Linear Algebra Quiz: Solving Ax Equals B Via Inverses
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Solving Ax Equals B Via InversesQuestion 1 of 20

Let AA and BB be invertible n×nn \times n matrices. To solve the matrix equation ABx=cAB\vec{x} = \vec{c} for the vector x\vec{x}, which of the following computations is correct?

x=A1B1c\vec{x} = A^{-1}B^{-1}\vec{c}
x=B1A1c\vec{x} = B^{-1}A^{-1}\vec{c}
x=cA1B1\vec{x} = \vec{c}A^{-1}B^{-1}
x=cB1A1\vec{x} = \vec{c}B^{-1}A^{-1}
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Linear Algebra Quiz: Solving Ax Equals B Via Inverses

Practice Solving Ax Equals B Via Inverses in Linear Algebra with focused quiz questions that help you check what you know, review explanations, and build confidence with test-style prompts.

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This quiz focuses on Solving Ax Equals B Via Inverses, giving you a quick way to practice the rules, question types, and explanations that matter most for Linear Algebra.

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Question 1

Let AA and BB be invertible n×nn \times n matrices. To solve the matrix equation ABx=cAB\vec{x} = \vec{c} for the vector x\vec{x}, which of the following computations is correct?

  1. x=A1B1c\vec{x} = A^{-1}B^{-1}\vec{c}
  2. x=B1A1c\vec{x} = B^{-1}A^{-1}\vec{c} (correct answer)
  3. x=cA1B1\vec{x} = \vec{c}A^{-1}B^{-1}
  4. x=cB1A1\vec{x} = \vec{c}B^{-1}A^{-1}
Explanation: To isolate x\vec{x}, we need to multiply both sides by the inverse of the matrix product ABAB. The inverse of a product of matrices is the product of their inverses in reverse order: (AB)1=B1A1(AB)^{-1} = B^{-1}A^{-1}. We must left-multiply both sides of the equation by this inverse: (AB)1(AB)x=(AB)1c(AB)^{-1}(AB)\vec{x} = (AB)^{-1}\vec{c}, which simplifies to Ix=B1A1cI\vec{x} = B^{-1}A^{-1}\vec{c}, so x=B1A1c\vec{x} = B^{-1}A^{-1}\vec{c}.

Question 2

Consider the system of equations 3x+2y=73x + 2y = 7 and 4x+3y=94x + 3y = 9. If this system is represented as Ax=bA\vec{x}=\vec{b}, and the solution is found using x=A1b\vec{x}=A^{-1}\vec{b}, what is the matrix A1A^{-1}?

  1. (3243)\begin{pmatrix} 3 & -2 \\ -4 & 3 \end{pmatrix} (correct answer)
  2. (3243)\begin{pmatrix} -3 & 2 \\ 4 & -3 \end{pmatrix}
  3. (3423)\begin{pmatrix} 3 & 4 \\ 2 & 3 \end{pmatrix}
  4. (1241)\begin{pmatrix} 1 & -2 \\ -4 & 1 \end{pmatrix}
Explanation: The matrix for the system is A=(3243)A = \begin{pmatrix} 3 & 2 \\ 4 & 3 \end{pmatrix}. To find the inverse, first calculate the determinant: det(A)=(3)(3)(2)(4)=98=1\det(A) = (3)(3) - (2)(4) = 9 - 8 = 1. The formula for the inverse of a 2x2 matrix (abcd)\begin{pmatrix} a & b \\ c & d \end{pmatrix} is 1adbc(dbca)\frac{1}{ad-bc}\begin{pmatrix} d & -b \\ -c & a \end{pmatrix}. Applying this, A1=11(3243)=(3243)A^{-1} = \frac{1}{1}\begin{pmatrix} 3 & -2 \\ -4 & 3 \end{pmatrix} = \begin{pmatrix} 3 & -2 \\ -4 & 3 \end{pmatrix}.

