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Chapter 4 · Determinants

Exercise 4.4 · Q5: Inverse of a 2×22\times2 matrix

Exercise 4.4Inverse of a matrix · Theorem 4, Section 4.5
The question

Find the inverse of each of the matrices (if it exists) given in Exercises 5 to 11.

5.

A=[2−243]A = \begin{bmatrix} 2 & -2 \\ 4 & 3 \end{bmatrix}
Exam answer ∣A∣=∣2−243∣=6+8=14|A| = \begin{vmatrix} 2 & -2 \\ 4 & 3 \end{vmatrix} = 6 + 8 = 14

Since ∣A∣≠0|A| \ne 0⁠, A−1A^{-1} exists.

Cofactors: A11=3A_{11} = 3⁠, A12=−4A_{12} = -4⁠, A21=2A_{21} = 2⁠, A22=2A_{22} = 2⁠.

adj⁡A=[32−42]\operatorname{adj}A = \begin{bmatrix} 3 & 2 \\ -4 & 2 \end{bmatrix} A−1=1∣A∣adj⁡AA^{-1} = \frac{1}{|A|}\operatorname{adj}A

∴ A−1=114[32−42]\therefore\ A^{-1} = \dfrac{1}{14}\begin{bmatrix} 3 & 2 \\ -4 & 2 \end{bmatrix}

Want the reason behind every step, the basics and the checks?

The one idea this problem is built on

A square matrix has an inverse exactly when its determinant is not zero (Theorem 4, Section 4.5), and then A−1=1∣A∣adj⁡AA^{-1} = \frac{1}{|A|}\operatorname{adj}A⁠. The formula comes straight from Theorem 1: A(adj⁡A)=∣A∣ IA(\operatorname{adj}A) = |A|\,I⁠, so dividing by the number ∣A∣|A| leaves II⁠. The phrase "if it exists" in the question is exactly the instruction to check ∣A∣≠0|A|\neq0 first. Here ∣A∣=14|A| = 14⁠, so the inverse exists.

Given, to find, and the plan
Given
The 2×22\times2 matrix AA above.
To find
A−1A^{-1}⁠, if it exists.
Plan
(1) Compute ∣A∣|A| and check it is not 0. (2) Find the four cofactors and adj⁡A\operatorname{adj}A⁠. (3) Divide by ∣A∣|A|⁠. (4) Verify AA−1=IAA^{-1}=I entry by entry.

Basics you need

1A 2×22\times2 determinant

∣A∣=a11a22−a12a21|A| = a_{11}a_{22} - a_{12}a_{21} (Section 4.2.2). Lesson: determinant of a square matrix

2Cofactors and the 2×22\times2 adjoint

Aij=(−1)i+jMijA_{ij}=(-1)^{i+j}M_{ij}⁠; adj⁡A\operatorname{adj}A is the transpose of [Aij][A_{ij}]⁠. For a 2×22\times2 matrix: swap the diagonal entries, change the sign of the other two (Remark after Example 12). Lesson: the adjoint

3Inverse via the adjoint

If ∣A∣≠0|A|\neq0⁠, then A−1=1∣A∣adj⁡AA^{-1} = \frac{1}{|A|}\operatorname{adj}A⁠; if ∣A∣=0|A|=0⁠, there is no inverse (Theorem 4, Section 4.5). Basics: why the inverse divides by |A|

4Scalar times a matrix

k Bk\,B multiplies every entry of BB by kk (Chapter 3, Section 3.4.2). Basics: scaling and adding matrices

5What "inverse" means

BB is the inverse of AA when AB=BA=IAB = BA = I (Chapter 3, Definition 6). So the final test of any answer is to multiply. Basics: matrix facts the chapter uses

Solution

  1. Compute ∣A∣|A| and decide whether A−1A^{-1} exists

    ∣A∣=∣2−243∣=2⋅3−(−2)⋅4=6+8=14\begin{aligned} |A| &= \begin{vmatrix} 2 & -2 \\ 4 & 3 \end{vmatrix} = 2\cdot 3 - (-2)\cdot 4 \\ &= 6 + 8 = 14 \end{aligned}

    ∣A∣=14≠0|A| = 14 \neq 0⁠, so AA is non-singular and A−1A^{-1} exists.

