Linear Algebra and Vector Geometry 201-SN4-RE • CEGEP, Montreal

Corrected practice final exam in linear algebra and vector geometry (201-SN4-RE)

This is a practice final exam for 201-SN4-RE (formerly 201-NYC), Linear Algebra and Vector Geometry, the CEGEP linear algebra course, calibrated to the real level of end-of-term finals. The course has two faces, the geometry of space on one side and abstract structures on the other, and the final exam is precisely the moment when you must show that they are the same objects. Every exercise therefore connects at least two chapters.

Twelve exercises in four parts of 25 marks cover the whole course. Part A is on vector geometry in space: the dot product, the cross product and the scalar triple product on a tetrahedron (exercise 1), lines and planes, with an angle, a distance and an orthogonal projection (exercise 2), and the discussion of a system of linear equations with a parameter in all three of its cases (exercise 3). Part B is on matrices: the inverse and the properties of determinants (exercises 4 and 5), rank, null space and the consistency theorem (exercise 6). Part C is on vector spaces and linear transformations: subspaces, bases and Grassmann's formula (exercise 7), the matrix of an orthogonal projection and of a reflection (exercise 8), kernel, image and the rank theorem for an operator on polynomials (exercise 9). Part D is on diagonalization, with the computation of AnA^{n} and of a square root of a matrix (exercise 10), and on complex numbers, exponential form, De Moivre's theorem and nnth roots (exercise 11). The synthesis problem shows that a permutation matrix is a rotation of space (exercise 12).

The traps are the ones that cost marks on the final: the angle between a line and a plane confused with its angle with the normal (exercise 2), only the generic case treated in a system discussion that requires three (exercise 3), a set that does not contain the zero vector taken for a subspace (exercise 7), and an operator declared diagonalizable without counting the dimensions of its eigenspaces (exercise 9).

Write it under real conditions: 3 hours, timed, calculator allowed, no formula sheet. In linear algebra, the write-up counts as much as the computation: saying which theorem you invoke and checking its hypotheses is often worth more marks than the result itself.

12 corrected exercises • 100 points • 180 minutes

This chapter is part of 201-SN4-RE, every chapter with its set and sheet

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Course recap

  • • Products: u⃗⋅v⃗=∥u⃗∥∥v⃗∥cos⁡θ\vec{u}\cdot\vec{v}=\|\vec{u}\|\|\vec{v}\|\cos\theta; ∥u⃗×v⃗∥=∥u⃗∥∥v⃗∥sin⁡θ\|\vec{u}\times\vec{v}\|=\|\vec{u}\|\|\vec{v}\|\sin\theta (area of the parallelogram); scalar triple product w⃗⋅(u⃗×v⃗)\vec{w}\cdot(\vec{u}\times\vec{v}) (volume of the parallelepiped, zero if the vectors are coplanar).
  • • Planes and lines: plane ax+by+cz=dax+by+cz=d with normal (a,b,c)(a,b,c); distance from a point PP to the plane ∣ax0+by0+cz0−d∣a2+b2+c2\dfrac{|ax_{0}+by_{0}+cz_{0}-d|}{\sqrt{a^{2}+b^{2}+c^{2}}}; distance to a line ∥AP⃗×d⃗∥∥d⃗∥\dfrac{\|\vec{AP}\times\vec{d}\|}{\|\vec{d}\|}.
  • • Systems: rank, consistency theorem, general solution = particular solution + null space. Cramer's rule applies if and only if the determinant is non-zero.
  • • Determinants: det⁡(AB)=det⁡Adet⁡B\det(AB)=\det A\det B, det⁡(kA)=kndet⁡A\det(kA)=k^{n}\det A, det⁡(A−1)=1/det⁡A\det(A^{-1})=1/\det A, det⁡(AT)=det⁡A\det(A^{T})=\det A, det⁡(adj⁡A)=(det⁡A)n−1\det(\operatorname{adj}A)=(\det A)^{n-1}.
  • • Spaces: subspace test, basis, dimension, rank theorem dim⁡ker⁡T+dim⁡im⁡T=dim⁡E\dim\ker T+\dim\operatorname{im}T=\dim E, Grassmann's formula dim⁡(F+G)=dim⁡F+dim⁡G−dim⁡(F∩G)\dim(F+G)=\dim F+\dim G-\dim(F\cap G).
  • • Diagonalization: det⁡(A−λI)=0\det(A-\lambda I)=0; A=PDP−1A=PDP^{-1} if the dimensions of the eigenspaces add up to nn; then Ak=PDkP−1A^{k}=PD^{k}P^{-1}.
  • • Complex numbers: z=reiθz=r e^{i\theta}, De Moivre zn=rneinθz^{n}=r^{n}e^{in\theta}, nnth roots evenly spaced on the circle of radius r1/nr^{1/n}.

