SlideShare a Scribd company logo
2.2 Inverse of a Matrix
Definition [Invertible Matrix]
An nxn matrix A is said to be invertible if there is an nxn matrix B such
that
AB=BA=I
in which case B is an inverse of A and we write B=A-1
Definition [Singular and Non Singular
Matrices]
A matrix that is not invertible is called a singular matrix. A matrix that is
invertible is called non singular matrix.
Example: The matrix B=
−2 1
1.5 −0.5
is an inverse
of A =
1 2
3 4
.
Solution:
Remark:
Statement: If a square matrix has an inverse then it is unique.
proof: Let A be an invertible matrix. If B be the inverse of the matrix A then
by definition,
AB = BA = I …………(i)
Suppose if possible, let C be another inverse of A, then again by definition,
AC = CA = I………….(ii)
Now, C = CI = CAB from (i)
= IB from (ii)
= B, thus B is unique.
Theorem 4
Let A=
𝑎 𝑏
𝑐 𝑑
, ad − bc ≠ 0, then A is invertible and
𝐴−1 =
1
𝑎𝑑−𝑏𝑐
𝑑 −𝑏
−𝑐 𝑎
. If ad-bc=0, then A is invertible.
Proof:
Definition [Determinant]
Let A=
𝑎 𝑏
𝑐 𝑑
. The quantity ad-bc is called the determinant of A. It is
denoted by detA or 𝐴 or ∆𝐴.
Thus, 𝐴 =ad-bc
Note:
1. Theorem 4 guarantees that a 2x2 matrix A is invertible if and only if
detA≠ 0.
2. A matrix A is singular if detA=0 and it is non singular if detA ≠ 0.
Example: Find the inverse of A=
2 4
3 5
.
Solution:
Theorem 5
If A is an invertible nxn matrix, then for each b in Rn, the equation Ax=b
has the unique solution x=A-1b.
Proof:
Example: Use the inverse of the matrix to
solve the system, 2x + 4y = 6 , 3x + 5y = 8.
Solution:
Theorem 6
a. If A is an invertible matrix, then A-1 is invertible and (A-1)-1=A.
b. If A and B are nxn invertible matrices, then so is AB and the inverse
of AB is the product of the inverses of A and B the reverse order.
That is (AB)-1=B-1A-1.
c. If A is an invertible matrix, then so is AT, and the inverse of AT is the
transpose of A-1. That is (AT)-1=(A-1)T.
Proof:
Elementary Matrices
A matrix that is obtained by performing a single elementary row
operation in an identity matrix is called an elementary matrix.
e.g. 𝐸1 =
1 0 0
0 1 0
−4 0 1
, is an elementary matrix which is obtained by
performing the operation R3→R3 – 4R1
Algorithm for finding A-1
Let A be and invertible matrix.
1. Row reduce the augmented matrix [A | I].
2. If A is row equivalent to I, then [A | I] is row equivalent to [I | A-1]
i.e. [A | I] ~ … ~ [I | A-1].
3. Otherwise , A does not have an inverse.
Example

More Related Content

What's hot

Matrix basic operations
Matrix basic operationsMatrix basic operations
Matrix basic operations
Jessica Garcia
 
Matrices and System of Linear Equations ppt
Matrices and System of Linear Equations pptMatrices and System of Linear Equations ppt
Matrices and System of Linear Equations ppt
Drazzer_Dhruv
 
Introduction of matrices
Introduction of matricesIntroduction of matrices
Introduction of matrices
Shakehand with Life
 
Matrix and its operations
Matrix and its operationsMatrix and its operations
Matrix and its operations
Pankaj Das
 
Lesson 4: Lines and Planes (slides + notes)
Lesson 4: Lines and Planes (slides + notes)Lesson 4: Lines and Planes (slides + notes)
Lesson 4: Lines and Planes (slides + notes)
Matthew Leingang
 
Matrix and Determinants
Matrix and DeterminantsMatrix and Determinants
Matrix and Determinants
AarjavPinara
 
