MATH 133 Linear Algebra and Geometry • McGill University, Montreal

MATH 133 practice final exam with full solutions (McGill)

This is a practice final examination for MATH 133, Linear Algebra and Geometry, the first-year course at McGill University. It covers the whole course and is weighted like a real cumulative final: one third on the material of the midterm, systems, matrices, inverses and determinants, and two thirds on what follows, vectors, lines and planes in space, the cross product, subspaces, bases and dimension, linear transformations, eigenvalues and diagonalization. Twelve questions, one hundred points, four parts.

Sit it as an exam: three hours on a timer, no calculator, no notes. Every characteristic polynomial factors by hand and every reduction keeps small integers, so a fraction that will not simplify is a signal to check your last step. Not one question repeats an exercise of the fourteen chapter sets of this site or of the practice midterm: the gestures are those of every MATH 133 final, the matrices, points and situations are new, so the paper measures what you can do rather than what you remember. Each question carries its Answers box for a quick first marking, and the full reasoning underneath.

The traps named in the solutions: answering a two-parameter question with one parameter, taking a column of A−1A^{-1} where the transpose asks for a row, swapping the indices of an adjugate entry, testing an intersection of lines with a single parameter, forgetting the one sixth of a tetrahedron, reading a basis of the column space off the reduced matrix, expanding a characteristic polynomial along the line that hides its factors, and claiming a repeated eigenvalue forbids diagonalization.

12 corrected exercises • 100 points • 180 minutes

Every MATH 133 chapter →

Part A: systems, matrices and determinants (/33)

Exercise 1: Two parameters, three verdicts

For real numbers kk and mm, consider {x+y+2z=12x+3y+3z=4x+2y+kz=m\begin{cases} x + y + 2z = 1 \\ 2x + 3y + 3z = 4 \\ x + 2y + kz = m \end{cases}.

  • a) Reduce the augmented matrix to row echelon form, naming every operation.
  • b) Say, in terms of kk and mm, when the system has no solution, exactly one solution, infinitely many solutions.
  • c) In the case of infinitely many solutions, give the general solution as a particular solution plus the solutions of the homogeneous system, and give the rank and the nullity of the coefficient matrix.
  • d) Solve the system for k=3k = 3 and m=5m = 5, and check.
Show the solution

Answers

  • a) last row (0,0,k−1∣m−3)(0, 0, k - 1 \mid m - 3)
  • b) exactly one if k≠1k \neq 1; infinitely many if k=1k = 1 and m=3m = 3; none if k=1k = 1 and m≠3m \neq 3
  • c) (x,y,z)=(−1,2,0)+t(−3,1,1)(x, y, z) = (-1, 2, 0) + t(-3, 1, 1); rank 22, nullity 11
  • d) (x,y,z)=(−4,3,1)(x, y, z) = (-4, 3, 1)

a) Start from \left(\begin{array}{ccc|c} 1 & 1 & 2 & 1 \\ 2 & 3 & 3 & 4 \\ 1 & 2 & k & m \end{array}\right). R2→R2−2R1R_2 \to R_2 - 2R_1 gives (0,1,−1∣2)(0, 1, -1 \mid 2) and R3→R3−R1R_3 \to R_3 - R_1 gives (0,1,k−2∣m−1)(0, 1, k - 2 \mid m - 1). Then R3→R3−R2R_3 \to R_3 - R_2 gives (0,0,k−1∣m−3)(0, 0, k - 1 \mid m - 3). The parameters have been gathered in the last row without a single division.

b) The last row reads (k−1)z=m−3(k - 1)z = m - 3. If k≠1k \neq 1, it has a non-zero pivot: three pivots, exactly one solution, whatever mm is. If k=1k = 1, the coefficient vanishes and the row reads 0=m−30 = m - 3: consistent only when m=3m = 3, and then rank 22 for 33 unknowns gives infinitely many solutions; when m≠3m \neq 3 it is a contradiction and there is no solution. With two parameters the verdict is a statement about the PAIR (k,m)(k, m), and mm only matters on the line k=1k = 1. Answering “no solution for k=1k = 1” without looking at mm is the typical half-mark answer.

c) For k=1k = 1 and m=3m = 3 the system is x+y+2z=1x + y + 2z = 1 and y−z=2y - z = 2. With z=tz = t: y=2+ty = 2 + t and x=1−y−2z=−1−3tx = 1 - y - 2z = -1 - 3t. So (x,y,z)=(−1,2,0)+t(−3,1,1)(x, y, z) = (-1, 2, 0) + t(-3, 1, 1): (−1,2,0)(-1, 2, 0) is a particular solution, and t(−3,1,1)t(-3, 1, 1) is the general solution of the homogeneous system, whose basic solution is (−3,1,1)(-3, 1, 1). The coefficient matrix has rank 22 and nullity 3−2=13 - 2 = 1, the number of parameters. Check in the third equation with k=1k = 1: (−1−3t)+2(2+t)+t=3(-1 - 3t) + 2(2 + t) + t = 3.

d) For k=3k = 3 and m=5m = 5, the last row reads 2z=22z = 2, so z=1z = 1. Then y−z=2y - z = 2 gives y=3y = 3, and x=1−y−2z=−4x = 1 - y - 2z = -4. Check: −4+3+2=1-4 + 3 + 2 = 1, −8+9+3=4-8 + 9 + 3 = 4, −4+6+3=5-4 + 6 + 3 = 5.

Exercise 2: An inverse, its transpose, and the elementary matrices behind it

Let A=(10−1−213023)A = \begin{pmatrix} 1 & 0 & -1 \\ -2 & 1 & 3 \\ 0 & 2 & 3 \end{pmatrix}.

  • a) Find A−1A^{-1} by reducing [A∣I][A \mid I], naming every row operation.
  • b) Check one row of A−1AA^{-1}A.
  • c) Solve ATy=(1,0,0)A^Ty = (1, 0, 0) without any new reduction.
  • d) Give (3AT)−1(3A^T)^{-1}.
  • e) Write AA as a product of elementary matrices, using the operations of a).
Show the solution

Answers

  • a) A−1=(−3−2163−1−4−21)A^{-1} = \begin{pmatrix} -3 & -2 & 1 \\ 6 & 3 & -1 \\ -4 & -2 & 1 \end{pmatrix}
  • b) row 11 of A−1AA^{-1}A is (1,0,0)(1, 0, 0)
  • c) y=(−3,−2,1)y = (-3, -2, 1), the first ROW of A−1A^{-1}
  • d) (3AT)−1=13(A−1)T=13(−36−4−23−21−11)(3A^T)^{-1} = \frac{1}{3}(A^{-1})^T = \frac{1}{3}\begin{pmatrix} -3 & 6 & -4 \\ -2 & 3 & -2 \\ 1 & -1 & 1 \end{pmatrix}
  • e) A=E1−1E2−1E3−1E4−1A = E_1^{-1}E_2^{-1}E_3^{-1}E_4^{-1} with E1−1:R2→R2−2R1E_1^{-1}: R_2 \to R_2 - 2R_1, E2−1:R3→R3+2R2E_2^{-1}: R_3 \to R_3 + 2R_2, E3−1:R2→R2+R3E_3^{-1}: R_2 \to R_2 + R_3, E4−1:R1→R1−R3E_4^{-1}: R_1 \to R_1 - R_3

a) Start from \left(\begin{array}{ccc|ccc} 1 & 0 & -1 & 1 & 0 & 0 \\ -2 & 1 & 3 & 0 & 1 & 0 \\ 0 & 2 & 3 & 0 & 0 & 1 \end{array}\right). R2→R2+2R1R_2 \to R_2 + 2R_1 gives (0,1,1∣2,1,0)(0, 1, 1 \mid 2, 1, 0). R3→R3−2R2R_3 \to R_3 - 2R_2 gives (0,0,1∣−4,−2,1)(0, 0, 1 \mid -4, -2, 1). The left block is now triangular with pivots 1,1,11, 1, 1. Upwards: R2→R2−R3R_2 \to R_2 - R_3 gives (0,1,0∣6,3,−1)(0, 1, 0 \mid 6, 3, -1) and R1→R1+R3R_1 \to R_1 + R_3 gives (1,0,0∣−3,−2,1)(1, 0, 0 \mid -3, -2, 1). So A−1=(−3−2163−1−4−21)A^{-1} = \begin{pmatrix} -3 & -2 & 1 \\ 6 & 3 & -1 \\ -4 & -2 & 1 \end{pmatrix}.