    Theorem 4 says a square matrix is invertible if and only if it is non-singular, that is ∣A∣≠0|A|\neq0⁠. Checking this first tells us whether the rest of the work makes sense: if ∣A∣|A| were 0, the answer would simply be "no inverse".
    Watch the signs: a12a21=(−2)⋅4=−8a_{12}a_{21} = (-2)\cdot4 = -8⁠, and subtracting −8-8 adds 8. Basics: algebra the chapter leans on
  2. The four cofactors

    For each entry, delete its row and its column; the single entry left over is the minor MijM_{ij}⁠. Then multiply by (−1)i+j(-1)^{i+j}⁠, which is ++ for positions (1,1)(1,1)⁠, (2,2)(2,2) and −- for (1,2)(1,2)⁠, (2,1)(2,1)⁠:

    ElementMinorCofactor
    a11=2a_{11} = 2⁠; delete row 1 and column 1M11=3M_{11} = 3A11=+(3)=3A_{11} = +(3) = 3
    a12=−2a_{12} = -2⁠; delete row 1 and column 2M12=4M_{12} = 4A12=−(4)=−4A_{12} = -(4) = -4
    a21=4a_{21} = 4⁠; delete row 2 and column 1M21=−2M_{21} = -2A21=−(−2)=2A_{21} = -(-2) = 2
    a22=3a_{22} = 3⁠; delete row 2 and column 2M22=2M_{22} = 2A22=+(2)=2A_{22} = +(2) = 2
    Deleting row ii and column jj of a 2×22\times2 matrix leaves the single entry diagonally opposite, and that is the minor. The positions (1,2)(1,2) and (2,1)(2,1) have odd i+ji+j⁠, so their minors change sign.
    Definitions 1 and 2, Section 4.4. Basics: the + − + sign pattern
  3. Build adj⁡A\operatorname{adj}A

    [Aij]=[3−422][A_{ij}] = \begin{bmatrix} 3 & -4 \\ 2 & 2 \end{bmatrix} adj⁡A=[32−42]\operatorname{adj}A = \begin{bmatrix} 3 & 2 \\ -4 & 2 \end{bmatrix}

    Shortcut check: swapping the diagonal entries 22 and 33 and changing the signs of −2-2 and 44 gives the same adj⁡A\operatorname{adj}A⁠.

    The adjoint is the transpose of the cofactor matrix (Definition 3, Section 4.5.1). The Remark after Example 12 gives the same result in one move for 2×22\times2 matrices; doing both catches slips.
    Transpose: row ii becomes column ii⁠. Basics: transpose and identity
  4. Divide by ∣A∣|A|

    A−1=1∣A∣adj⁡A=114[32−42]A^{-1} = \frac{1}{|A|}\operatorname{adj}A = \frac{1}{14}\begin{bmatrix} 3 & 2 \\ -4 & 2 \end{bmatrix}

    Multiplying each entry by 114\frac{1}{14} and reducing the fractions:

    A−1=[31417−2717]A^{-1} = \begin{bmatrix} \frac{3}{14} & \frac{1}{7} \\ -\frac{2}{7} & \frac{1}{7} \end{bmatrix}
    Theorem 4 gives A−1=1∣A∣adj⁡AA^{-1} = \frac{1}{|A|}\operatorname{adj}A⁠. Keeping the factor 114\frac{1}{14} outside, as the textbook does, avoids fractions inside the matrix; both forms are the same matrix.
    Scalar multiplication acts on every entry (Chapter 3, Section 3.4.2). Lesson: inverse of a matrix
  5. Verify AA−1=IAA^{-1} = I