Matrix multiplication, inverse
Matrix multiplication, inverseMatrix multiplication, inverse
Matrix multiplication, inverse
Prasanth George
 
Matrix Algebra seminar ppt
Matrix Algebra seminar pptMatrix Algebra seminar ppt
Matrix Algebra seminar ppt
Swetalina Pradhan
 
Matrices
MatricesMatrices
Matrices
ashishtqm
 
Matrix presentation By DHEERAJ KATARIA
Matrix presentation By DHEERAJ KATARIAMatrix presentation By DHEERAJ KATARIA
Matrix presentation By DHEERAJ KATARIA
Dheeraj Kataria
 
Matrix introduction and matrix operations.
Matrix introduction and matrix operations. Matrix introduction and matrix operations.
Matrix introduction and matrix operations.
Learnbay Datascience
 
Matrices ppt
Matrices pptMatrices ppt
Matrices ppt
aakashray33
 
Lesson 5: Matrix Algebra (slides)
Lesson 5: Matrix Algebra (slides)Lesson 5: Matrix Algebra (slides)
Lesson 5: Matrix Algebra (slides)
Matthew Leingang
 
Matrix.
Matrix.Matrix.
Matrix.
Awais Bakshy
 
Matrices and Determinants
Matrices and DeterminantsMatrices and Determinants
Matrices and Determinants
SOMASUNDARAM T
 
Inner product spaces
Inner product spacesInner product spaces
Inner product spaces
EasyStudy3
 
matrix algebra
matrix algebramatrix algebra
matrix algebra
kganu
 
Poset in Relations(Discrete Mathematics)
Poset in Relations(Discrete Mathematics)Poset in Relations(Discrete Mathematics)
Poset in Relations(Discrete Mathematics)
Rachana Pathak
 
Binomial Theorem
Binomial TheoremBinomial Theorem
Binomial Theorem
itutor
 
MATRICES AND ITS TYPE
MATRICES AND ITS TYPEMATRICES AND ITS TYPE
MATRICES AND ITS TYPE
Himanshu Negi
 

What's hot (20)

Matrix basic operations
Matrix basic operationsMatrix basic operations
Matrix basic operations
 
Matrices and System of Linear Equations ppt
Matrices and System of Linear Equations pptMatrices and System of Linear Equations ppt
Matrices and System of Linear Equations ppt
 
Introduction of matrices
Introduction of matricesIntroduction of matrices
Introduction of matrices
 
Matrix and its operations
Matrix and its operationsMatrix and its operations
Matrix and its operations
 
Lesson 4: Lines and Planes (slides + notes)
Lesson 4: Lines and Planes (slides + notes)Lesson 4: Lines and Planes (slides + notes)
Lesson 4: Lines and Planes (slides + notes)
 
Matrix and Determinants
Matrix and DeterminantsMatrix and Determinants
Matrix and Determinants
 
Matrix multiplication, inverse
Matrix multiplication, inverseMatrix multiplication, inverse
Matrix multiplication, inverse
 
Matrix Algebra seminar ppt
Matrix Algebra seminar pptMatrix Algebra seminar ppt
Matrix Algebra seminar ppt
 
Matrices
MatricesMatrices
Matrices
 
Matrix presentation By DHEERAJ KATARIA
Matrix presentation By DHEERAJ KATARIAMatrix presentation By DHEERAJ KATARIA
Matrix presentation By DHEERAJ KATARIA
 
Matrix introduction and matrix operations.
Matrix introduction and matrix operations. Matrix introduction and matrix operations.
Matrix introduction and matrix operations.
 
Matrices ppt
Matrices pptMatrices ppt
Matrices ppt
 
Lesson 5: Matrix Algebra (slides)
Lesson 5: Matrix Algebra (slides)Lesson 5: Matrix Algebra (slides)
Lesson 5: Matrix Algebra (slides)
 
Matrix.
Matrix.Matrix.
Matrix.
 