b) Row 11 of A−1A^{-1}, (−3,−2,1)(-3, -2, 1), against the columns of AA: −3+4+0=1-3 + 4 + 0 = 1, 0−2+2=00 - 2 + 2 = 0, 3−6+3=03 - 6 + 3 = 0. That is row 11 of II.

c) ATA^T is invertible with (AT)−1=(A−1)T(A^T)^{-1} = (A^{-1})^T, so y=(A−1)Te1y = (A^{-1})^Te_1, the first column of (A−1)T(A^{-1})^T, which is the first ROW of A−1A^{-1}: y=(−3,−2,1)y = (-3, -2, 1). Check: AT=(1−20012−133)A^T = \begin{pmatrix} 1 & -2 & 0 \\ 0 & 1 & 2 \\ -1 & 3 & 3 \end{pmatrix} and ATy=(−3+4,−2+2,3−6+3)=(1,0,0)A^Ty = (-3 + 4, -2 + 2, 3 - 6 + 3) = (1, 0, 0). Taking the first COLUMN of A−1A^{-1}, (−3,6,−4)(-3, 6, -4), solves Ax=e1Ax = e_1, a different system.

d) (3AT)−1=13(AT)−1=13(A−1)T=13(−36−4−23−21−11)(3A^T)^{-1} = \frac{1}{3}(A^T)^{-1} = \frac{1}{3}(A^{-1})^T = \frac{1}{3}\begin{pmatrix} -3 & 6 & -4 \\ -2 & 3 & -2 \\ 1 & -1 & 1 \end{pmatrix}. The scalar inverts to 13\frac{1}{3}, the transpose stays a transpose.

e) The four operations of a) are left multiplications by E1E_1 (R2→R2+2R1R_2 \to R_2 + 2R_1), E2E_2 (R3→R3−2R2R_3 \to R_3 - 2R_2), E3E_3 (R2→R2−R3R_2 \to R_2 - R_3) and E4E_4 (R1→R1+R3R_1 \to R_1 + R_3), and E4E3E2E1A=IE_4E_3E_2E_1A = I. Multiply on the left by the inverses, the last one first: A=E1−1E2−1E3−1E4−1A = E_1^{-1}E_2^{-1}E_3^{-1}E_4^{-1}, where each inverse undoes its operation, E1−1E_1^{-1} is R2→R2−2R1R_2 \to R_2 - 2R_1, E2−1E_2^{-1} is R3→R3+2R2R_3 \to R_3 + 2R_2, E3−1E_3^{-1} is R2→R2+R3R_2 \to R_2 + R_3 and E4−1E_4^{-1} is R1→R1−R3R_1 \to R_1 - R_3. As matrices, \begin{pmatrix} 1 & 0 & 0 \\ -2 & 1 & 0 \\ 0 & 0 & 1 \end{pmatrix}\begin{pmatrix} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 2 & 1 \end{pmatrix}\begin{pmatrix} 1 & 0 & 0 \\ 0 & 1 & 1 \\ 0 & 0 & 1 \end{pmatrix}\begin{pmatrix} 1 & 0 & -1 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{pmatrix} =A= A. The order is the trap: the operation performed FIRST has its inverse written FIRST, on the far left.

Exercise 3: Cramer's rule for two unknowns, and one entry of the inverse

Consider the system Nx=bNx = b with N=(12−1310201)N = \begin{pmatrix} 1 & 2 & -1 \\ 3 & 1 & 0 \\ 2 & 0 & 1 \end{pmatrix}, x=(x1,x2,x3)x = (x_1, x_2, x_3) and b=(3,7,5)b = (3, 7, 5).

  • a) Compute det⁡N\det N by expanding along the line of your choice, and justify the choice.
  • b) Find x3x_3 by Cramer's rule.
  • c) Find x2x_2 by Cramer's rule, then x1x_1 from one equation of the system, and check the solution.
  • d) Compute the entry in row 33, column 11 of N−1N^{-1} without computing N−1N^{-1}.
  • e) Give det⁡(adj N)\det(\text{adj}\,N) and det⁡(2N−1)\det(2N^{-1}).
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Answers

  • a) det⁡N=−3\det N = -3
  • b) x3=−3−3=1x_3 = \frac{-3}{-3} = 1
  • c) x2=1x_2 = 1, x1=2x_1 = 2
  • d) (N−1)31=C13det⁡N=−2−3=23(N^{-1})_{31} = \frac{C_{13}}{\det N} = \frac{-2}{-3} = \frac{2}{3}
  • e) det⁡(adj N)=9\det(\text{adj}\,N) = 9 and det⁡(2N−1)=−83\det(2N^{-1}) = -\frac{8}{3}

a) Row 33 holds a zero, and so do row 22 and column 22 and column 33. Along row 33: det⁡N=2C31+0⋅C32+1⋅C33\det N = 2C_{31} + 0 \cdot C_{32} + 1 \cdot C_{33} with C31=+det⁡(2−110)=1C_{31} = +\det\begin{pmatrix} 2 & -1 \\ 1 & 0 \end{pmatrix} = 1 and C33=+det⁡(1231)=−5C_{33} = +\det\begin{pmatrix} 1 & 2 \\ 3 & 1 \end{pmatrix} = -5. So det⁡N=2−5=−3\det N = 2 - 5 = -3. Both surviving positions, (3,1)(3,1) and (3,3)(3,3), carry the sign ++.

b) Replace column 33 by bb: N3=(123317205)N_3 = \begin{pmatrix} 1 & 2 & 3 \\ 3 & 1 & 7 \\ 2 & 0 & 5 \end{pmatrix}. Along row 33: 2det⁡(2317)+5det⁡(1231)=2(11)+5(−5)=−32\det\begin{pmatrix} 2 & 3 \\ 1 & 7 \end{pmatrix} + 5\det\begin{pmatrix} 1 & 2 \\ 3 & 1 \end{pmatrix} = 2(11) + 5(-5) = -3. So x3=−3−3=1x_3 = \frac{-3}{-3} = 1.

c) Replace column 22 by bb: N2=(13−1370251)N_2 = \begin{pmatrix} 1 & 3 & -1 \\ 3 & 7 & 0 \\ 2 & 5 & 1 \end{pmatrix}. Along column 33: (−1)C13+0+1⋅C33=−det⁡(3725)+det⁡(1337)=−1−2=−3(-1)C_{13} + 0 + 1 \cdot C_{33} = -\det\begin{pmatrix} 3 & 7 \\ 2 & 5 \end{pmatrix} + \det\begin{pmatrix} 1 & 3 \\ 3 & 7 \end{pmatrix} = -1 - 2 = -3, so x2=1x_2 = 1. The second equation, 3x1+x2=73x_1 + x_2 = 7, gives x1=2x_1 = 2 at once, cheaper than a third determinant. Check: 2+2−1=32 + 2 - 1 = 3, 6+1=76 + 1 = 7, 4+1=54 + 1 = 5.

