Learn on PengiOpenStax Algebra and TrigonometryChapter 11: Systems of Equations and Inequalities

Lesson 11.7: Solving Systems with Inverses

New Concept We'll explore the inverse of a matrix, $A^{ 1}$. Similar to how $a \cdot a^{ 1} = 1$, a matrix and its inverse multiply to equal the identity matrix, $I$. This powerful tool allows us to solve systems of linear equations.

Section 1

πŸ“˜ Solving Systems with Inverses

New Concept

We'll explore the inverse of a matrix, Aβˆ’1A^{-1}. Similar to how aβ‹…aβˆ’1=1a \cdot a^{-1} = 1, a matrix and its inverse multiply to equal the identity matrix, II. This powerful tool allows us to solve systems of linear equations.

What’s next

Now that you have the big picture, you'll master finding inverses and applying them through a series of practice cards and interactive examples.

Section 2

Identity Matrix and Multiplicative Inverse

Property

The identity matrix, InI_n, is a square matrix containing ones down the main diagonal and zeros everywhere else. We identify identity matrices by InI_n where nn represents the dimension of the matrix.

I2=[1001]I3=[100010001]I_2 = \begin{bmatrix} 1 & 0 \\ 0 & 1 \end{bmatrix} \qquad I_3 = \begin{bmatrix} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{bmatrix}

If AA is an nΓ—nn \times n matrix and BB is an nΓ—nn \times n matrix such that AB=BA=InAB = BA = I_n, then B=Aβˆ’1B = A^{-1}, the multiplicative inverse of a matrix AA. A matrix that has a multiplicative inverse has the properties AAβˆ’1=IAA^{-1} = I and Aβˆ’1A=IA^{-1}A = I.

Examples

  • Given matrix A=[52βˆ’13]A = \begin{bmatrix} 5 & 2 \\ -1 & 3 \end{bmatrix}, we can show that AI=IA=AAI = IA = A. AI=[52βˆ’13][1001]=[5(1)+2(0)5(0)+2(1)βˆ’1(1)+3(0)βˆ’1(0)+3(1)]=[52βˆ’13]AI = \begin{bmatrix} 5 & 2 \\ -1 & 3 \end{bmatrix} \begin{bmatrix} 1 & 0 \\ 0 & 1 \end{bmatrix} = \begin{bmatrix} 5(1)+2(0) & 5(0)+2(1) \\ -1(1)+3(0) & -1(0)+3(1) \end{bmatrix} = \begin{bmatrix} 5 & 2 \\ -1 & 3 \end{bmatrix}.
  • To show that A=[2513]A = \begin{bmatrix} 2 & 5 \\ 1 & 3 \end{bmatrix} and B=[3βˆ’5βˆ’12]B = \begin{bmatrix} 3 & -5 \\ -1 & 2 \end{bmatrix} are inverses, we multiply them: AB=[2(3)+5(βˆ’1)2(βˆ’5)+5(2)1(3)+3(βˆ’1)1(βˆ’5)+3(2)]=[1001]=I2AB = \begin{bmatrix} 2(3)+5(-1) & 2(-5)+5(2) \\ 1(3)+3(-1) & 1(-5)+3(2) \end{bmatrix} = \begin{bmatrix} 1 & 0 \\ 0 & 1 \end{bmatrix} = I_2.

Section 3

Inverse of a 2x2 Matrix

Property

If AA is a 2Γ—22 \times 2 matrix, such as

A=[abcd]A = \begin{bmatrix} a & b \\ c & d \end{bmatrix}

the multiplicative inverse of AA is given by the formula

Aβˆ’1=1adβˆ’bc[dβˆ’bβˆ’ca]A^{-1} = \frac{1}{ad - bc} \begin{bmatrix} d & -b \\ -c & a \end{bmatrix}

where adβˆ’bcβ‰ 0ad - bc \neq 0. If adβˆ’bc=0ad - bc = 0, then AA has no inverse.