Matrices and Determinants
Matrices and DeterminantsMatrices and Determinants
Matrices and Determinants
 
Inner product spaces
Inner product spacesInner product spaces
Inner product spaces
 
matrix algebra
matrix algebramatrix algebra
matrix algebra
 
Poset in Relations(Discrete Mathematics)
Poset in Relations(Discrete Mathematics)Poset in Relations(Discrete Mathematics)
Poset in Relations(Discrete Mathematics)
 
Binomial Theorem
Binomial TheoremBinomial Theorem
Binomial Theorem
 
MATRICES AND ITS TYPE
MATRICES AND ITS TYPEMATRICES AND ITS TYPE
MATRICES AND ITS TYPE
 

Similar to 2.2 inverse of a matrix

Invertible Matrix and Factorization.pptx
Invertible Matrix and Factorization.pptxInvertible Matrix and Factorization.pptx
Invertible Matrix and Factorization.pptx
IkhlaqAhmad18
 
Matrices & determinants
Matrices & determinantsMatrices & determinants
Matrices & determinants
indu thakur
 
Some notes on Matrix Algebra
Some notes on Matrix AlgebraSome notes on Matrix Algebra
Some notes on Matrix Algebra
Shailesh Kumar
 
Big Thm2
Big Thm2Big Thm2
Big Thm2
guest192ade
 
Maths-->>Eigenvalues and eigenvectors
Maths-->>Eigenvalues and eigenvectorsMaths-->>Eigenvalues and eigenvectors
Maths-->>Eigenvalues and eigenvectors
Jaydev Kishnani
 
Bba i-bm-u-2- matrix -
Bba i-bm-u-2- matrix -Bba i-bm-u-2- matrix -
Bba i-bm-u-2- matrix -
Rai University
 
Lecture 5 inverse of matrices - section 2-2 and 2-3
Lecture 5   inverse of matrices - section 2-2 and 2-3Lecture 5   inverse of matrices - section 2-2 and 2-3
Lecture 5 inverse of matrices - section 2-2 and 2-3
njit-ronbrown
 
Eigen value and vectors
Eigen value and vectorsEigen value and vectors
Eigen value and vectors
Praveen Prashant
 
chap01987654etghujh76687976jgtfhhhgve.ppt
chap01987654etghujh76687976jgtfhhhgve.pptchap01987654etghujh76687976jgtfhhhgve.ppt
chap01987654etghujh76687976jgtfhhhgve.ppt
adonyasdd
 
CMSC 56 | Lecture 17: Matrices
CMSC 56 | Lecture 17: MatricesCMSC 56 | Lecture 17: Matrices
CMSC 56 | Lecture 17: Matrices
allyn joy calcaben
 
Eighan values and diagonalization
Eighan values and diagonalization Eighan values and diagonalization
Eighan values and diagonalization
gandhinagar
 
Inverse-power-method.pdf
Inverse-power-method.pdfInverse-power-method.pdf
Inverse-power-method.pdf
Isaac Yowetu
 
Ch07 3
Ch07 3Ch07 3
Ch07 3
Rendy Robert
 
Eigen value and eigen vector
Eigen value and eigen vectorEigen value and eigen vector
Eigen value and eigen vector
Rutvij Patel
 
Linear Algebra and Matrix
Linear Algebra and MatrixLinear Algebra and Matrix
Linear Algebra and Matrix
itutor
 
7 4
7 47 4
7 4
ELIMENG
 
Matrices y determinants
Matrices y determinantsMatrices y determinants
Matrices y determinants
Jeannie
 
Engg maths k notes(4)
Engg maths k notes(4)Engg maths k notes(4)
Engg maths k notes(4)
Ranjay Kumar
 
On Fully Indecomposable Quaternion Doubly Stochastic Matrices
On Fully Indecomposable Quaternion Doubly Stochastic MatricesOn Fully Indecomposable Quaternion Doubly Stochastic Matrices
On Fully Indecomposable Quaternion Doubly Stochastic Matrices
ijtsrd
 