Part A: Vector geometry in space (/25)

Exercise 1: A tetrahedron and the three products

Consider the points A(1,0,2)A(1,0,2), B(3,1,0)B(3,1,0), C(2,4,1)C(2,4,1) and D(0,2,5)D(0,2,5). Each question is settled with one product and one only: it is up to you to choose which one, and to say why the other two do not fit.

ABCD
  • a) Compute AB⃗\vec{AB}, AC⃗\vec{AC} and AD⃗\vec{AD}, then the norm of AB⃗\vec{AB} and the angle between AB⃗\vec{AB} and AC⃗\vec{AC} (to the nearest tenth of a degree).
  • b) Compute AB⃗×AC⃗\vec{AB}\times\vec{AC} and deduce the exact area of triangle ABCABC.
  • c) Compute the scalar triple product AD⃗⋅(AB⃗×AC⃗)\vec{AD}\cdot\left(\vec{AB}\times\vec{AC}\right). Are the four points coplanar? Give the volume of tetrahedron ABCDABCD.
  • d) Deduce the distance from DD to the plane ABCABC, then compute separately the distance from DD to the line ABAB. Explain why these two distances have no reason to be equal.

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  • a) AB⃗=(2,1,−2)\vec{AB}=(2,1,-2), norm 3; angle 51.1∘51.1^\circ
  • b) (7,0,7)(7,0,7); area 722≈4.95\frac{7\sqrt2}{2}\approx 4.95
  • c) 14: not coplanar; volume 73\frac73
  • d) 2\sqrt2 to the plane, 10\sqrt{10} to the line
a)

AB⃗=(2,1,−2)\vec{AB}=(2,1,-2), AC⃗=(1,4,−1)\vec{AC}=(1,4,-1), AD⃗=(−1,2,3)\vec{AD}=(-1,2,3). Norm: ∥AB⃗∥=4+1+4=3\|\vec{AB}\|=\sqrt{4+1+4}=3. Angle: the dot product is the tool for angles. AB⃗⋅AC⃗=2+4+2=8\vec{AB}\cdot\vec{AC}=2+4+2=8 and ∥AC⃗∥=18=32\|\vec{AC}\|=\sqrt{18}=3\sqrt{2}, so cos⁡θ=83⋅32=892≈0.6285\cos\theta=\dfrac{8}{3\cdot 3\sqrt{2}}=\dfrac{8}{9\sqrt{2}}\approx 0.6285 and θ≈51.1∘\theta\approx 51.1^{\circ}.

b)

The cross product is the tool for areas. AB⃗×AC⃗=∣i⃗j⃗k⃗21−214−1∣=(1⋅(−1)−(−2)⋅4) i⃗−(2⋅(−1)−(−2)⋅1) j⃗+(2⋅4−1⋅1) k⃗=(7,0,7)\vec{AB}\times\vec{AC}=\begin{vmatrix}\vec{i}&\vec{j}&\vec{k}\\ 2&1&-2\\ 1&4&-1\end{vmatrix}=(1\cdot(-1)-(-2)\cdot 4)\,\vec{i}-(2\cdot(-1)-(-2)\cdot 1)\,\vec{j}+(2\cdot 4-1\cdot 1)\,\vec{k}=(7,0,7). Its norm is 727\sqrt{2}, which is the area of the parallelogram. The area of the triangle is half of it: 722≈4.95\dfrac{7\sqrt{2}}{2}\approx 4.95.

c)