Examples

  • Find the inverse of A=[5332]A = \begin{bmatrix} 5 & 3 \\ 3 & 2 \end{bmatrix}. The determinant is 5(2)βˆ’3(3)=15(2) - 3(3) = 1. So, Aβˆ’1=11[2βˆ’3βˆ’35]=[2βˆ’3βˆ’35]A^{-1} = \frac{1}{1} \begin{bmatrix} 2 & -3 \\ -3 & 5 \end{bmatrix} = \begin{bmatrix} 2 & -3 \\ -3 & 5 \end{bmatrix}.
  • Find the inverse of A=[4253]A = \begin{bmatrix} 4 & 2 \\ 5 & 3 \end{bmatrix}. The determinant is 4(3)βˆ’2(5)=24(3) - 2(5) = 2. So, Aβˆ’1=12[3βˆ’2βˆ’54]=[32βˆ’1βˆ’522]A^{-1} = \frac{1}{2} \begin{bmatrix} 3 & -2 \\ -5 & 4 \end{bmatrix} = \begin{bmatrix} \frac{3}{2} & -1 \\ -\frac{5}{2} & 2 \end{bmatrix}.

Section 4

Finding an Inverse by Augmenting

Property

To find the multiplicative inverse, augment the matrix with the identity. When matrix AA is transformed into II using row operations, the augmented identity matrix II transforms into Aβˆ’1A^{-1}.

  1. Write the original matrix augmented with the identity matrix on the right: [A∣I][A|I].
  2. Use elementary row operations so that the identity appears on the left.
  3. The matrix on the right is the inverse: [I∣Aβˆ’1][I|A^{-1}]
  4. You can check your answer by showing AAβˆ’1=IAA^{-1} = I.

Examples

  • To find the inverse of A=[3152]A = \begin{bmatrix} 3 & 1 \\ 5 & 2 \end{bmatrix}, we start with [31105201]\left[ \begin{array}{cc|cc} 3 & 1 & 1 & 0 \\ 5 & 2 & 0 & 1 \end{array} \right]. Row operations lead to [102βˆ’101βˆ’53]\left[ \begin{array}{cc|cc} 1 & 0 & 2 & -1 \\ 0 & 1 & -5 & 3 \end{array} \right]. Thus, Aβˆ’1=[2βˆ’1βˆ’53]A^{-1} = \begin{bmatrix} 2 & -1 \\ -5 & 3 \end{bmatrix}.
  • Find the inverse for A=[110011101]A = \begin{bmatrix} 1 & 1 & 0 \\ 0 & 1 & 1 \\ 1 & 0 & 1 \end{bmatrix}. Augmenting and using row operations gives [10012βˆ’12120101212βˆ’12001βˆ’121212]\left[ \begin{array}{ccc|ccc} 1 & 0 & 0 & \frac{1}{2} & -\frac{1}{2} & \frac{1}{2} \\ 0 & 1 & 0 & \frac{1}{2} & \frac{1}{2} & -\frac{1}{2} \\ 0 & 0 & 1 & -\frac{1}{2} & \frac{1}{2} & \frac{1}{2} \end{array} \right]. So Aβˆ’1=12[1βˆ’1111βˆ’1βˆ’111]A^{-1} = \frac{1}{2}\begin{bmatrix} 1 & -1 & 1 \\ 1 & 1 & -1 \\ -1 & 1 & 1 \end{bmatrix}.

Section 5

Solving Systems with an Inverse Matrix

Property

Given a system of equations, write the coefficient matrix AA, the variable matrix XX, and the constant matrix BB. The system can be expressed as:

AX=BAX = B

Multiply both sides by the inverse of AA to obtain the solution.

(Aβˆ’1)AX=(Aβˆ’1)B(A^{-1})AX = (A^{-1})B
IX=(Aβˆ’1)BIX = (A^{-1})B
X=(Aβˆ’1)BX = (A^{-1})B