d) N−1=1det⁡Nadj(N)N^{-1} = \frac{1}{\det N}\text{adj}(N) and adj(N)\text{adj}(N) is the TRANSPOSE of the cofactor matrix, so the entry in row 33, column 11 of N−1N^{-1} is C13det⁡N\frac{C_{13}}{\det N}, indices swapped. C13=+det⁡(3120)=−2C_{13} = +\det\begin{pmatrix} 3 & 1 \\ 2 & 0 \end{pmatrix} = -2, hence (N−1)31=−2−3=23(N^{-1})_{31} = \frac{-2}{-3} = \frac{2}{3}. Using C31=1C_{31} = 1 instead would give −13-\frac{1}{3}, the entry in row 11, column 33.

e) From N adj(N)=(det⁡N)IN\,\text{adj}(N) = (\det N)I and n=3n = 3: det⁡N⋅det⁡(adj N)=(det⁡N)3\det N \cdot \det(\text{adj}\,N) = (\det N)^3, so det⁡(adj N)=(det⁡N)2=9\det(\text{adj}\,N) = (\det N)^2 = 9. And det⁡(2N−1)=23⋅1det⁡N=−83\det(2N^{-1}) = 2^3 \cdot \frac{1}{\det N} = -\frac{8}{3}.

Part B: vectors, lines and planes (/22)

Exercise 4: Two lines that meet, and a parallel that does not

Let L1L_1 be the line through A(1,−1,2)A(1, -1, 2) and B(3,0,1)B(3, 0, 1), and L2:(x,y,z)=(3,−3,−6)+s(1,2,3)L_2 : (x, y, z) = (3, -3, -6) + s(1, 2, 3).

  • a) Write a vector equation of L1L_1. Is the point C(7,2,−1)C(7, 2, -1) on L1L_1?
  • b) Show that L1L_1 and L2L_2 intersect, and find the point of intersection.
  • c) Let L3L_3 be the line through the origin parallel to L1L_1. Give the relative position of L3L_3 and L2L_2.
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Answers

  • a) L1:(x,y,z)=(1,−1,2)+t(2,1,−1)L_1 : (x, y, z) = (1, -1, 2) + t(2, 1, -1); CC is on L1L_1 (t=3t = 3)
  • b) t=2t = 2, s=2s = 2: they meet at (5,1,0)(5, 1, 0)
  • c) L3L_3 and L2L_2 are skew

a) Direction AB→=(2,1,−1)\overrightarrow{AB} = (2, 1, -1), so L1:(x,y,z)=(1,−1,2)+t(2,1,−1)L_1 : (x, y, z) = (1, -1, 2) + t(2, 1, -1). For CC: the first coordinate gives 1+2t=71 + 2t = 7, so t=3t = 3, and then (1+6,−1+3,2−3)=(7,2,−1)(1 + 6, -1 + 3, 2 - 3) = (7, 2, -1). All three coordinates agree with ONE value of tt, so CC is on L1L_1. One coordinate alone proves nothing.

b) The directions (2,1,−1)(2, 1, -1) and (1,2,3)(1, 2, 3) are not proportional, so the lines meet or are skew. Use two DIFFERENT parameters: 1+2t=3+s1 + 2t = 3 + s, −1+t=−3+2s-1 + t = -3 + 2s, 2−t=−6+3s2 - t = -6 + 3s. The first gives s=2t−2s = 2t - 2; in the second, −1+t=−3+4t−4-1 + t = -3 + 4t - 4, so t=2t = 2 and s=2s = 2. The third equation is the test: 2−2=02 - 2 = 0 and −6+6=0-6 + 6 = 0. It holds, so the lines meet, at t=2t = 2 on L1L_1: (5,1,0)(5, 1, 0), which is also s=2s = 2 on L2L_2.

c) L3:(x,y,z)=u(2,1,−1)L_3 : (x, y, z) = u(2, 1, -1). Its direction is not proportional to (1,2,3)(1, 2, 3), so L3L_3 and L2L_2 are not parallel. Solve 2u=3+s2u = 3 + s, u=−3+2su = -3 + 2s, −u=−6+3s-u = -6 + 3s. The last two give −3+2s=6−3s-3 + 2s = 6 - 3s, so s=95s = \frac{9}{5} and u=35u = \frac{3}{5}. The first equation then reads 65=245\frac{6}{5} = \frac{24}{5}, false. No common point and not parallel: L3L_3 and L2L_2 are skew. Parallel to a line that meets L2L_2 does not mean meeting L2L_2: moving L1L_1 to the origin moved it out of the plane that L1L_1 and L2L_2 span.

Exercise 5: A point and a plane: distance, foot, mirror image and side

Let Π\Pi be the plane x+2y+2z=10x + 2y + 2z = 10 and Q=(3,5,3)Q = (3, 5, 3).

  • a) Compute the distance from QQ to Π\Pi by projecting a vector on the normal, starting from a point of Π\Pi that you choose.
  • b) Find the point FF of Π\Pi closest to QQ, and the mirror image Q′Q' of QQ across Π\Pi.
  • c) Are QQ and the origin on the same side of Π\Pi? If not, where does the segment from OO to QQ cross Π\Pi?
  • d) Find the cosine of the angle between Π\Pi and the horizontal plane z=0z = 0.
Show the solution

Answers

  • a) distance =93=3= \frac{9}{3} = 3
  • b) F=(2,3,1)F = (2, 3, 1), Q′=(1,1,−1)Q' = (1, 1, -1)
  • c) opposite sides; the segment crosses Π\Pi at 1019Q=(3019,5019,3019)\frac{10}{19}Q = (\frac{30}{19}, \frac{50}{19}, \frac{30}{19})
  • d) cos⁡θ=23\cos\theta = \frac{2}{3}

a) P0=(10,0,0)P_0 = (10, 0, 0) is on Π\Pi, and P0Q→=(−7,5,3)\overrightarrow{P_0Q} = (-7, 5, 3). With n=(1,2,2)n = (1, 2, 2) and ∥n∥=3\|n\| = 3, the scalar component of P0Q→\overrightarrow{P_0Q} along nn is P0Q→⋅n∥n∥=−7+10+63=3\frac{\overrightarrow{P_0Q} \cdot n}{\|n\|} = \frac{-7 + 10 + 6}{3} = 3. The distance is 33. The formula gives the same thing: ∣3+10+6−10∣3=3\frac{|3 + 10 + 6 - 10|}{3} = 3, and the choice of P0P_0 cannot matter, since two points of Π\Pi differ by a vector orthogonal to nn.

b) From QQ, walk back along the normal by the projection of P0Q→\overrightarrow{P_0Q} on nn, which is 99n=n\frac{9}{9}n = n: F=Q−n=(2,3,1)F = Q - n = (2, 3, 1). Check: 2+6+2=102 + 6 + 2 = 10, so FF is on Π\Pi. The mirror image is as far beyond FF as QQ is before it: Q′=2F−Q=(1,1,−1)Q' = 2F - Q = (1, 1, -1). Check: 1+2−2=11 + 2 - 2 = 1, so its distance is ∣1−10∣3=3\frac{|1 - 10|}{3} = 3, on the other side. The corrected figure shows the cross-section along the normal.

c) Evaluate x+2y+2z−10x + 2y + 2z - 10: at QQ it is 19−10=9>019 - 10 = 9 > 0, at OO it is −10<0-10 < 0. The signs differ: opposite sides. The segment is sQsQ for 0≤s≤10 \le s \le 1, and it meets Π\Pi when s(3+10+6)=10s(3 + 10 + 6) = 10, so s=1019s = \frac{10}{19} and the crossing point is (3019,5019,3019)(\frac{30}{19}, \frac{50}{19}, \frac{30}{19}).

d) The angle between two planes is the angle between their normals: cos⁡θ=(1,2,2)⋅(0,0,1)3×1=23\cos\theta = \frac{(1, 2, 2) \cdot (0, 0, 1)}{3 \times 1} = \frac{2}{3}, an acute angle, so no absolute value is needed.