The scalar triple product is the tool for volumes. AD⃗⋅(7,0,7)=−7+0+21=14≠0\vec{AD}\cdot(7,0,7)=-7+0+21=14\neq 0: the four points are therefore not coplanar, they form a genuine tetrahedron. Volume of the parallelepiped =∣14∣=14=|14|=14, volume of the tetrahedron =146=73≈2.33=\dfrac{14}{6}=\dfrac{7}{3}\approx 2.33.

d)

Distance to the plane: from V=13AhV=\frac{1}{3}\mathcal{A}h, where A\mathcal{A} is the area of the base ABCABC, we get h=3VA=3⋅73722=7⋅272=2≈1.41h=\dfrac{3V}{\mathcal{A}}=\dfrac{3\cdot\frac{7}{3}}{\frac{7\sqrt{2}}{2}}=\dfrac{7\cdot 2}{7\sqrt{2}}=\sqrt{2}\approx 1.41. Distance to the line ABAB: AD⃗×AB⃗=(−7,4,−5)\vec{AD}\times\vec{AB}=(-7,4,-5), of norm 49+16+25=90=310\sqrt{49+16+25}=\sqrt{90}=3\sqrt{10}, so d=3103=10≈3.16d=\dfrac{3\sqrt{10}}{3}=\sqrt{10}\approx 3.16. The two distances measure different things: the first is the distance to a whole plane, the second the distance to a single line lying in that plane. A point is always at least as far from a line as from the plane that contains it, which 10>2\sqrt{10}>\sqrt{2} confirms.

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Exercise 2: A line, a plane and a point off the plane

Consider the plane π: 2x−y+3z=5\pi:\ 2x-y+3z=5, the line L\mathcal{L} through (1,2,−1)(1,2,-1) with direction vector d⃗=(1,1,−1)\vec{d}=(1,1,-1), and the point P(4,1,0)P(4,1,0).

  • a) Show that L\mathcal{L} is neither parallel to π\pi nor contained in it, then give their point of intersection.
  • b) Compute the angle between L\mathcal{L} and the plane π\pi (to the nearest hundredth of a degree). Be careful not to confuse this angle with the angle between L\mathcal{L} and the normal.
  • c) Compute the distance from PP to the plane π\pi, then the coordinates of the orthogonal projection of PP onto π\pi.
  • d) Give the scalar equation of the plane parallel to π\pi through PP, and check that it agrees with the distance found in c).

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  • a) n⃗⋅d⃗=−2\vec n\cdot\vec d=-2; point (−3,−2,3)(-3,-2,3)
  • b) α≈17.98∘\alpha\approx 17.98^\circ
  • c) 214≈0.535\frac{2}{\sqrt{14}}\approx 0.535; (267,87,−37)\left(\frac{26}{7},\frac87,-\frac37\right)
  • d) 2x−y+3z=72x-y+3z=7
a)

The normal of π\pi is n⃗=(2,−1,3)\vec{n}=(2,-1,3). Since n⃗⋅d⃗=2−1−3=−2≠0\vec{n}\cdot\vec{d}=2-1-3=-2\neq 0, the line is not parallel to the plane (so it is not contained in it either): it crosses it at a single point. Parametrize the line as (1+t, 2+t, −1−t)(1+t,\,2+t,\,-1-t) and substitute: 2(1+t)−(2+t)+3(−1−t)=−2t−3=52(1+t)-(2+t)+3(-1-t)=-2t-3=5, so t=−4t=-4 and the point of intersection is (−3,−2,3)(-3,-2,3). Check: −6+2+9=5-6+2+9=5.

b)

The angle α\alpha between a line and a plane is the complement of the angle between the line and the normal, hence sin⁡α=∣n⃗⋅d⃗∣∥n⃗∥ ∥d⃗∥=2143=242≈0.3086\sin\alpha=\frac{|\vec{n}\cdot\vec{d}|}{\|\vec{n}\|\,\|\vec{d}\|}=\frac{2}{\sqrt{14}\sqrt{3}}=\frac{2}{\sqrt{42}}\approx 0.3086, so α≈17.98∘\alpha\approx 17.98^{\circ}. It really is a sine and not a cosine: that is where most of the marks are lost on the exam.