Examples

  • Solve the system 2x+5y=12x+5y=1 and x+3y=0x+3y=0. Here, A=[2513]A=\begin{bmatrix} 2 & 5 \\ 1 & 3 \end{bmatrix}, X=[xy]X=\begin{bmatrix} x \\ y \end{bmatrix}, and B=[10]B=\begin{bmatrix} 1 \\ 0 \end{bmatrix}. The inverse is Aβˆ’1=[3βˆ’5βˆ’12]A^{-1}=\begin{bmatrix} 3 & -5 \\ -1 & 2 \end{bmatrix}. So, X=Aβˆ’1B=[3βˆ’5βˆ’12][10]=[3βˆ’1]X = A^{-1}B = \begin{bmatrix} 3 & -5 \\ -1 & 2 \end{bmatrix} \begin{bmatrix} 1 \\ 0 \end{bmatrix} = \begin{bmatrix} 3 \\ -1 \end{bmatrix}. The solution is (3,βˆ’1)(3, -1).
  • Solve the system 3x+8y=53x+8y=5 and 4x+11y=74x+11y=7. The matrices are A=[38411]A=\begin{bmatrix} 3 & 8 \\ 4 & 11 \end{bmatrix} and B=[57]B=\begin{bmatrix} 5 \\ 7 \end{bmatrix}. The inverse is Aβˆ’1=[11βˆ’8βˆ’43]A^{-1}=\begin{bmatrix} 11 & -8 \\ -4 & 3 \end{bmatrix}. The solution is X=Aβˆ’1B=[11βˆ’8βˆ’43][57]=[βˆ’11]X = A^{-1}B = \begin{bmatrix} 11 & -8 \\ -4 & 3 \end{bmatrix} \begin{bmatrix} 5 \\ 7 \end{bmatrix} = \begin{bmatrix} -1 \\ 1 \end{bmatrix}. The solution is (βˆ’1,1)(-1, 1).

Book overview

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Chapter 11: Systems of Equations and Inequalities

  1. Lesson 1

    Lesson 11.1: Systems of Linear Equations: Two Variables

  2. Lesson 2

    Lesson 11.2: Systems of Linear Equations: Three Variables

  3. Lesson 3

    Lesson 11.3: Systems of Nonlinear Equations and Inequalities: Two Variables

  4. Lesson 4

    Lesson 11.4: Partial Fractions

  5. Lesson 5

    Lesson 11.5: Matrices and Matrix Operations

  6. Lesson 6

    Lesson 11.6: Solving Systems with Gaussian Elimination

  7. Lesson 7Current

    Lesson 11.7: Solving Systems with Inverses

  8. Lesson 8

    Lesson 11.8: Solving Systems with Cramer's Rule

Lesson overview

Expand to review the lesson summary and core properties.

Expand

Section 1

πŸ“˜ Solving Systems with Inverses

New Concept

We'll explore the inverse of a matrix, Aβˆ’1A^{-1}. Similar to how aβ‹…aβˆ’1=1a \cdot a^{-1} = 1, a matrix and its inverse multiply to equal the identity matrix, II. This powerful tool allows us to solve systems of linear equations.

What’s next

Now that you have the big picture, you'll master finding inverses and applying them through a series of practice cards and interactive examples.

Section 2

Identity Matrix and Multiplicative Inverse

Property

The identity matrix, InI_n, is a square matrix containing ones down the main diagonal and zeros everywhere else. We identify identity matrices by InI_n where nn represents the dimension of the matrix.

I2=[1001]I3=[100010001]I_2 = \begin{bmatrix} 1 & 0 \\ 0 & 1 \end{bmatrix} \qquad I_3 = \begin{bmatrix} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{bmatrix}

If AA is an nΓ—nn \times n matrix and BB is an nΓ—nn \times n matrix such that AB=BA=InAB = BA = I_n, then B=Aβˆ’1B = A^{-1}, the multiplicative inverse of a matrix AA. A matrix that has a multiplicative inverse has the properties AAβˆ’1=IAA^{-1} = I and Aβˆ’1A=IA^{-1}A = I.