Eigen value , eigen vectors, caley hamilton theorem
Eigen value , eigen vectors, caley hamilton theoremEigen value , eigen vectors, caley hamilton theorem
Eigen value , eigen vectors, caley hamilton theorem
gidc engineering college
 

Similar to 2.2 inverse of a matrix (20)

Invertible Matrix and Factorization.pptx
Invertible Matrix and Factorization.pptxInvertible Matrix and Factorization.pptx
Invertible Matrix and Factorization.pptx
 
Matrices & determinants
Matrices & determinantsMatrices & determinants
Matrices & determinants
 
Some notes on Matrix Algebra
Some notes on Matrix AlgebraSome notes on Matrix Algebra
Some notes on Matrix Algebra
 
Big Thm2
Big Thm2Big Thm2
Big Thm2
 
Maths-->>Eigenvalues and eigenvectors
Maths-->>Eigenvalues and eigenvectorsMaths-->>Eigenvalues and eigenvectors
Maths-->>Eigenvalues and eigenvectors
 
Bba i-bm-u-2- matrix -
Bba i-bm-u-2- matrix -Bba i-bm-u-2- matrix -
Bba i-bm-u-2- matrix -
 
Lecture 5 inverse of matrices - section 2-2 and 2-3
Lecture 5   inverse of matrices - section 2-2 and 2-3Lecture 5   inverse of matrices - section 2-2 and 2-3
Lecture 5 inverse of matrices - section 2-2 and 2-3
 
Eigen value and vectors
Eigen value and vectorsEigen value and vectors
Eigen value and vectors
 
chap01987654etghujh76687976jgtfhhhgve.ppt
chap01987654etghujh76687976jgtfhhhgve.pptchap01987654etghujh76687976jgtfhhhgve.ppt
chap01987654etghujh76687976jgtfhhhgve.ppt
 
CMSC 56 | Lecture 17: Matrices
CMSC 56 | Lecture 17: MatricesCMSC 56 | Lecture 17: Matrices
CMSC 56 | Lecture 17: Matrices
 
Eighan values and diagonalization
Eighan values and diagonalization Eighan values and diagonalization
Eighan values and diagonalization
 
Inverse-power-method.pdf
Inverse-power-method.pdfInverse-power-method.pdf
Inverse-power-method.pdf
 
Ch07 3
Ch07 3Ch07 3
Ch07 3
 
Eigen value and eigen vector
Eigen value and eigen vectorEigen value and eigen vector
Eigen value and eigen vector
 
Linear Algebra and Matrix
Linear Algebra and MatrixLinear Algebra and Matrix
Linear Algebra and Matrix
 
7 4
7 47 4
7 4
 
Matrices y determinants
Matrices y determinantsMatrices y determinants
Matrices y determinants
 
Engg maths k notes(4)
Engg maths k notes(4)Engg maths k notes(4)
Engg maths k notes(4)
 
On Fully Indecomposable Quaternion Doubly Stochastic Matrices
On Fully Indecomposable Quaternion Doubly Stochastic MatricesOn Fully Indecomposable Quaternion Doubly Stochastic Matrices
On Fully Indecomposable Quaternion Doubly Stochastic Matrices
 
Eigen value , eigen vectors, caley hamilton theorem
Eigen value , eigen vectors, caley hamilton theoremEigen value , eigen vectors, caley hamilton theorem
Eigen value , eigen vectors, caley hamilton theorem
 

More from Self-Employed

Queue AS an ADT (Abstract Data Type)
Queue AS an ADT (Abstract Data Type)Queue AS an ADT (Abstract Data Type)
Queue AS an ADT (Abstract Data Type)
Self-Employed
 
Infix prefix postfix
Infix prefix postfixInfix prefix postfix
Infix prefix postfix
Self-Employed
 
Ds lec 5_chap4
Ds lec 5_chap4Ds lec 5_chap4
Ds lec 5_chap4
Self-Employed
 
Discrete mathematics counting and logic relation
Discrete mathematics counting and logic relationDiscrete mathematics counting and logic relation
Discrete mathematics counting and logic relation
Self-Employed
 