Question 3

The system of linear equations Ax=bA\vec{x}=\vec{b} is to be solved using the matrix inverse method, yielding the unique solution x=A1b\vec{x}=A^{-1}\vec{b}. Which of the following conditions is necessary for this method to be applicable?

  1. The matrix AA must be symmetric.
  2. The vector b\vec{b} must not be the zero vector.
  3. The number of equations must be greater than the number of variables.
  4. The determinant of matrix AA must be non-zero. (correct answer)
Explanation: The formula x=A1b\vec{x}=A^{-1}\vec{b} requires the existence of the inverse matrix A1A^{-1}. A square matrix AA has an inverse if and only if it is non-singular, which is equivalent to its determinant being non-zero. The other conditions are not necessary. Symmetry is not required for invertibility. The method works for b=0\vec{b}=\vec{0} (giving x=0\vec{x}=\vec{0}). For a unique solution via matrix inversion, AA must be a square matrix (number of equations equals number of variables).

Question 4

Consider the system of equations defined by Ax=bA\vec{x}=\vec{b} where A=(k49k)A = \begin{pmatrix} k & 4 \\ 9 & k \end{pmatrix}. For which value(s) of kk is it NOT possible to find a unique solution using the formula x=A1b\vec{x}=A^{-1}\vec{b}?

  1. k=0k=0 only
  2. k=36k=36 only
  3. k=6k=6 and k=6k=-6 (correct answer)
  4. k=4k=4 and k=9k=9
Explanation: The inverse method is not possible when the matrix AA is singular, which occurs when its determinant is zero. The determinant of AA is det(A)=(k)(k)(4)(9)=k236\det(A) = (k)(k) - (4)(9) = k^2 - 36. Setting the determinant to zero gives the equation k236=0k^2 - 36 = 0. This factors as (k6)(k+6)=0(k-6)(k+6)=0, which has solutions k=6k=6 and k=6k=-6. For these values of kk, the matrix AA is not invertible.

Question 5

To solve the matrix equation XA=BXA = B for the matrix XX, where AA and BB are known n×nn \times n matrices and AA is invertible, one must perform a matrix multiplication involving A1A^{-1} and BB. Which expression correctly calculates XX?

  1. X=A1BX = A^{-1}B
  2. X=BA1X = BA^{-1} (correct answer)
  3. X=(AB)1X = (AB)^{-1}
  4. X=B(A1)TX = B(A^{-1})^T
Explanation: To isolate the matrix XX in the equation XA=BXA = B, we need to multiply by A1A^{-1}. Since matrix multiplication is not commutative, the side on which we multiply matters. To cancel AA, we must multiply by A1A^{-1} on the right side of both sides of the equation: XAA1=BA1XAA^{-1} = BA^{-1}. This simplifies to XI=BA1XI = BA^{-1}, so X=BA1X = BA^{-1}. Choice A is the solution to AX=BAX=B.

Question 6

Let AA be an invertible 3×33 \times 3 matrix. If one were to solve the system Ax=e2A\vec{x} = \vec{e_2}, where e2=(010)\vec{e_2} = \begin{pmatrix} 0 \\ 1 \\ 0 \end{pmatrix} is the second standard basis vector, what would the solution vector x\vec{x} represent?

  1. The second row of A1A^{-1}.
  2. The second column of A1A^{-1}. (correct answer)
  3. The inverse of the second column of AA.
  4. The zero vector.
Explanation: The solution to the system is given by x=A1b\vec{x} = A^{-1}\vec{b}. In this case, b=e2\vec{b} = \vec{e_2}. So, x=A1e2\vec{x} = A^{-1}\vec{e_2}. The product of a matrix and the jj-th standard basis vector, ej\vec{e_j}, results in the jj-th column of that matrix. Therefore, A1e2A^{-1}\vec{e_2} is precisely the second column of the matrix A1A^{-1}.

Question 7

For an invertible matrix A=(abcd)A = \begin{pmatrix} a & b \\ c & d \end{pmatrix}, the solution to Ax=(pq)A\vec{x} = \begin{pmatrix} p \\ q \end{pmatrix} is x=(x1x2)\vec{x} = \begin{pmatrix} x_1 \\ x_2 \end{pmatrix}. Which expression represents x1x_1?