    Since A−1=114adj⁡AA^{-1} = \frac{1}{14}\operatorname{adj}A⁠, we have AA−1=114(A(adj⁡A))AA^{-1} = \frac{1}{14}\bigl(A(\operatorname{adj}A)\bigr)⁠. Compute A(adj⁡A)=[pij]A(\operatorname{adj}A)=[p_{ij}]⁠, row of AA times column of adj⁡A\operatorname{adj}A⁠:

    p11=2⋅3+(−2)⋅(−4)=6+8=14p12=2⋅2+(−2)⋅2=4−4=0p21=4⋅3+3⋅(−4)=12−12=0p22=4⋅2+3⋅2=8+6=14\begin{aligned} p_{11} &= 2\cdot 3 + (-2)\cdot (-4) \\ &= 6 + 8 = 14 \\ p_{12} &= 2\cdot 2 + (-2)\cdot 2 \\ &= 4 - 4 = 0 \\ p_{21} &= 4\cdot 3 + 3\cdot (-4) \\ &= 12 - 12 = 0 \\ p_{22} &= 4\cdot 2 + 3\cdot 2 \\ &= 8 + 6 = 14 \end{aligned} AA−1=114[140014]=[1001]=IAA^{-1} = \frac{1}{14}\begin{bmatrix} 14 & 0 \\ 0 & 14 \end{bmatrix} = \begin{bmatrix} 1 & 0 \\ 0 & 1 \end{bmatrix} = I

    The other order works too. With (adj⁡A)A=[qij](\operatorname{adj}A)A = [q_{ij}]⁠:

    q11=3⋅2+2⋅4=6+8=14q12=3⋅(−2)+2⋅3=−6+6=0q21=(−4)⋅2+2⋅4=−8+8=0q22=(−4)⋅(−2)+2⋅3=8+6=14\begin{aligned} q_{11} &= 3\cdot 2 + 2\cdot 4 \\ &= 6 + 8 = 14 \\ q_{12} &= 3\cdot (-2) + 2\cdot 3 \\ &= -6 + 6 = 0 \\ q_{21} &= (-4)\cdot 2 + 2\cdot 4 \\ &= -8 + 8 = 0 \\ q_{22} &= (-4)\cdot (-2) + 2\cdot 3 \\ &= 8 + 6 = 14 \end{aligned}

    so A−1A=114 (14 I)=IA^{-1}A = \frac{1}{14}\,(14\,I) = I as well.

    By definition (Chapter 3, Definition 6), BB is the inverse of AA when AB=BA=IAB = BA = I⁠. Both products give II⁠, so the matrix of Step 4 is A−1A^{-1}⁠. It is the only one, because the inverse of a matrix is unique (Chapter 3, Theorem 3).
    Pulling the scalar out: A(kB)=k(AB)A(kB) = k(AB)⁠. Basics: matrix facts the chapter uses
Result

∣A∣=14≠0|A| = 14\neq0⁠, so A−1A^{-1} exists and

A−1=114[32−42]=[31417−2717]\begin{aligned} A^{-1} &= \frac{1}{14}\begin{bmatrix} 3 & 2 \\ -4 & 2 \end{bmatrix} \\ &= \begin{bmatrix} \frac{3}{14} & \frac{1}{7} \\ -\frac{2}{7} & \frac{1}{7} \end{bmatrix} \end{aligned}

(Textbook answer: the same matrix, written with the factor 114\frac{1}{14} outside.)

Check it with numbers

An inverse undoes multiplication by AA⁠. Take x=(1, 1)\mathbf{x} = (1,\ 1) as a column and compute b=Ax\mathbf{b} = A\mathbf{x}⁠:

b=Ax=[2−243][11]=[2−24+3]=[07]\begin{aligned} \mathbf{b} = A\mathbf{x} &= \begin{bmatrix} 2 & -2 \\ 4 & 3 \end{bmatrix}\begin{bmatrix} 1 \\ 1 \end{bmatrix} \\ &= \begin{bmatrix} 2 - 2 \\ 4 + 3 \end{bmatrix} \\ &= \begin{bmatrix} 0 \\ 7 \end{bmatrix} \end{aligned}