Q(3, 5, 3)F(2, 3, 1)Q'(1, 1, -1)n = (1, 2, 2)plane, seen edge on33

Exercise 6: A plane through three points, a triangle and a tetrahedron

The points are A(1,0,1)A(1, 0, 1), B(2,2,0)B(2, 2, 0), C(0,1,3)C(0, 1, 3) and D(3,1,4)D(3, 1, 4), sketched on the figure.

A(1, 0, 1)B(2, 2, 0)C(0, 1, 3)D(3, 1, 4)sketch, not to scale
  • a) Find a normal vector to the plane ABCABC and its equation in the form ax+by+cz=dax + by + cz = d. Check it with the three points.
  • b) Compute the area of triangle ABCABC.
  • c) Compute the volume of the tetrahedron ABCDABCD.
  • d) Deduce the distance from DD to the plane ABCABC, and check it with the distance formula.
Show the solution

Answers

  • a) n=(5,−1,3)n = (5, -1, 3), plane 5x−y+3z=85x - y + 3z = 8
  • b) 352\frac{\sqrt{35}}{2}
  • c) 186=3\frac{18}{6} = 3
  • d) 1835\frac{18}{\sqrt{35}}

a) Edges from the same vertex: AB→=(1,2,−1)\overrightarrow{AB} = (1, 2, -1) and AC→=(−1,1,2)\overrightarrow{AC} = (-1, 1, 2). Their cross product is n=(2⋅2−(−1)⋅1, (−1)(−1)−1⋅2, 1⋅1−2(−1))=(5,−1,3)n = (2 \cdot 2 - (-1) \cdot 1,\ (-1)(-1) - 1 \cdot 2,\ 1 \cdot 1 - 2(-1)) = (5, -1, 3). Check: n⋅AB→=5−2−3=0n \cdot \overrightarrow{AB} = 5 - 2 - 3 = 0 and n⋅AC→=−5−1+6=0n \cdot \overrightarrow{AC} = -5 - 1 + 6 = 0. The plane is 5x−y+3z=d5x - y + 3z = d with d=5(1)−0+3(1)=8d = 5(1) - 0 + 3(1) = 8. Check: BB gives 10−2+0=810 - 2 + 0 = 8, CC gives 0−1+9=80 - 1 + 9 = 8.

b) Area =12∥AB→×AC→∥=1225+1+9=352= \frac{1}{2}\|\overrightarrow{AB} \times \overrightarrow{AC}\| = \frac{1}{2}\sqrt{25 + 1 + 9} = \frac{\sqrt{35}}{2}. The cross product was already there: the normal of a) gives the area for free.

c) AD→=(2,1,3)\overrightarrow{AD} = (2, 1, 3) and the triple product is AD→⋅(AB→×AC→)=10−1+9=18\overrightarrow{AD} \cdot (\overrightarrow{AB} \times \overrightarrow{AC}) = 10 - 1 + 9 = 18. The parallelepiped on the three edges has volume 1818, and the tetrahedron is one sixth of it: 33. One sixth, not one third: a tetrahedron is one third of a PRISM on the triangle, and the triangle is half the parallelogram.

d) Volume =13(base area)(height)= \frac{1}{3}(\text{base area})(\text{height}), so h=3×335/2=1835h = \frac{3 \times 3}{\sqrt{35}/2} = \frac{18}{\sqrt{35}}. Check with the formula: ∣5(3)−1+3(4)−8∣35=1835\frac{|5(3) - 1 + 3(4) - 8|}{\sqrt{35}} = \frac{18}{\sqrt{35}}. The two agree, and the same 1818 appears in both, which is no accident: it is AD→⋅n\overrightarrow{AD} \cdot n.

Part C: subspaces, bases and linear maps (/22)

Exercise 7: Two sets to test, and a family with a parameter

Let U={(a+b, a−b, 2b, a):a,b∈R}U = \{(a + b,\ a - b,\ 2b,\ a) : a, b \in \mathbb{R}\} in R4\mathbb{R}^4 and W={(x,y,z):x≤y}W = \{(x, y, z) : x \le y\} in R3\mathbb{R}^3. In R4\mathbb{R}^4, let v1=(1,1,0,1)v_1 = (1, 1, 0, 1), v2=(0,1,1,2)v_2 = (0, 1, 1, 2) and v3=(1,3,k,5)v_3 = (1, 3, k, 5).

  • a) Show that UU is a subspace of R4\mathbb{R}^4 and give a basis and its dimension.
  • b) Is WW a subspace of R3\mathbb{R}^3?
  • c) Find every kk for which {v1,v2,v3}\{v_1, v_2, v_3\} is linearly dependent, and give the dependency relation.
  • d) Is w=(2,3,1,4)w = (2, 3, 1, 4) in span{v1,v2}\text{span}\{v_1, v_2\}? Give dim⁡span{v1,v2,v3}\dim \text{span}\{v_1, v_2, v_3\} for every kk.
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Answers

  • a) U=span{(1,1,0,1),(1,−1,2,0)}U = \text{span}\{(1, 1, 0, 1), (1, -1, 2, 0)\}, basis of 22 vectors, dim⁡U=2\dim U = 2
  • b) no: (0,1,0)∈W(0, 1, 0) \in W but −(0,1,0)∉W-(0, 1, 0) \notin W
  • c) dependent exactly when k=2k = 2, and then v3=v1+2v2v_3 = v_1 + 2v_2
  • d) yes, w=2v1+v2w = 2v_1 + v_2; the dimension is 22 for k=2k = 2 and 33 otherwise

a) Split by parameter: (a+b,a−b,2b,a)=a(1,1,0,1)+b(1,−1,2,0)(a + b, a - b, 2b, a) = a(1, 1, 0, 1) + b(1, -1, 2, 0). So U=span{(1,1,0,1),(1,−1,2,0)}U = \text{span}\{(1, 1, 0, 1), (1, -1, 2, 0)\}, and a span is always a subspace: it contains 00 (a=b=0a = b = 0), and sums and multiples of combinations are combinations. The two spanning vectors are not multiples of each other (the third coordinates are 00 and 22 while the fourth are 11 and 00), so they are independent: they form a basis and dim⁡U=2\dim U = 2.

b) No. WW contains 00 and is closed under addition, since x1≤y1x_1 \le y_1 and x2≤y2x_2 \le y_2 give x1+x2≤y1+y2x_1 + x_2 \le y_1 + y_2. But (0,1,0)∈W(0, 1, 0) \in W while (−1)(0,1,0)=(0,−1,0)(-1)(0, 1, 0) = (0, -1, 0) is not, since 0≤−10 \le -1 is false. One failing condition is enough, and it must be shown with numbers.

c) Put the vectors as columns and reduce: rows (1,0,1)(1, 0, 1), (1,1,3)(1, 1, 3), (0,1,k)(0, 1, k), (1,2,5)(1, 2, 5). R2→R2−R1R_2 \to R_2 - R_1 gives (0,1,2)(0, 1, 2), R4→R4−R1R_4 \to R_4 - R_1 gives (0,2,4)(0, 2, 4); then R3→R3−R2R_3 \to R_3 - R_2 gives (0,0,k−2)(0, 0, k - 2) and R4→R4−2R2R_4 \to R_4 - 2R_2 gives a zero row. There are three pivots, hence independence, exactly when k≠2k \neq 2. For k=2k = 2 the third column is free, and the reduced form reads c1=−c3c_1 = -c_3 and c2=−2c3c_2 = -2c_3: with c3=−1c_3 = -1, v1+2v2−v3=0v_1 + 2v_2 - v_3 = 0, that is v3=v1+2v2v_3 = v_1 + 2v_2. Check: v1+2v2=(1,1,0,1)+(0,2,2,4)=(1,3,2,5)v_1 + 2v_2 = (1, 1, 0, 1) + (0, 2, 2, 4) = (1, 3, 2, 5), which is v3v_3 for k=2k = 2.

d) 2v1+v2=(2,2,0,2)+(0,1,1,2)=(2,3,1,4)=w2v_1 + v_2 = (2, 2, 0, 2) + (0, 1, 1, 2) = (2, 3, 1, 4) = w: yes. The span of the three vectors has dimension 33 when they are independent, k≠2k \neq 2, and 22 when k=2k = 2, since v3v_3 then adds nothing to span{v1,v2}\text{span}\{v_1, v_2\}.