Algorithm and C code related to data structure
Algorithm and C code related to data structureAlgorithm and C code related to data structure
Algorithm and C code related to data structure
Self-Employed
 
Abstract data types (adt) intro to data structure part 2
Abstract data types (adt)   intro to data structure part 2Abstract data types (adt)   intro to data structure part 2
Abstract data types (adt) intro to data structure part 2
Self-Employed
 
8086 architecture
8086 architecture8086 architecture
8086 architecture
Self-Employed
 

More from Self-Employed (7)

Queue AS an ADT (Abstract Data Type)
Queue AS an ADT (Abstract Data Type)Queue AS an ADT (Abstract Data Type)
Queue AS an ADT (Abstract Data Type)
 
Infix prefix postfix
Infix prefix postfixInfix prefix postfix
Infix prefix postfix
 
Ds lec 5_chap4
Ds lec 5_chap4Ds lec 5_chap4
Ds lec 5_chap4
 
Discrete mathematics counting and logic relation
Discrete mathematics counting and logic relationDiscrete mathematics counting and logic relation
Discrete mathematics counting and logic relation
 
Algorithm and C code related to data structure
Algorithm and C code related to data structureAlgorithm and C code related to data structure
Algorithm and C code related to data structure
 
Abstract data types (adt) intro to data structure part 2
Abstract data types (adt)   intro to data structure part 2Abstract data types (adt)   intro to data structure part 2
Abstract data types (adt) intro to data structure part 2
 
8086 architecture
8086 architecture8086 architecture
8086 architecture
 

Recently uploaded

International Conference on NLP, Artificial Intelligence, Machine Learning an...
International Conference on NLP, Artificial Intelligence, Machine Learning an...International Conference on NLP, Artificial Intelligence, Machine Learning an...
International Conference on NLP, Artificial Intelligence, Machine Learning an...
gerogepatton
 
Introduction to AI Safety (public presentation).pptx
Introduction to AI Safety (public presentation).pptxIntroduction to AI Safety (public presentation).pptx
Introduction to AI Safety (public presentation).pptx
MiscAnnoy1
 
132/33KV substation case study Presentation
132/33KV substation case study Presentation132/33KV substation case study Presentation
132/33KV substation case study Presentation
kandramariana6
 
Hematology Analyzer Machine - Complete Blood Count
Hematology Analyzer Machine - Complete Blood CountHematology Analyzer Machine - Complete Blood Count
Hematology Analyzer Machine - Complete Blood Count
shahdabdulbaset
 
ACEP Magazine edition 4th launched on 05.06.2024
ACEP Magazine edition 4th launched on 05.06.2024ACEP Magazine edition 4th launched on 05.06.2024
ACEP Magazine edition 4th launched on 05.06.2024
Rahul
 
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
IJECEIAES
 
Transformers design and coooling methods
Transformers design and coooling methodsTransformers design and coooling methods
Transformers design and coooling methods
Roger Rozario
 
学校原版美国波士顿大学毕业证学历学位证书原版一模一样
学校原版美国波士顿大学毕业证学历学位证书原版一模一样学校原版美国波士顿大学毕业证学历学位证书原版一模一样
学校原版美国波士顿大学毕业证学历学位证书原版一模一样
171ticu
 
spirit beverages ppt without graphics.pptx
spirit beverages ppt without graphics.pptxspirit beverages ppt without graphics.pptx
spirit beverages ppt without graphics.pptx
Madan Karki
 
Comparative analysis between traditional aquaponics and reconstructed aquapon...
Comparative analysis between traditional aquaponics and reconstructed aquapon...Comparative analysis between traditional aquaponics and reconstructed aquapon...
Comparative analysis between traditional aquaponics and reconstructed aquapon...
bijceesjournal
 
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdfBPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
MIGUELANGEL966976
 
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
IJECEIAES
 
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTCHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT
jpsjournal1
 