Then A−1b=1∣A∣(adj⁡A) bA^{-1}\mathbf{b} = \frac{1}{|A|}(\operatorname{adj}A)\,\mathbf{b} should return x\mathbf{x}⁠. Entry ii of (adj⁡A) b(\operatorname{adj}A)\,\mathbf{b} is row ii of adj⁡A\operatorname{adj}A times b\mathbf{b}⁠, written with the products already worked out:

(adj⁡A) b=[0+140+14]=[1414]\begin{aligned} (\operatorname{adj}A)\,\mathbf{b} &= \begin{bmatrix} 0 + 14 \\ 0 + 14 \end{bmatrix} \\ &= \begin{bmatrix} 14 \\ 14 \end{bmatrix} \end{aligned} A−1b=114[1414]=[11]A^{-1}\mathbf{b} = \frac{1}{14}\begin{bmatrix} 14 \\ 14 \end{bmatrix} = \begin{bmatrix} 1 \\ 1 \end{bmatrix}

We are back at x=(1, 1)\mathbf{x} = (1,\ 1)⁠, as an inverse must give.

Common mistakes
  1. Skipping the determinant check. "If it exists" means: compute ∣A∣|A| first. With ∣A∣=0|A|=0 there is no inverse to find.
  2. Swapping the wrong pair. In the 2×22\times2 adjoint it is the diagonal pair that changes places; the off-diagonal pair stays put and changes sign.
  3. Subtracting a negative. ∣A∣=2⋅3−(−2)⋅4=6+8=14|A| = 2\cdot3 - (-2)\cdot4 = 6 + 8 = 14⁠. Writing 6−8=−26 - 8 = -2 changes every entry of the answer.
  4. Not checking. One multiplication A(adj⁡A)A(\operatorname{adj}A) confirms the answer; if it is not ∣A∣ I|A|\,I⁠, something is wrong.
What to take away
  1. For a 2×22\times2 matrix with ∣A∣=a11a22−a12a21≠0|A| = a_{11}a_{22}-a_{12}a_{21} \neq 0⁠, A−1=1∣A∣[a22−a12−a21a11].A^{-1} = \frac{1}{|A|}\begin{bmatrix} a_{22} & -a_{12} \\ -a_{21} & a_{11}\end{bmatrix}.
  2. The formula works because A(adj⁡A)=∣A∣ IA(\operatorname{adj}A) = |A|\,I (Theorem 1), and dividing by ∣A∣|A| is allowed only when ∣A∣≠0|A|\neq0⁠.
  3. Always confirm with AA−1=IAA^{-1} = I⁠.

Every fact used, and where it comes from

FactUsed inSource
∣A∣=a11a22−a12a21|A| = a_{11}a_{22}-a_{12}a_{21}Step 1Section 4.2.2 (Lesson: determinant)
Non-singular: ∣A∣≠0|A|\neq0Step 1Definition 5, Section 4.5
Minor and cofactorStep 2Definitions 1 and 2, Section 4.4 (Lesson: minors and cofactors)
Adjoint; 2×22\times2 shortcutStep 3Definition 3 and Remark after Example 12, Section 4.5.1 (Lesson: the adjoint)
Invertible   ⟺  ∣A∣≠0\iff |A|\neq0⁠; A−1=1∣A∣adj⁡AA^{-1} = \frac{1}{|A|}\operatorname{adj}ASteps 1, 4Theorem 4, Section 4.5 (Lesson: inverse of a matrix)
Scalar multiple of a matrixSteps 4, 5Chapter 3, Section 3.4.2
Product of matrices, row times columnStep 5, CheckChapter 3, Section 3.4.5 (Basics: matrix facts)
A(adj⁡A)=(adj⁡A)A=∣A∣ IA(\operatorname{adj}A)=(\operatorname{adj}A)A=|A|\,IStep 5Theorem 1, Section 4.5.1
Definition of inverse; uniquenessStep 5Chapter 3, Definition 6 and Theorem 3, Section 3.7
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