Exercise 8: One matrix, four subspaces

Let A=(120132414712134)A = \begin{pmatrix} 1 & 2 & 0 & 1 & 3 \\ 2 & 4 & 1 & 4 & 7 \\ 1 & 2 & 1 & 3 & 4 \end{pmatrix}.

  • a) Reduce AA to reduced row echelon form, naming every operation.
  • b) Give a basis and the dimension of null(A)\text{null}(A), col(A)\text{col}(A) and row(A)\text{row}(A), and check the rank theorem.
  • c) Give dim⁡null(AT)\dim \text{null}(A^T) and a non-zero vector of null(AT)\text{null}(A^T), read from a relation between the rows of AA.
  • d) Deduce an equation of col(A)\text{col}(A). Are (1,1,1)(1, 1, 1) and (2,5,3)(2, 5, 3) in col(A)\text{col}(A)?
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Answers

  • a) R=(120130012100000)R = \begin{pmatrix} 1 & 2 & 0 & 1 & 3 \\ 0 & 0 & 1 & 2 & 1 \\ 0 & 0 & 0 & 0 & 0 \end{pmatrix}
  • b) null\text{null}: (−2,1,0,0,0)(-2, 1, 0, 0, 0), (−1,0,−2,1,0)(-1, 0, -2, 1, 0), (−3,0,−1,0,1)(-3, 0, -1, 0, 1), dim 33; col\text{col}: (1,2,1)(1, 2, 1), (0,1,1)(0, 1, 1), dim 22; row\text{row}: (1,2,0,1,3)(1, 2, 0, 1, 3), (0,0,1,2,1)(0, 0, 1, 2, 1), dim 22; 2+3=52 + 3 = 5
  • c) dim⁡null(AT)=1\dim \text{null}(A^T) = 1, spanned by (1,−1,1)(1, -1, 1)
  • d) col(A)\text{col}(A) is the plane y1−y2+y3=0y_1 - y_2 + y_3 = 0: (1,1,1)(1, 1, 1) no, (2,5,3)(2, 5, 3) yes

a) R2→R2−2R1R_2 \to R_2 - 2R_1 gives (0,0,1,2,1)(0, 0, 1, 2, 1) and R3→R3−R1R_3 \to R_3 - R_1 gives (0,0,1,2,1)(0, 0, 1, 2, 1) as well; R3→R3−R2R_3 \to R_3 - R_2 gives a zero row. The matrix \begin{pmatrix} 1 & 2 & 0 & 1 & 3 \\ 0 & 0 & 1 & 2 & 1 \\ 0 & 0 & 0 & 0 & 0 \end{pmatrix} is reduced: pivots in columns 11 and 33, with a 00 above the second pivot.

b) Free variables x2=sx_2 = s, x4=tx_4 = t, x5=ux_5 = u; the rows give x3=−2t−ux_3 = -2t - u and x1=−2s−t−3ux_1 = -2s - t - 3u. So null(A)\text{null}(A) has basis (−2,1,0,0,0)(-2, 1, 0, 0, 0), (−1,0,−2,1,0)(-1, 0, -2, 1, 0), (−3,0,−1,0,1)(-3, 0, -1, 0, 1), dimension 33. col(A)\text{col}(A) takes the columns of the ORIGINAL AA in the pivot positions 11 and 33: (1,2,1)(1, 2, 1) and (0,1,1)(0, 1, 1), dimension 22. The pivot columns of RR, (1,0,0)(1, 0, 0) and (0,1,0)(0, 1, 0), would be wrong: row operations change the column space. row(A)\text{row}(A) takes the non-zero rows of RR: (1,2,0,1,3)(1, 2, 0, 1, 3) and (0,0,1,2,1)(0, 0, 1, 2, 1), dimension 22. Rank theorem: 2+3=52 + 3 = 5, the number of columns.

c) dim⁡null(AT)=3−2=1\dim \text{null}(A^T) = 3 - 2 = 1, the number of rows minus the rank. The reduction showed that row 33 minus row 11 equals row 22 minus 22 row 11, that is R1−R2+R3=0R_1 - R_2 + R_3 = 0. Check: (1−2+1,2−4+2,0−1+1,1−4+3,3−7+4)=0(1 - 2 + 1, 2 - 4 + 2, 0 - 1 + 1, 1 - 4 + 3, 3 - 7 + 4) = 0. So (1,−1,1)TA=0(1, -1, 1)^TA = 0, and (1,−1,1)(1, -1, 1) spans null(AT)\text{null}(A^T).

d) Every column cc of AA satisfies (1,−1,1)⋅c=0(1, -1, 1) \cdot c = 0, since that is exactly the relation R1−R2+R3=0R_1 - R_2 + R_3 = 0 read column by column. So col(A)\text{col}(A) lies in the plane y1−y2+y3=0y_1 - y_2 + y_3 = 0, and since both have dimension 22, they are equal. (1,1,1)(1, 1, 1) gives 1≠01 \neq 0: not in col(A)\text{col}(A). (2,5,3)(2, 5, 3) gives 00: it is, and indeed 2(1,2,1)+(0,1,1)=(2,5,3)2(1, 2, 1) + (0, 1, 1) = (2, 5, 3).

Exercise 9: A linear map known on three vectors, its kernel and a right inverse

T:R3→R2T : \mathbb{R}^3 \to \mathbb{R}^2 is linear, with T(1,0,1)=(1,2)T(1, 0, 1) = (1, 2), T(0,1,1)=(0,1)T(0, 1, 1) = (0, 1) and T(1,1,1)=(2,3)T(1, 1, 1) = (2, 3).