Examples

  • Given matrix A=[52βˆ’13]A = \begin{bmatrix} 5 & 2 \\ -1 & 3 \end{bmatrix}, we can show that AI=IA=AAI = IA = A. AI=[52βˆ’13][1001]=[5(1)+2(0)5(0)+2(1)βˆ’1(1)+3(0)βˆ’1(0)+3(1)]=[52βˆ’13]AI = \begin{bmatrix} 5 & 2 \\ -1 & 3 \end{bmatrix} \begin{bmatrix} 1 & 0 \\ 0 & 1 \end{bmatrix} = \begin{bmatrix} 5(1)+2(0) & 5(0)+2(1) \\ -1(1)+3(0) & -1(0)+3(1) \end{bmatrix} = \begin{bmatrix} 5 & 2 \\ -1 & 3 \end{bmatrix}.
  • To show that A=[2513]A = \begin{bmatrix} 2 & 5 \\ 1 & 3 \end{bmatrix} and B=[3βˆ’5βˆ’12]B = \begin{bmatrix} 3 & -5 \\ -1 & 2 \end{bmatrix} are inverses, we multiply them: AB=[2(3)+5(βˆ’1)2(βˆ’5)+5(2)1(3)+3(βˆ’1)1(βˆ’5)+3(2)]=[1001]=I2AB = \begin{bmatrix} 2(3)+5(-1) & 2(-5)+5(2) \\ 1(3)+3(-1) & 1(-5)+3(2) \end{bmatrix} = \begin{bmatrix} 1 & 0 \\ 0 & 1 \end{bmatrix} = I_2.

Section 3

Inverse of a 2x2 Matrix

Property

If AA is a 2Γ—22 \times 2 matrix, such as

A=[abcd]A = \begin{bmatrix} a & b \\ c & d \end{bmatrix}

the multiplicative inverse of AA is given by the formula

Aβˆ’1=1adβˆ’bc[dβˆ’bβˆ’ca]A^{-1} = \frac{1}{ad - bc} \begin{bmatrix} d & -b \\ -c & a \end{bmatrix}

where adβˆ’bcβ‰ 0ad - bc \neq 0. If adβˆ’bc=0ad - bc = 0, then AA has no inverse.

Examples

  • Find the inverse of A=[5332]A = \begin{bmatrix} 5 & 3 \\ 3 & 2 \end{bmatrix}. The determinant is 5(2)βˆ’3(3)=15(2) - 3(3) = 1. So, Aβˆ’1=11[2βˆ’3βˆ’35]=[2βˆ’3βˆ’35]A^{-1} = \frac{1}{1} \begin{bmatrix} 2 & -3 \\ -3 & 5 \end{bmatrix} = \begin{bmatrix} 2 & -3 \\ -3 & 5 \end{bmatrix}.
  • Find the inverse of A=[4253]A = \begin{bmatrix} 4 & 2 \\ 5 & 3 \end{bmatrix}. The determinant is 4(3)βˆ’2(5)=24(3) - 2(5) = 2. So, Aβˆ’1=12[3βˆ’2βˆ’54]=[32βˆ’1βˆ’522]A^{-1} = \frac{1}{2} \begin{bmatrix} 3 & -2 \\ -5 & 4 \end{bmatrix} = \begin{bmatrix} \frac{3}{2} & -1 \\ -\frac{5}{2} & 2 \end{bmatrix}.

Section 4

Finding an Inverse by Augmenting

Property

To find the multiplicative inverse, augment the matrix with the identity. When matrix AA is transformed into II using row operations, the augmented identity matrix II transforms into Aβˆ’1A^{-1}.

  1. Write the original matrix augmented with the identity matrix on the right: [A∣I][A|I].
  2. Use elementary row operations so that the identity appears on the left.
  3. The matrix on the right is the inverse: [I∣Aβˆ’1][I|A^{-1}]
  4. You can check your answer by showing AAβˆ’1=IAA^{-1} = I.