  1. x1=apcqadbcx_1 = \frac{ap - cq}{ad-bc}
  2. x1=dpbqx_1 = dp - bq
  3. x1=aqcpadbcx_1 = \frac{aq - cp}{ad-bc}
  4. x1=dpbqadbcx_1 = \frac{dp - bq}{ad-bc} (correct answer)
Explanation: First, find the inverse of AA. The determinant is det(A)=adbc\det(A)=ad-bc. The inverse is A1=1adbc(dbca)A^{-1} = \frac{1}{ad-bc} \begin{pmatrix} d & -b \\ -c & a \end{pmatrix}. The solution is x=A1b\vec{x} = A^{-1}\vec{b}. So, (x1x2)=1adbc(dbca)(pq)=1adbc(dpbqcp+aq)\begin{pmatrix} x_1 \\ x_2 \end{pmatrix} = \frac{1}{ad-bc} \begin{pmatrix} d & -b \\ -c & a \end{pmatrix} \begin{pmatrix} p \\ q \end{pmatrix} = \frac{1}{ad-bc} \begin{pmatrix} dp - bq \\ -cp + aq \end{pmatrix}. The first component of the solution vector is x1=dpbqadbcx_1 = \frac{dp - bq}{ad-bc}.

Question 8

A bakery sells two types of boxes: a pastry box with 3 croissants and 4 muffins, and a breakfast box with 5 croissants and 2 muffins. On a given day, they use a total of 160 croissants and 100 muffins to fill an order. Let pp be the number of pastry boxes and bb be the number of breakfast boxes. The situation is modeled by the matrix equation A(pb)=(160100)A\begin{pmatrix} p \\ b \end{pmatrix} = \begin{pmatrix} 160 \\ 100 \end{pmatrix}. Which of the following expressions correctly solves for the number of boxes?

  1. (3542)(160100)\begin{pmatrix} 3 & 5 \\ 4 & 2 \end{pmatrix} \begin{pmatrix} 160 \\ 100 \end{pmatrix}
  2. (correct answer)
  3. (2543)(160100)\begin{pmatrix} 2 & -5 \\ -4 & 3 \end{pmatrix} \begin{pmatrix} 160 \\ 100 \end{pmatrix}
Explanation: The system of equations is 3p+5b=1603p + 5b = 160 (croissants) and 4p+2b=1004p + 2b = 100 (muffins). The coefficient matrix is A=(3542)A = \begin{pmatrix} 3 & 5 \\ 4 & 2 \end{pmatrix}. The solution is given by (pb)=A1(160100)\begin{pmatrix} p \\ b \end{pmatrix} = A^{-1}\begin{pmatrix} 160 \\ 100 \end{pmatrix}. We must calculate A1A^{-1}. The determinant is det(A)=(3)(2)(5)(4)=620=14\det(A) = (3)(2) - (5)(4) = 6 - 20 = -14. The inverse is A1=114(2543)A^{-1} = \frac{1}{-14}\begin{pmatrix} 2 & -5 \\ -4 & 3 \end{pmatrix}. Therefore, the correct expression for the solution is 114(2543)(160100)\frac{1}{-14} \begin{pmatrix} 2 & -5 \\ -4 & 3 \end{pmatrix} \begin{pmatrix} 160 \\ 100 \end{pmatrix}.

Question 9

A system of linear equations Ax=bA\vec{x}=\vec{b} is solved using the inverse matrix A1A^{-1}. Given A1=(2513)A^{-1} = \begin{pmatrix} 2 & 5 \\ 1 & 3 \end{pmatrix} and b=(12)\vec{b} = \begin{pmatrix} -1 \\ 2 \end{pmatrix}, what is the solution vector x\vec{x}?