  • a) Find T(e1)T(e_1), T(e2)T(e_2), T(e3)T(e_3) and the standard matrix AA of TT.
  • b) Find a basis of ker⁡T\ker T. Is TT one-to-one? Is it onto? Give every xx with T(x)=(3,4)T(x) = (3, 4).
  • c) Find a linear map U:R2→R3U : \mathbb{R}^2 \to \mathbb{R}^3 with T∘U=IT \circ U = I, by giving its matrix. Is it the only one?
  • d) Explain why no linear map V:R2→R3V : \mathbb{R}^2 \to \mathbb{R}^3 can satisfy V∘T=IV \circ T = I.
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Answers

  • a) T(e1)=(2,2)T(e_1) = (2, 2), T(e2)=(1,1)T(e_2) = (1, 1), T(e3)=(−1,0)T(e_3) = (-1, 0), A=(21−1210)A = \begin{pmatrix} 2 & 1 & -1 \\ 2 & 1 & 0 \end{pmatrix}
  • b) ker⁡T=span{(1,−2,0)}\ker T = \text{span}\{(1, -2, 0)\}: not one-to-one, onto; x=(2,0,1)+s(1,−2,0)x = (2, 0, 1) + s(1, -2, 0)
  • c) U=(0001−11)U = \begin{pmatrix} 0 & 0 \\ 0 & 1 \\ -1 & 1 \end{pmatrix}; not unique, any kernel vector may be added to each column
  • d) V∘TV \circ T has rank at most 22, and I3I_3 has rank 33

a) Write each standard vector with the three given ones: e1=(1,1,1)−(0,1,1)e_1 = (1, 1, 1) - (0, 1, 1), e2=(1,1,1)−(1,0,1)e_2 = (1, 1, 1) - (1, 0, 1) and e3=(1,0,1)+(0,1,1)−(1,1,1)e_3 = (1, 0, 1) + (0, 1, 1) - (1, 1, 1). By linearity T(e1)=(2,3)−(0,1)=(2,2)T(e_1) = (2, 3) - (0, 1) = (2, 2), T(e2)=(2,3)−(1,2)=(1,1)T(e_2) = (2, 3) - (1, 2) = (1, 1) and T(e3)=(1,2)+(0,1)−(2,3)=(−1,0)T(e_3) = (1, 2) + (0, 1) - (2, 3) = (-1, 0). These are the COLUMNS of A=(21−1210)A = \begin{pmatrix} 2 & 1 & -1 \\ 2 & 1 & 0 \end{pmatrix}. Check on a given vector: A(1,0,1)=(2−1,2+0)=(1,2)A(1, 0, 1) = (2 - 1, 2 + 0) = (1, 2).

b) R2→R2−R1R_2 \to R_2 - R_1 gives (0,0,1)(0, 0, 1), so Ax=0Ax = 0 means x3=0x_3 = 0 and 2x1+x2=02x_1 + x_2 = 0: ker⁡T=span{(1,−2,0)}\ker T = \text{span}\{(1, -2, 0)\}. A non-zero kernel means TT is not one-to-one. The rank is 2=dim⁡R22 = \dim \mathbb{R}^2, so every bb is reached: TT is onto, and 2+1=32 + 1 = 3 is the rank theorem. For (3,4)(3, 4): 2x1+x2−x3=32x_1 + x_2 - x_3 = 3 and 2x1+x2=42x_1 + x_2 = 4 give x3=1x_3 = 1 and x2=4−2x1x_2 = 4 - 2x_1, so x=(2,0,1)+s(1,−2,0)x = (2, 0, 1) + s(1, -2, 0), a particular solution plus the kernel.

c) T∘U=IT \circ U = I asks AU=I2AU = I_2, so the columns u1u_1, u2u_2 of UU must satisfy Au1=e1Au_1 = e_1 and Au2=e2Au_2 = e_2. For e1e_1: 2x1+x2−x3=12x_1 + x_2 - x_3 = 1 and 2x1+x2=02x_1 + x_2 = 0 give x3=−1x_3 = -1; take u1=(0,0,−1)u_1 = (0, 0, -1). For e2e_2: 2x1+x2=12x_1 + x_2 = 1 and x3=2x1+x2=1x_3 = 2x_1 + x_2 = 1; take u2=(0,1,1)u_2 = (0, 1, 1). So U=(0001−11)U = \begin{pmatrix} 0 & 0 \\ 0 & 1 \\ -1 & 1 \end{pmatrix}, and AU=(1001)AU = \begin{pmatrix} 1 & 0 \\ 0 & 1 \end{pmatrix}. It is not unique: adding (1,−2,0)(1, -2, 0), or any kernel vector, to either column keeps AU=IAU = I.

d) V∘TV \circ T has matrix BABA for some 3×23 \times 2 matrix BB, and rank(BA)≤rank(A)=2\text{rank}(BA) \le \text{rank}(A) = 2. The identity of R3\mathbb{R}^3 has rank 33. Put differently: TT sends (1,−2,0)(1, -2, 0) and 00 to the same point, and no map applied afterwards can separate them again. A one-sided inverse exists on the side of the smaller space only.

Part D: eigenvalues and diagonalization (/23)

Exercise 10: A 3 by 3 whose polynomial arrives factored

Let A=(1200302−42)A = \begin{pmatrix} 1 & 2 & 0 \\ 0 & 3 & 0 \\ 2 & -4 & 2 \end{pmatrix}, and use the convention cA(x)=det⁡(xI−A)c_A(x) = \det(xI - A).

  • a) Compute cA(x)c_A(x) by cofactor expansion along a line of xI−AxI - A that you choose, and give the eigenvalues.
  • b) Find a basis of each eigenspace, and check each basic eigenvector by one multiplication.
  • c) Check the eigenvalues against the trace and the determinant of AA.
  • d) Without any new determinant: is AA invertible? Give the eigenvalues of A−1A^{-1} and of A2−2AA^2 - 2A, and say whether A2−2AA^2 - 2A is invertible.
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Answers

  • a) cA(x)=(x−1)(x−2)(x−3)c_A(x) = (x - 1)(x - 2)(x - 3): eigenvalues 11, 22, 33
  • b) E1=span{(1,0,−2)}E_1 = \text{span}\{(1, 0, -2)\}, E2=span{(0,0,1)}E_2 = \text{span}\{(0, 0, 1)\}, E3=span{(1,1,−2)}E_3 = \text{span}\{(1, 1, -2)\}
  • c) tr A=6=1+2+3\text{tr}\,A = 6 = 1 + 2 + 3 and det⁡A=6=1⋅2⋅3\det A = 6 = 1 \cdot 2 \cdot 3
  • d) invertible; A−1A^{-1}: 1,12,131, \frac{1}{2}, \frac{1}{3}; A2−2AA^2 - 2A: −1,0,3-1, 0, 3, so it is NOT invertible

a) xI−A=(x−1−200x−30−24x−2)xI - A = \begin{pmatrix} x - 1 & -2 & 0 \\ 0 & x - 3 & 0 \\ -2 & 4 & x - 2 \end{pmatrix}. Column 33 has two zeros, and so does row 22. Along column 33, only the (3,3)(3,3) entry survives, with sign ++: cA(x)=(x−2)det⁡(x−1−20x−3)=(x−2)(x−1)(x−3)c_A(x) = (x - 2)\det\begin{pmatrix} x - 1 & -2 \\ 0 & x - 3 \end{pmatrix} = (x - 2)(x - 1)(x - 3). The polynomial comes out FACTORED, which is the reason for the choice: expanding along row 11 would give a cubic to factor by trial. The eigenvalues are 11, 22 and 33.

b) λ=1\lambda = 1: I−A=(0−200−20−24−1)I - A = \begin{pmatrix} 0 & -2 & 0 \\ 0 & -2 & 0 \\ -2 & 4 & -1 \end{pmatrix}. The first row gives y=0y = 0 (R2→R2−R1R_2 \to R_2 - R_1 clears the second), and then the third gives −2x−z=0-2x - z = 0: basic eigenvector (1,0,−2)(1, 0, -2). λ=2\lambda = 2: 2I−A=(1−200−10−240)2I - A = \begin{pmatrix} 1 & -2 & 0 \\ 0 & -1 & 0 \\ -2 & 4 & 0 \end{pmatrix}, so y=0y = 0 and then x=0x = 0, with zz free: (0,0,1)(0, 0, 1). λ=3\lambda = 3: 3I−A=(2−20000−241)3I - A = \begin{pmatrix} 2 & -2 & 0 \\ 0 & 0 & 0 \\ -2 & 4 & 1 \end{pmatrix}, so x=yx = y, and −2x+4x+z=0-2x + 4x + z = 0 gives z=−2xz = -2x: (1,1,−2)(1, 1, -2). Checks: A(1,0,−2)=(1,0,2−4)=1⋅(1,0,−2)A(1, 0, -2) = (1, 0, 2 - 4) = 1 \cdot (1, 0, -2), A(0,0,1)=(0,0,2)=2(0,0,1)A(0, 0, 1) = (0, 0, 2) = 2(0, 0, 1), A(1,1,−2)=(3,3,−6)=3(1,1,−2)A(1, 1, -2) = (3, 3, -6) = 3(1, 1, -2).

