
- Procedure for Computing to Matrix: Step 39:50
- Matrix of a Linear Map: Property10:41
- Example 114:07
- Example 218:12
- Example 324:31

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For more information, please see full course syllabus of Linear Algebra

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2 answers

Last reply by: John Lins

Tue Jan 31, 2017 2:25 PM

Post by John Lins on January 30, 2017

Sorry! I was trying to write the matrix as {{2,1},{0,2}}

Here is the question:

can't solve this question: Let M = {{2,1},{0,2}}. Find all eigenvalues of M. Does M have two independent eigenvector? Can M be diagonalized?

0 answers

Post by John Lins on January 30, 2017

Please, Help!! I can't solve this question: Let M = (2 1).

0 2

Find all eigenvalues of M. Does M have two independent eigenvector?

Can M be diagonalized?

0 answers

Post by John Lins on January 30, 2017

Please, Help!! I can't solve this question:

Let M = (2 1). Find all eigenvalues of M. Does M have two independent eigenvector?

0 2

Can M be diagonalized?

1 answer

Last reply by: Professor Hovasapian

Sun Jul 6, 2014 7:31 PM

Post by E D on July 6, 2014

Please help!! :'(

At 32:53, i don't understand why x3 = r, when every value in row3 is zero.

i can see why x1 = -x3 and there fore x1 = -r

and x2 = (-3/2)x3 and there fore x2 = (-3/2)r

but x3 looks like it could be: x3 = -x1

or x3 = (-2/3)x2

1 answer

Last reply by: Professor Hovasapian

Tue Jun 3, 2014 7:12 PM

Post by ALI SAAD on June 3, 2014

I have a question, Why do we use (A-rI) at differential equation class but here we use (rI-A)? does it not matter which one comes first?

4 answers

Last reply by: Christian Fischer

Sat Nov 30, 2013 11:16 AM

Post by Christian Fischer on November 24, 2013

Hi Raffi! Great lecture, now i understand it 100%.

Just to make sure: at 21:02 you say that the eigenvector is ((r/2),r)

And then you say the product A*x = (1,1;-1,4)(18,9) is the same as 3*(18,9) but don't you mean 3*(9,18) because i get (1,1;-1,4)(18,9)=(27,72)=3(9,18)

Thank you again and have a great day,

Christian

0 answers

Post by Manfred Berger on June 22, 2013

I may be jumping ahead here but with the zero vector mapping to the zero vector under every linear mapping and 0*any vector being the scalar zero I don't get why you wouldn't exclude both. Can you help me out?

4 answers

Last reply by: Matt C

Sun Apr 28, 2013 11:57 AM

Post by Matt C on April 27, 2013

Hello again. In the book it gives me a 3x3 matrix [[0, -1, 0], [0,0,-1], [a,b,c]]. I wrote the matrix in column form. It then asks me to find a condition on a,b,c such that 1 is an eigenvalue of A. I figured out the question with my first try by just plugging 1 in for each variable (a=1,b=1,c=1). That gives eigenvalues = (1, 1,-1). My question is, is there a method or mathematical way of doing this, instead of just guessing and plugging numbers in? I don't want to see this question on the test and just be plugging in numbers and guessing. Any help or advice would be nice. Thanks.

2 answers

Last reply by: Professor Hovasapian

Fri Aug 10, 2012 10:42 PM

Post by Shahaz Shajahan on August 10, 2012

H, just a quick question

Why is the n x n matrix M with its only eigenvalue is 0 is not invertible?

Just came across this question and its asking for a proof, yet i'm stumped! would really appreciate any help.