  1. (01)\begin{pmatrix} 0 \\ 1 \end{pmatrix}
  2. (85)\begin{pmatrix} 8 \\ 5 \end{pmatrix} (correct answer)
  3. (95)\begin{pmatrix} -9 \\ -5 \end{pmatrix}
  4. (34)\begin{pmatrix} 3 \\ 4 \end{pmatrix}
Explanation: The solution is given by the formula x=A1b\vec{x} = A^{-1}\vec{b}. We compute the matrix-vector product: $$\vec{x} = \begin{pmatrix} 2 & 5 \ 1 & 3 \end{pmatrix} \begin{pmatrix} -1 \ 2 \end{pmatrix} = \begin{pmatrix} (2)(-1) + (5)(2) \ (1)(-1) + (3)(2) \end{pmatrix} = \begin{pmatrix} -2 + 10 \ -1 + 6 \end{pmatrix} = \begin{pmatrix} 8 \ 5 \end{pmatrix}

Question 10

The solution to the system Ax=bA\vec{x}=\vec{b} is x=(52)\vec{x}=\begin{pmatrix} 5 \\ -2 \end{pmatrix}. If the same matrix AA is used in a new system Ay=cA\vec{y}=\vec{c}, where c=3b\vec{c} = 3\vec{b}, what is the solution vector y\vec{y}?

  1. (156)\begin{pmatrix} 15 \\ -6 \end{pmatrix} (correct answer)
  2. (5/32/3)\begin{pmatrix} 5/3 \\ -2/3 \end{pmatrix}
  3. (81)\begin{pmatrix} 8 \\ 1 \end{pmatrix}
  4. Cannot be determined without knowing the matrix AA.
Explanation: From the first system, we know that x=A1b\vec{x} = A^{-1}\vec{b}. The solution to the new system is y=A1c\vec{y} = A^{-1}\vec{c}. Since we are given that c=3b\vec{c} = 3\vec{b}, we can substitute this into the equation for y\vec{y}: y=A1(3b)\vec{y} = A^{-1}(3\vec{b}). By the properties of matrix multiplication, the scalar can be factored out: y=3(A1b)\vec{y} = 3(A^{-1}\vec{b}). Since A1b=xA^{-1}\vec{b}=\vec{x}, we have y=3x\vec{y} = 3\vec{x}. Therefore, y=3(52)=(156)\vec{y} = 3\begin{pmatrix} 5 \\ -2 \end{pmatrix} = \begin{pmatrix} 15 \\ -6 \end{pmatrix}.

Question 11

A student is solving the system Ax=bA\vec{x}=\vec{b} where A=(4253)A=\begin{pmatrix} 4 & 2 \\ 5 & 3 \end{pmatrix} and b=(810)\vec{b}=\begin{pmatrix} 8 \\ 10 \end{pmatrix}. Their work is shown below.

Step 1: Calculate determinant: det(A)=4(3)2(5)=1210=2\det(A) = 4(3) - 2(5) = 12 - 10 = 2. Step 2: Find the adjugate matrix: adj(A)=(3254)\text{adj}(A) = \begin{pmatrix} 3 & -2 \\ -5 & 4 \end{pmatrix}. Step 3: Determine the inverse: A1=(3254)A^{-1} = \begin{pmatrix} 3 & -2 \\ -5 & 4 \end{pmatrix}. Step 4: Calculate the solution: x=A1b=(3254)(810)=(40)\vec{x} = A^{-1}\vec{b} = \begin{pmatrix} 3 & -2 \\ -5 & 4 \end{pmatrix}\begin{pmatrix} 8 \\ 10 \end{pmatrix} = \begin{pmatrix} 4 \\ 0 \end{pmatrix}.

In which step did the student first make an error?

  1. Step 1
  2. Step 2
  3. Step 3 (correct answer)
  4. Step 4
Explanation: The formula for the inverse matrix is A1=1det(A)adj(A)A^{-1} = \frac{1}{\det(A)}\text{adj}(A). The student correctly calculated the determinant in Step 1 and the adjugate matrix in Step 2. However, in Step 3, the student failed to divide the adjugate matrix by the determinant (which is 2). The correct inverse should be A1=12(3254)A^{-1} = \frac{1}{2}\begin{pmatrix} 3 & -2 \\ -5 & 4 \end{pmatrix}. This is the first error in the process.