c) tr A=1+3+2=6=1+2+3\text{tr}\,A = 1 + 3 + 2 = 6 = 1 + 2 + 3. det⁡A\det A along column 33: 2det⁡(1203)=6=1⋅2⋅32\det\begin{pmatrix} 1 & 2 \\ 0 & 3 \end{pmatrix} = 6 = 1 \cdot 2 \cdot 3. Both agree.

d) 00 is not an eigenvalue, so AA is invertible. From Ax=λxAx = \lambda x: A−1x=1λxA^{-1}x = \frac{1}{\lambda}x, giving 11, 12\frac{1}{2}, 13\frac{1}{3} with the same eigenvectors; and (A2−2A)x=(λ2−2λ)x(A^2 - 2A)x = (\lambda^2 - 2\lambda)x, giving −1-1, 00, 33. Since 00 is among them, A2−2AA^2 - 2A is NOT invertible, and (0,0,1)(0, 0, 1) is in its null space. Three distinct eigenvalues also make AA diagonalizable, with no rank test needed.

Exercise 11: A repeated eigenvalue that passes the test, and a power without P inverse

Let A=(211121001)A = \begin{pmatrix} 2 & 1 & 1 \\ 1 & 2 & 1 \\ 0 & 0 & 1 \end{pmatrix}.

  • a) Compute cA(x)c_A(x), factored, and give the eigenvalues with their algebraic multiplicities.
  • b) Find a basis of each eigenspace, and decide whether AA is diagonalizable.
  • c) Give an invertible PP and a diagonal DD with P−1AP=DP^{-1}AP = D, and check the answer without computing P−1P^{-1}.
  • d) Let N=A−IN = A - I. Show that N2=2NN^2 = 2N, and deduce that An=I+3n−12NA^n = I + \frac{3^n - 1}{2}N for every n≥1n \ge 1. Check the formula at n=2n = 2.
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Answers

  • a) cA(x)=(x−1)2(x−3)c_A(x) = (x - 1)^2(x - 3): 11 (double) and 33 (simple)
  • b) E1=span{(−1,1,0),(−1,0,1)}E_1 = \text{span}\{(-1, 1, 0), (-1, 0, 1)\}, E3=span{(1,1,0)}E_3 = \text{span}\{(1, 1, 0)\}: diagonalizable
  • c) P=(−1−11101010)P = \begin{pmatrix} -1 & -1 & 1 \\ 1 & 0 & 1 \\ 0 & 1 & 0 \end{pmatrix}, D=(100010003)D = \begin{pmatrix} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 3 \end{pmatrix}
  • d) An=I+3n−12(111111000)A^n = I + \frac{3^n - 1}{2}\begin{pmatrix} 1 & 1 & 1 \\ 1 & 1 & 1 \\ 0 & 0 & 0 \end{pmatrix}; A2=(544454001)A^2 = \begin{pmatrix} 5 & 4 & 4 \\ 4 & 5 & 4 \\ 0 & 0 & 1 \end{pmatrix}

a) Row 33 of xI−A=(x−2−1−1−1x−2−100x−1)xI - A = \begin{pmatrix} x - 2 & -1 & -1 \\ -1 & x - 2 & -1 \\ 0 & 0 & x - 1 \end{pmatrix} has two zeros. Along it, with sign ++ at (3,3)(3,3): cA(x)=(x−1)[(x−2)2−1]=(x−1)(x−1)(x−3)=(x−1)2(x−3)c_A(x) = (x - 1)\left[(x - 2)^2 - 1\right] = (x - 1)(x - 1)(x - 3) = (x - 1)^2(x - 3). The factor (x−2)2−1(x - 2)^2 - 1 is a difference of squares, (x−3)(x−1)(x - 3)(x - 1): no quadratic formula needed. Eigenvalues: 11 with algebraic multiplicity 22, and 33.

b) λ=1\lambda = 1: I−A=(−1−1−1−1−1−1000)I - A = \begin{pmatrix} -1 & -1 & -1 \\ -1 & -1 & -1 \\ 0 & 0 & 0 \end{pmatrix}; R2→R2−R1R_2 \to R_2 - R_1 leaves the single equation x+y+z=0x + y + z = 0, rank 11, so dim⁡E1=3−1=2\dim E_1 = 3 - 1 = 2, with basis (−1,1,0)(-1, 1, 0) and (−1,0,1)(-1, 0, 1). λ=3\lambda = 3: 3I−A=(1−1−1−11−1002)3I - A = \begin{pmatrix} 1 & -1 & -1 \\ -1 & 1 & -1 \\ 0 & 0 & 2 \end{pmatrix} gives z=0z = 0 and x=yx = y: basis (1,1,0)(1, 1, 0). Each eigenspace has the dimension of its algebraic multiplicity, so AA has three independent eigenvectors and IS diagonalizable. The simple eigenvalue never needs the test; the double one is the only place the count could fall short, and it does not.

c) P=(−1−11101010)P = \begin{pmatrix} -1 & -1 & 1 \\ 1 & 0 & 1 \\ 0 & 1 & 0 \end{pmatrix}, the eigenvectors as COLUMNS, and D=(100010003)D = \begin{pmatrix} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 3 \end{pmatrix} in the same order. PP is invertible: along row 33, det⁡P=−det⁡(−1111)=2\det P = -\det\begin{pmatrix} -1 & 1 \\ 1 & 1 \end{pmatrix} = 2. Check AP=PDAP = PD column by column, which is the statement Av=λvAv = \lambda v for each column: A(−1,1,0)=(−1,1,0)A(-1, 1, 0) = (-1, 1, 0), A(−1,0,1)=(−1,0,1)A(-1, 0, 1) = (-1, 0, 1), A(1,1,0)=(3,3,0)A(1, 1, 0) = (3, 3, 0).

d) N=(111111000)N = \begin{pmatrix} 1 & 1 & 1 \\ 1 & 1 & 1 \\ 0 & 0 & 0 \end{pmatrix} and N2=(222222000)=2NN^2 = \begin{pmatrix} 2 & 2 & 2 \\ 2 & 2 & 2 \\ 0 & 0 & 0 \end{pmatrix} = 2N. By induction Nk=2k−1NN^k = 2^{k-1}N for k≥1k \ge 1. Since II and NN commute, the binomial expansion is legal: An=(I+N)n=I+∑k=1n(nk)2k−1N=I+12[(1+2)n−1]N=I+3n−12NA^n = (I + N)^n = I + \sum_{k=1}^{n}\binom{n}{k}2^{k-1}N = I + \frac{1}{2}\left[(1 + 2)^n - 1\right]N = I + \frac{3^n - 1}{2}N. At n=2n = 2: I+4N=(544454001)I + 4N = \begin{pmatrix} 5 & 4 & 4 \\ 4 & 5 & 4 \\ 0 & 0 & 1 \end{pmatrix}, and A2A^2 computed directly gives the same matrix (row 11 of AA against its columns: 4+1+0=54 + 1 + 0 = 5, 2+2+0=42 + 2 + 0 = 4, 2+1+1=42 + 1 + 1 = 4).