Examples

  • To find the inverse of A=[3152]A = \begin{bmatrix} 3 & 1 \\ 5 & 2 \end{bmatrix}, we start with [31105201]\left[ \begin{array}{cc|cc} 3 & 1 & 1 & 0 \\ 5 & 2 & 0 & 1 \end{array} \right]. Row operations lead to [102βˆ’101βˆ’53]\left[ \begin{array}{cc|cc} 1 & 0 & 2 & -1 \\ 0 & 1 & -5 & 3 \end{array} \right]. Thus, Aβˆ’1=[2βˆ’1βˆ’53]A^{-1} = \begin{bmatrix} 2 & -1 \\ -5 & 3 \end{bmatrix}.
  • Find the inverse for A=[110011101]A = \begin{bmatrix} 1 & 1 & 0 \\ 0 & 1 & 1 \\ 1 & 0 & 1 \end{bmatrix}. Augmenting and using row operations gives [10012βˆ’12120101212βˆ’12001βˆ’121212]\left[ \begin{array}{ccc|ccc} 1 & 0 & 0 & \frac{1}{2} & -\frac{1}{2} & \frac{1}{2} \\ 0 & 1 & 0 & \frac{1}{2} & \frac{1}{2} & -\frac{1}{2} \\ 0 & 0 & 1 & -\frac{1}{2} & \frac{1}{2} & \frac{1}{2} \end{array} \right]. So Aβˆ’1=12[1βˆ’1111βˆ’1βˆ’111]A^{-1} = \frac{1}{2}\begin{bmatrix} 1 & -1 & 1 \\ 1 & 1 & -1 \\ -1 & 1 & 1 \end{bmatrix}.

Section 5

Solving Systems with an Inverse Matrix

Property

Given a system of equations, write the coefficient matrix AA, the variable matrix XX, and the constant matrix BB. The system can be expressed as:

AX=BAX = B

Multiply both sides by the inverse of AA to obtain the solution.

(Aβˆ’1)AX=(Aβˆ’1)B(A^{-1})AX = (A^{-1})B
IX=(Aβˆ’1)BIX = (A^{-1})B
X=(Aβˆ’1)BX = (A^{-1})B

Examples

  • Solve the system 2x+5y=12x+5y=1 and x+3y=0x+3y=0. Here, A=[2513]A=\begin{bmatrix} 2 & 5 \\ 1 & 3 \end{bmatrix}, X=[xy]X=\begin{bmatrix} x \\ y \end{bmatrix}, and B=[10]B=\begin{bmatrix} 1 \\ 0 \end{bmatrix}. The inverse is Aβˆ’1=[3βˆ’5βˆ’12]A^{-1}=\begin{bmatrix} 3 & -5 \\ -1 & 2 \end{bmatrix}. So, X=Aβˆ’1B=[3βˆ’5βˆ’12][10]=[3βˆ’1]X = A^{-1}B = \begin{bmatrix} 3 & -5 \\ -1 & 2 \end{bmatrix} \begin{bmatrix} 1 \\ 0 \end{bmatrix} = \begin{bmatrix} 3 \\ -1 \end{bmatrix}. The solution is (3,βˆ’1)(3, -1).
  • Solve the system 3x+8y=53x+8y=5 and 4x+11y=74x+11y=7. The matrices are A=[38411]A=\begin{bmatrix} 3 & 8 \\ 4 & 11 \end{bmatrix} and B=[57]B=\begin{bmatrix} 5 \\ 7 \end{bmatrix}. The inverse is Aβˆ’1=[11βˆ’8βˆ’43]A^{-1}=\begin{bmatrix} 11 & -8 \\ -4 & 3 \end{bmatrix}. The solution is X=Aβˆ’1B=[11βˆ’8βˆ’43][57]=[βˆ’11]X = A^{-1}B = \begin{bmatrix} 11 & -8 \\ -4 & 3 \end{bmatrix} \begin{bmatrix} 5 \\ 7 \end{bmatrix} = \begin{bmatrix} -1 \\ 1 \end{bmatrix}. The solution is (βˆ’1,1)(-1, 1).

Book overview

Jump across lessons in the current chapter without opening the full course modal.

Continue this chapter

Chapter 11: Systems of Equations and Inequalities

  1. Lesson 1

    Lesson 11.1: Systems of Linear Equations: Two Variables

  2. Lesson 2

    Lesson 11.2: Systems of Linear Equations: Three Variables

  3. Lesson 3

    Lesson 11.3: Systems of Nonlinear Equations and Inequalities: Two Variables

  4. Lesson 4

    Lesson 11.4: Partial Fractions

  5. Lesson 5

    Lesson 11.5: Matrices and Matrix Operations

  6. Lesson 6

    Lesson 11.6: Solving Systems with Gaussian Elimination

  7. Lesson 7Current

    Lesson 11.7: Solving Systems with Inverses

  8. Lesson 8

    Lesson 11.8: Solving Systems with Cramer's Rule