Question 12

A system Ax=bAx = b has a unique solution that can be expressed as x=A1bx = A^{-1}b. If matrix AA is replaced by 2A2A, what expression gives the solution to the new system (2A)y=b(2A)y = b?

  1. y=12A1by = \frac{1}{2}A^{-1}b (correct answer)
  2. y=2A1by = 2A^{-1}b
  3. y=A1by = A^{-1}b
  4. y=(2A)1by = (2A)^{-1}b
Explanation: For the system (2A)y=b(2A)y = b, we need (2A)1(2A)^{-1}. Since (2A)1=12A1(2A)^{-1} = \frac{1}{2}A^{-1}, the solution is y=(2A)1b=12A1by = (2A)^{-1}b = \frac{1}{2}A^{-1}b. Choice B incorrectly multiplies by 2 instead of 12\frac{1}{2}. Choice C ignores the scalar multiplication entirely. Choice D is technically correct notation but doesn't simplify the expression as requested.

Question 13

Consider the block matrix M=(A00B)M = \begin{pmatrix} A & 0 \\ 0 & B \end{pmatrix} where AA and BB are invertible 2×22 \times 2 matrices. To solve M(xy)=(uv)M\begin{pmatrix} x \\ y \end{pmatrix} = \begin{pmatrix} u \\ v \end{pmatrix} using M1M^{-1}, which statement is correct?

  1. $$ and the system decouples into $$Ax = u$$ and $$By = v$$ (correct answer)
  2. $$ and the system requires simultaneous solution
  3. $$ and cross-terms appear in the solution
  4. $$ but the system cannot be decoupled due to matrix structure
Explanation: For a block diagonal matrix M=(A00B)M = \begin{pmatrix} A & 0 \\ 0 & B \end{pmatrix} , the inverse is M1=(A100B1)M^{-1} = \begin{pmatrix} A^{-1} & 0 \\ 0 & B^{-1} \end{pmatrix} . The system M(xy)=(uv)M\begin{pmatrix} x \\ y \end{pmatrix} = \begin{pmatrix} u \\ v \end{pmatrix} decouples into two independent systems: Ax=uAx = u and By=vBy = v. Choice B gives wrong form for block diagonal inverse. Choice C confuses block diagonal with general 2×22 \times 2 matrix inverse formula. Choice D correctly identifies M1M^{-1} but incorrectly claims the system cannot decouple.

Question 14

The system Ax=bAx = b has solution xx^*. A student claims that if A1\|A^{-1}\| is large, then small changes in bb will cause large changes in the solution xx^*. To test this claim using the inverse method x=A1bx = A^{-1}b, which approach is most appropriate?

  1. Compute A1(b+δb)A1b\|A^{-1}(b + \delta b)\| - \|A^{-1}b\| for small perturbations δb\delta b and verify it's proportional to A1\|A^{-1}\|
  2. Compute A1(b+δb)A1b\|A^{-1}(b + \delta b) - A^{-1}b\| for small perturbations δb\delta b and compare to A1δb\|A^{-1}\|\|\delta b\| (correct answer)
  3. Show that det(A1)\det(A^{-1}) is large whenever A1\|A^{-1}\| is large, confirming sensitivity to perturbations in bb
  4. Verify that A1bA^{-1}b exists whenever A1\|A^{-1}\| is large, which guarantees stability of the solution method
Explanation: The change in solution is Δx=A1(b+δb)A1b=A1δb\Delta x = A^{-1}(b + \delta b) - A^{-1}b = A^{-1}\delta b. So Δx=A1δbA1δb\|\Delta x\| = \|A^{-1}\delta b\| \leq \|A^{-1}\|\|\delta b\|. Testing this relationship directly validates the student's claim about sensitivity. Choice A looks at changes in norms rather than changes in vectors. Choice C incorrectly relates determinant to matrix norm and sensitivity. Choice D confuses existence of solution with stability of solution.