The formula is diagonalization in disguise: on E1E_1, NN acts as 00 and An=IA^n = I; on E3E_3, NN acts as 22 and AnA^n multiplies by 1+(3n−1)=3n1 + (3^n - 1) = 3^n. Two eigenvalues, so a polynomial of degree one in AA is enough, and P−1P^{-1} never had to be computed.

Exercise 12: Two lakes joined by a channel, and the fishing quota

Two lakes, AA and BB, are joined by a channel. Let aka_k and bkb_k be their trout populations in year kk, in hundreds. Each year reproduction, mortality and migration through the channel combine into ak+1=0.8ak+0.4bka_{k+1} = 0.8a_k + 0.4b_k and bk+1=0.4ak+0.8bkb_{k+1} = 0.4a_k + 0.8b_k, so xk+1=Mxkx_{k+1} = Mx_k with xk=(ak,bk)x_k = (a_k, b_k) and M=(0.80.40.40.8)M = \begin{pmatrix} 0.8 & 0.4 \\ 0.4 & 0.8 \end{pmatrix}. At the start, x0=(5,1)x_0 = (5, 1): 500500 trout in lake AA and 100100 in lake BB. The figure plots the first five years.

1234567812345678year 0year 1year 2year 3year 4lake A (hundreds)lake B (hundreds)
  • a) Diagonalize MM.
  • b) Write x0x_0 as a combination of the eigenvectors and give a closed formula for xkx_k. Check it against year 11 on the figure.
  • c) Describe the long run: the yearly growth rate and the eventual ratio of the two populations. From which year on does lake BB hold at least 9090 per cent of the population of lake AA?
  • d) A fishing quota removes, after the yearly update, the same fraction hh of each lake. For which hh do the populations neither grow nor die out in the long run, and where do they settle?
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Answers

  • a) P=(111−1)P = \begin{pmatrix} 1 & 1 \\ 1 & -1 \end{pmatrix}, D=(1.2000.4)D = \begin{pmatrix} 1.2 & 0 \\ 0 & 0.4 \end{pmatrix}
  • b) x0=3(1,1)+2(1,−1)x_0 = 3(1, 1) + 2(1, -1) and xk=3(1.2)k(1,1)+2(0.4)k(1,−1)x_k = 3(1.2)^k(1, 1) + 2(0.4)^k(1, -1); x1=(4.4,2.8)x_1 = (4.4, 2.8)
  • c) growth 2020 per cent a year, ratio tending to 11; bkak=3⋅3k−23⋅3k+2\frac{b_k}{a_k} = \frac{3 \cdot 3^k - 2}{3 \cdot 3^k + 2} reaches 0.90.9 from year 33 on
  • d) h=0.2h = 0.2; the populations settle at (3,3)(3, 3), 300300 trout in each lake

a) cM(x)=(x−0.8)2−0.16=(x−0.8−0.4)(x−0.8+0.4)=(x−1.2)(x−0.4)c_M(x) = (x - 0.8)^2 - 0.16 = (x - 0.8 - 0.4)(x - 0.8 + 0.4) = (x - 1.2)(x - 0.4), a difference of squares again. For 1.21.2: 1.2I−M=(0.4−0.4−0.40.4)1.2I - M = \begin{pmatrix} 0.4 & -0.4 \\ -0.4 & 0.4 \end{pmatrix}, so a=ba = b and (1,1)(1, 1). For 0.40.4: 0.4I−M=(−0.4−0.4−0.4−0.4)0.4I - M = \begin{pmatrix} -0.4 & -0.4 \\ -0.4 & -0.4 \end{pmatrix}, so a=−ba = -b and (1,−1)(1, -1). Hence P=(111−1)P = \begin{pmatrix} 1 & 1 \\ 1 & -1 \end{pmatrix} and D=(1.2000.4)D = \begin{pmatrix} 1.2 & 0 \\ 0 & 0.4 \end{pmatrix} with P−1MP=DP^{-1}MP = D. Check: M(1,1)=(1.2,1.2)M(1, 1) = (1.2, 1.2) and M(1,−1)=(0.4,−0.4)M(1, -1) = (0.4, -0.4).

b) (5,1)=c1(1,1)+c2(1,−1)(5, 1) = c_1(1, 1) + c_2(1, -1) gives c1+c2=5c_1 + c_2 = 5 and c1−c2=1c_1 - c_2 = 1, so c1=3c_1 = 3 and c2=2c_2 = 2. Each eigenvector is simply multiplied by its eigenvalue every year, so xk=3(1.2)k(1,1)+2(0.4)k(1,−1)x_k = 3(1.2)^k(1, 1) + 2(0.4)^k(1, -1). Year 11: 3(1.2)(1,1)+2(0.4)(1,−1)=(3.6+0.8,3.6−0.8)=(4.4,2.8)3(1.2)(1, 1) + 2(0.4)(1, -1) = (3.6 + 0.8, 3.6 - 0.8) = (4.4, 2.8), the point of the figure. Year 22 gives (4.32+0.32,4.32−0.32)=(4.64,4)(4.32 + 0.32, 4.32 - 0.32) = (4.64, 4), also on the figure.

c) Factor out the dominant term: xk=(1.2)k[3(1,1)+2(13)k(1,−1)]x_k = (1.2)^k\left[3(1, 1) + 2\left(\frac{1}{3}\right)^k(1, -1)\right], since 0.41.2=13\frac{0.4}{1.2} = \frac{1}{3}. The bracket tends to 3(1,1)3(1, 1): in the long run both lakes hold the same number of trout, and each population grows by 2020 per cent a year. The corrected figure shows the points approaching the eigenline y=xy = x. The ratio is bkak=3(1.2)k−2(0.4)k3(1.2)k+2(0.4)k=3⋅3k−23⋅3k+2\frac{b_k}{a_k} = \frac{3(1.2)^k - 2(0.4)^k}{3(1.2)^k + 2(0.4)^k} = \frac{3 \cdot 3^k - 2}{3 \cdot 3^k + 2}, dividing top and bottom by (0.4)k(0.4)^k. It is at least 0.90.9 when 3⋅3k−2≥2.7⋅3k+1.83 \cdot 3^k - 2 \ge 2.7 \cdot 3^k + 1.8, that is 0.3⋅3k≥3.80.3 \cdot 3^k \ge 3.8, or 3k≥3833^k \ge \frac{38}{3}. Since 32=93^2 = 9 and 33=273^3 = 27, this first happens in year 33: 7983≈0.95\frac{79}{83} \approx 0.95, while year 22 gives 2529≈0.86\frac{25}{29} \approx 0.86.

d) With the quota, xk+1=(M−hI)xkx_{k+1} = (M - hI)x_k. The matrix M−hIM - hI has the SAME eigenvectors, with eigenvalues 1.2−h1.2 - h and 0.4−h0.4 - h, because (M−hI)v=(λ−h)v(M - hI)v = (\lambda - h)v. The populations neither explode nor vanish when the dominant eigenvalue is exactly 11: h=0.2h = 0.2, a fifth of each lake caught every year. Then xk=3(1,1)+2(0.2)k(1,−1)→(3,3)x_k = 3(1, 1) + 2(0.2)^k(1, -1) \to (3, 3): 300300 trout in each lake. For hh slightly above 0.20.2 both populations die out; slightly below, they still grow. The limit (3,3)(3, 3) comes from the coefficient c1=a0+b02c_1 = \frac{a_0 + b_0}{2}, so it depends on the starting TOTAL, not on how it was split.

1234567812345678year 0year 1year 2year 3year 4eigenline y = x, eigenvalue 1.2lake A (hundreds)lake B (hundreds)

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