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
Yasser Mahgoub
 
Casting-Defect-inSlab continuous casting.pdf
Casting-Defect-inSlab continuous casting.pdfCasting-Defect-inSlab continuous casting.pdf
Casting-Defect-inSlab continuous casting.pdf
zubairahmad848137
 
ML Based Model for NIDS MSc Updated Presentation.v2.pptx
ML Based Model for NIDS MSc Updated Presentation.v2.pptxML Based Model for NIDS MSc Updated Presentation.v2.pptx
ML Based Model for NIDS MSc Updated Presentation.v2.pptx
JamalHussainArman
 
john krisinger-the science and history of the alcoholic beverage.pptx
john krisinger-the science and history of the alcoholic beverage.pptxjohn krisinger-the science and history of the alcoholic beverage.pptx
john krisinger-the science and history of the alcoholic beverage.pptx
Madan Karki
 
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by AnantLLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
Anant Corporation
 
一比一原版(CalArts毕业证)加利福尼亚艺术学院毕业证如何办理
一比一原版(CalArts毕业证)加利福尼亚艺术学院毕业证如何办理一比一原版(CalArts毕业证)加利福尼亚艺术学院毕业证如何办理
一比一原版(CalArts毕业证)加利福尼亚艺术学院毕业证如何办理
ecqow
 
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressions
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsKuberTENes Birthday Bash Guadalajara - K8sGPT first impressions
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressions
Victor Morales
 

Recently uploaded (20)

International Conference on NLP, Artificial Intelligence, Machine Learning an...
International Conference on NLP, Artificial Intelligence, Machine Learning an...International Conference on NLP, Artificial Intelligence, Machine Learning an...
International Conference on NLP, Artificial Intelligence, Machine Learning an...
 
Introduction to AI Safety (public presentation).pptx
Introduction to AI Safety (public presentation).pptxIntroduction to AI Safety (public presentation).pptx
Introduction to AI Safety (public presentation).pptx
 
132/33KV substation case study Presentation
132/33KV substation case study Presentation132/33KV substation case study Presentation
132/33KV substation case study Presentation
 
Hematology Analyzer Machine - Complete Blood Count
Hematology Analyzer Machine - Complete Blood CountHematology Analyzer Machine - Complete Blood Count
Hematology Analyzer Machine - Complete Blood Count
 
ACEP Magazine edition 4th launched on 05.06.2024
ACEP Magazine edition 4th launched on 05.06.2024ACEP Magazine edition 4th launched on 05.06.2024
ACEP Magazine edition 4th launched on 05.06.2024
 
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
 
Transformers design and coooling methods
Transformers design and coooling methodsTransformers design and coooling methods
Transformers design and coooling methods
 
学校原版美国波士顿大学毕业证学历学位证书原版一模一样
学校原版美国波士顿大学毕业证学历学位证书原版一模一样学校原版美国波士顿大学毕业证学历学位证书原版一模一样
学校原版美国波士顿大学毕业证学历学位证书原版一模一样
 
spirit beverages ppt without graphics.pptx
spirit beverages ppt without graphics.pptxspirit beverages ppt without graphics.pptx
spirit beverages ppt without graphics.pptx
 
Comparative analysis between traditional aquaponics and reconstructed aquapon...
Comparative analysis between traditional aquaponics and reconstructed aquapon...Comparative analysis between traditional aquaponics and reconstructed aquapon...
Comparative analysis between traditional aquaponics and reconstructed aquapon...
 