Question 15

For the system Ax=bAx = b where A=(cosθsinθsinθcosθ)A = \begin{pmatrix} \cos\theta & -\sin\theta \\ \sin\theta & \cos\theta \end{pmatrix} and b=(10)b = \begin{pmatrix} 1 \\ 0 \end{pmatrix} , the solution using x=A1bx = A^{-1}b is $$x = \begin{pmatrix} \cos\theta \ \sin\theta \end{pmatrix}

  1. Rotating the vector bb counterclockwise by angle θ\theta to obtain the solution vector xx
  2. Rotating the vector bb clockwise by angle θ\theta to obtain the solution vector xx (correct answer)
  3. Projecting the vector bb onto the line making angle θ\theta with the positive xx-axis
  4. Reflecting the vector bb across the line making angle θ/2\theta/2 with the positive xx-axis
Explanation: Matrix AA is a rotation matrix that rotates vectors counterclockwise by θ\theta. Since x=A1bx = A^{-1}b, we have Ax=bAx = b, meaning rotating xx counterclockwise by θ\theta gives b=(10)b = \begin{pmatrix} 1 \\ 0 \end{pmatrix} . Therefore, xx is obtained by rotating bb clockwise by θ\theta. Indeed, A1=AT=(cosθsinθsinθcosθ)A^{-1} = A^T = \begin{pmatrix} \cos\theta & \sin\theta \\ -\sin\theta & \cos\theta \end{pmatrix} rotates clockwise by θ\theta. Choice A incorrectly states counterclockwise rotation. Choice C describes projection, not rotation. Choice D describes reflection, which is incorrect.

Question 16

Consider two systems: Ax=b1Ax = b_1 with solution x1=A1b1x_1 = A^{-1}b_1 and Ax=b2Ax = b_2 with solution x2=A1b2x_2 = A^{-1}b_2. If b2=b1+αAx1b_2 = b_1 + \alpha Ax_1 for some scalar α\alpha, what is x2x_2 in terms of x1x_1?

  1. x2=(1+α)A1x1x_2 = (1 + \alpha)A^{-1}x_1
  2. x2=x1+αA1x1x_2 = x_1 + \alpha A^{-1}x_1
  3. x2=x1+αx1=(1+α)x1x_2 = x_1 + \alpha x_1 = (1 + \alpha)x_1 (correct answer)
  4. x2=x1+αA2x1x_2 = x_1 + \alpha A^{-2}x_1
Explanation: When you encounter linear systems with related right-hand sides, the key insight is recognizing how changes in the input vector affect the solution. This question tests your understanding of how matrix inverses distribute over vector addition. Let's work through this systematically. We know that x2=A1b2x_2 = A^{-1}b_2, and we're given that b2=b1+αAx1b_2 = b_1 + \alpha Ax_1. Substituting this expression: x2=A1(b1+αAx1)=A1b1+A1(αAx1)=A1b1+αA1Ax1x_2 = A^{-1}(b_1 + \alpha Ax_1) = A^{-1}b_1 + A^{-1}(\alpha Ax_1) = A^{-1}b_1 + \alpha A^{-1}Ax_1 Since A1A=IA^{-1}A = I (the identity matrix), we have A1Ax1=x1A^{-1}Ax_1 = x_1. Also, since x1=A1b1x_1 = A^{-1}b_1, our expression becomes: x2=x1+αx1=(1+α)x1x_2 = x_1 + \alpha x_1 = (1 + \alpha)x_1 This confirms answer C is correct. Looking at the wrong answers: A) x2=(1+α)A1x1x_2 = (1 + \alpha)A^{-1}x_1 incorrectly applies the inverse to x1x_1 instead of recognizing that A1b1=x1A^{-1}b_1 = x_1. B) x2=x1+αA1x1x_2 = x_1 + \alpha A^{-1}x_1 stops one step short of the simplification, failing to recognize that A1Ax1=x1A^{-1}Ax_1 = x_1. D) x2=x1+αA2x1x_2 = x_1 + \alpha A^{-2}x_1 incorrectly squares the inverse, showing a fundamental misunderstanding of matrix multiplication. Study tip: When working with linear systems and matrix inverses, always look for opportunities to use the fact that A1A=IA^{-1}A = I. This identity is your key to simplifying expressions involving both a matrix and its inverse.

Question 17

A 3×33 \times 3 matrix AA has the property that A1=12ATA^{-1} = \frac{1}{2}A^T. If the system Ax=e1Ax = e_1 (where e1=(100)e_1 = \begin{pmatrix} 1 \\ 0 \\ 0 \end{pmatrix} ) has solution $$x = \begin{pmatrix} a \ b \ c \end{pmatrix}

  1. a2+b2+c2=14a^2 + b^2 + c^2 = \frac{1}{4}
  2. a2+b2+c2=2a^2 + b^2 + c^2 = 2
  3. a2+b2+c2=1a^2 + b^2 + c^2 = 1
  4. a2+b2+c2=12a^2 + b^2 + c^2 = \frac{1}{2} (correct answer)
Explanation: This question tests your understanding of orthogonal matrices and their properties. When you see that A1=12ATA^{-1} = \frac{1}{2}A^T, you're dealing with a scaled orthogonal matrix, which means the columns (and rows) of AA have special geometric relationships. To find the constraint, start with the given relationship A1=12ATA^{-1} = \frac{1}{2}A^T. Since AA1=IAA^{-1} = I, we have: A12AT=IA \cdot \frac{1}{2}A^T = I 12AAT=I\frac{1}{2}AA^T = I AAT=2IAA^T = 2I Now, since Ax=e1Ax = e_1 where x=(abc)x = \begin{pmatrix} a \\ b \\ c \end{pmatrix} , we can use the relationship AAT=2IAA^T = 2I. The first column of AATAA^T equals AA times the first column of ATA^T (which is the first row of AA). But we also know that the first column of AATAA^T is the first column of 2I2I, which is $$ \begin{pmatrix} 2 \ 0 \ 0 \end{pmatrix} Since $$Ax = e_1$$, we have $$x = A^{-1}e_1 = \frac{1}{2}A^T e_1$$, which means $$x$$ is half the first column of $$A^T$$ (or half the first row of $$A$$). The constraint $$AA^T = 2I$$ tells us that the dot product of the first row of $$A$$ with itself equals 2. Since $$x$$ is half this row, we get $$a^2 + b^2 + c^2 = \frac{1}{2}$$. Choice A gives $$\frac{1}{4}$$ (dividing by 4 instead of 2), choice B gives 2 (forgetting the scaling), and choice C gives 1 (assuming standard orthogonal matrix). Choice D correctly accounts for the $$\frac{1}{2}$$ scaling factor. **Study tip:** When working with scaled orthogonal matrices, always track how the scaling affects inner products and norms throughout your calculations.

Question 18

The matrix equation AXB=CAXB = C can be solved for XX when AA and BB are invertible. If this equation is rewritten as a system of the form Mx=cMx = c where xx contains the entries of XX, which matrix MM allows solution via x=M1cx = M^{-1}c?