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdfBPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
BPV-GUI-01-Guide-for-ASME-Review-Teams-(General)-10-10-2023.pdf
 
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
 
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTCHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT
 
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
 
Casting-Defect-inSlab continuous casting.pdf
Casting-Defect-inSlab continuous casting.pdfCasting-Defect-inSlab continuous casting.pdf
Casting-Defect-inSlab continuous casting.pdf
 
ML Based Model for NIDS MSc Updated Presentation.v2.pptx
ML Based Model for NIDS MSc Updated Presentation.v2.pptxML Based Model for NIDS MSc Updated Presentation.v2.pptx
ML Based Model for NIDS MSc Updated Presentation.v2.pptx
 
john krisinger-the science and history of the alcoholic beverage.pptx
john krisinger-the science and history of the alcoholic beverage.pptxjohn krisinger-the science and history of the alcoholic beverage.pptx
john krisinger-the science and history of the alcoholic beverage.pptx
 
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by AnantLLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
 
一比一原版(CalArts毕业证)加利福尼亚艺术学院毕业证如何办理
一比一原版(CalArts毕业证)加利福尼亚艺术学院毕业证如何办理一比一原版(CalArts毕业证)加利福尼亚艺术学院毕业证如何办理
一比一原版(CalArts毕业证)加利福尼亚艺术学院毕业证如何办理
 
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressions
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsKuberTENes Birthday Bash Guadalajara - K8sGPT first impressions
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressions
 

2.2 inverse of a matrix

  • 1. 2.2 Inverse of a Matrix
  • 2. Definition [Invertible Matrix] An nxn matrix A is said to be invertible if there is an nxn matrix B such that AB=BA=I in which case B is an inverse of A and we write B=A-1
  • 3. Definition [Singular and Non Singular Matrices] A matrix that is not invertible is called a singular matrix. A matrix that is invertible is called non singular matrix.
  • 4. Example: The matrix B= −2 1 1.5 −0.5 is an inverse of A = 1 2 3 4 . Solution:
  • 5. Remark: Statement: If a square matrix has an inverse then it is unique. proof: Let A be an invertible matrix. If B be the inverse of the matrix A then by definition, AB = BA = I …………(i) Suppose if possible, let C be another inverse of A, then again by definition, AC = CA = I………….(ii) Now, C = CI = CAB from (i) = IB from (ii) = B, thus B is unique.
  • 6. Theorem 4 Let A= 𝑎 𝑏 𝑐 𝑑 , ad − bc ≠ 0, then A is invertible and 𝐴−1 = 1 𝑎𝑑−𝑏𝑐 𝑑 −𝑏 −𝑐 𝑎 . If ad-bc=0, then A is invertible. Proof:
  • 7.
  • 8. Definition [Determinant] Let A= 𝑎 𝑏 𝑐 𝑑 . The quantity ad-bc is called the determinant of A. It is denoted by detA or 𝐴 or ∆𝐴. Thus, 𝐴 =ad-bc
  • 9. Note: 1. Theorem 4 guarantees that a 2x2 matrix A is invertible if and only if detA≠ 0. 2. A matrix A is singular if detA=0 and it is non singular if detA ≠ 0.
  • 10. Example: Find the inverse of A= 2 4 3 5 . Solution:
  • 11. Theorem 5 If A is an invertible nxn matrix, then for each b in Rn, the equation Ax=b has the unique solution x=A-1b. Proof:
  • 12. Example: Use the inverse of the matrix to solve the system, 2x + 4y = 6 , 3x + 5y = 8. Solution:
  • 13. Theorem 6 a. If A is an invertible matrix, then A-1 is invertible and (A-1)-1=A. b. If A and B are nxn invertible matrices, then so is AB and the inverse of AB is the product of the inverses of A and B the reverse order. That is (AB)-1=B-1A-1. c. If A is an invertible matrix, then so is AT, and the inverse of AT is the transpose of A-1. That is (AT)-1=(A-1)T. Proof:
  • 14.
  • 15. Elementary Matrices A matrix that is obtained by performing a single elementary row operation in an identity matrix is called an elementary matrix. e.g. 𝐸1 = 1 0 0 0 1 0 −4 0 1 , is an elementary matrix which is obtained by performing the operation R3→R3 – 4R1
  • 16. Algorithm for finding A-1 Let A be and invertible matrix. 1. Row reduce the augmented matrix [A | I]. 2. If A is row equivalent to I, then [A | I] is row equivalent to [I | A-1] i.e. [A | I] ~ … ~ [I | A-1]. 3. Otherwise , A does not have an inverse.