  1. M=ABM = A \otimes B where \otimes denotes the Kronecker product
  2. M=ABM = AB using standard matrix multiplication
  3. M=ABTM = A \otimes B^T where \otimes denotes the Kronecker product
  4. M=BTAM = B^T \otimes A where \otimes denotes the Kronecker product (correct answer)
Explanation: When you encounter matrix equations like AXB=CAXB = C, the key is understanding how to vectorize them using the Kronecker product. This transformation allows you to convert a matrix equation into a standard linear system that can be solved using matrix inversion. The vectorization of AXB=CAXB = C follows a specific rule: vec(AXB)=(BTA)vec(X)\text{vec}(AXB) = (B^T \otimes A)\text{vec}(X). This identity comes from the fundamental property that vec(PQR)=(RTP)vec(Q)\text{vec}(PQR) = (R^T \otimes P)\text{vec}(Q). Applying this to our equation, we get (BTA)vec(X)=vec(C)(B^T \otimes A)\text{vec}(X) = \text{vec}(C), which gives us the system Mx=cMx = c where M=BTAM = B^T \otimes A. Answer D is correct because it provides the matrix M=BTAM = B^T \otimes A that emerges from proper vectorization of the original equation. Answer A is wrong because ABA \otimes B doesn't account for the transpose needed in the vectorization formula. Answer B is incorrect because ABAB uses standard multiplication, which doesn't preserve the structure needed for vectorization—the dimensions won't even match in most cases. Answer C is close but has the Kronecker product terms reversed; ABTA \otimes B^T doesn't follow the correct vectorization rule. Remember this pattern: when vectorizing AXB=CAXB = C, the coefficient matrix is always (BTA)(B^T \otimes A)—the rightmost matrix gets transposed and appears first in the Kronecker product. This reversal and transpose rule is essential for solving matrix equations via vectorization.

Question 19

Matrix AA satisfies A2=I+2AA^2 = I + 2A. To solve Ax=bAx = b using the inverse method, what is the correct expression for A1A^{-1}?

  1. A1=I+2AA^{-1} = I + 2A
  2. A1=12(AI)A^{-1} = \frac{1}{2}(A - I)
  3. A1=A2IA^{-1} = A - 2I (correct answer)
  4. A1=2IAA^{-1} = 2I - A
Explanation: When you encounter a matrix equation like A2=I+2AA^2 = I + 2A, you're dealing with finding a matrix inverse through algebraic manipulation. The key insight is to rearrange this equation to isolate terms that will help you express A1A^{-1}. Starting with A2=I+2AA^2 = I + 2A, subtract 2A2A from both sides: A22A=IA^2 - 2A = I. Now factor the left side: A(A2I)=IA(A - 2I) = I. This is the crucial step—you've found that AA multiplied by (A2I)(A - 2I) equals the identity matrix II. By definition of matrix inverses, if AB=IAB = I, then B=A1B = A^{-1}. Therefore, A1=A2IA^{-1} = A - 2I. Choice A (A1=I+2AA^{-1} = I + 2A) comes from incorrectly assuming you can just move terms around without proper algebraic manipulation. If this were correct, then A(I+2A)=IA(I + 2A) = I, which would mean A+2A2=IA + 2A^2 = I—but substituting our original equation shows this doesn't work. Choice B (A1=12(AI)A^{-1} = \frac{1}{2}(A - I)) likely results from incorrect factoring or arithmetic errors in the algebraic manipulation. Choice D (A1=2IAA^{-1} = 2I - A) is close to the correct answer but has the wrong sign. This might come from incorrectly rearranging A(A2I)=IA(A - 2I) = I as (2IA)A=I(2I - A)A = I. Study tip: When finding matrix inverses from polynomial equations, always factor to get the form A(expression)=IA \cdot (\text{expression}) = I. The expression in parentheses is your inverse. Double-check by verifying that your candidate inverse actually satisfies AA1=IAA^{-1} = I.

Question 20

Let A=(1224)A = \begin{pmatrix} 1 & 2 \\ 2 & 4 \end{pmatrix}. An attempt to solve the system Ax=bA\vec{x}=\vec{b} using the inverse method fails. What is the fundamental reason for this failure?

  1. The system must have no solution, regardless of b\vec{b}.
  2. AA is not a square matrix.
  3. The matrix AA is singular because its columns are linearly dependent. (correct answer)
  4. The vector b\vec{b} is the zero vector.
Explanation: The inverse method requires the matrix AA to be invertible. A matrix is invertible if and only if its determinant is non-zero. The determinant of AA is det(A)=(1)(4)(2)(2)=44=0\det(A) = (1)(4) - (2)(2) = 4 - 4 = 0. Since the determinant is zero, AA is singular and not invertible. This singularity is a result of the columns being linearly dependent (the second column is twice the first column). This is the fundamental reason the inverse method fails.