c)

d(P,π)=∣2(4)−1+3(0)−5∣4+1+9=∣8−1−5∣14=214≈0.535d(P,\pi)=\dfrac{|2(4)-1+3(0)-5|}{\sqrt{4+1+9}}=\dfrac{|8-1-5|}{\sqrt{14}}=\dfrac{2}{\sqrt{14}}\approx 0.535. For the projection, move from PP along the normal: P′=P−2x0−y0+3z0−5∥n⃗∥2 n⃗=(4,1,0)−214(2,−1,3)=(267,87,−37)P'=P-\dfrac{2x_{0}-y_{0}+3z_{0}-5}{\|\vec{n}\|^{2}}\,\vec{n}=(4,1,0)-\dfrac{2}{14}(2,-1,3)=\left(\dfrac{26}{7},\dfrac{8}{7},-\dfrac{3}{7}\right). Check: 2⋅267−87+3(−37)=52−8−97=52\cdot\frac{26}{7}-\frac{8}{7}+3\left(-\frac{3}{7}\right)=\frac{52-8-9}{7}=5.

d)

A parallel plane has the same normal, so its equation is 2x−y+3z=k2x-y+3z=k; requiring it to pass through PP gives k=8−1+0=7k=8-1+0=7. The two planes are 2x−y+3z=52x-y+3z=5 and 2x−y+3z=72x-y+3z=7, and the distance between two parallel planes is ∣7−5∣∥n⃗∥=214\dfrac{|7-5|}{\|\vec{n}\|}=\dfrac{2}{\sqrt{14}}, exactly the distance found in c). This is consistent: the distance from PP to π\pi is, by definition, the distance between π\pi and the parallel plane through PP.

πPP′2/√14

Exercise 3: A system with a parameter: the three cases

For a∈Ra\in\mathbb{R}, consider the system {x+y+z=12x+3y+az=3x+ay+3z=3\begin{cases} x+y+z=1\\ 2x+3y+az=3\\ x+ay+3z=3\end{cases}. The complete discussion of such a system is the classic end-of-term exercise: all three cases are required, not only the generic one.

  • a) Compute the determinant of the coefficient matrix as a function of aa and factor it.
  • b) For which values of aa does the system have a unique solution? Solve it for a=1a=1.
  • c) Deal with the case a=3a=3: show that the system has infinitely many solutions and describe them with one parameter.
  • d) Deal with the case a=0a=0: show that the system is inconsistent. Interpret cases c) and d) geometrically in terms of three planes.

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  • a) Δ=−a(a−3)\Delta=-a(a-3)
  • b) Unique if a≠0a\neq 0 and a≠3a\neq 3; (−2,2,1)(-2,2,1)
  • c) (0, 1−t, t)(0,\ 1-t,\ t)
  • d) Inconsistent: a prism; in c), a common line
a)

Expanding along the first row: Δ=∣11123a1a3∣=(9−a2)−(6−a)+(2a−3)=−a2+3a=−a(a−3)\Delta=\begin{vmatrix}1&1&1\\ 2&3&a\\ 1&a&3\end{vmatrix}=(9-a^{2})-(6-a)+(2a-3)=-a^{2}+3a=-a(a-3).

b)

The system has a unique solution if and only if Δ≠0\Delta\neq 0, that is for a≠0a\neq 0 and a≠3a\neq 3. For a=1a=1: R3−R1R_{3}-R_{1} gives 2z=22z=2, so z=1z=1; R2−2R1R_{2}-2R_{1} gives y−z=1y-z=1, so y=2y=2; then R1R_{1} gives x=1−2−1=−2x=1-2-1=-2. The solution is (x,y,z)=(−2,2,1)(x,y,z)=(-2,2,1). Check in R2R_{2}: −4+6+1=3-4+6+1=3, and in R3R_{3}: −2+2+3=3-2+2+3=3.

c)

For a=3a=3, Δ=0\Delta=0. Row reduce: R2−2R1R_{2}-2R_{1} gives y+z=1y+z=1 and R3−R1R_{3}-R_{1} gives 2y+2z=22y+2z=2, that is again y+z=1y+z=1. The third equation is therefore the same as the second: the coefficient matrix has rank 2, so does the augmented matrix, and the system is consistent with 3−2=13-2=1 free parameter. From y+z=1y+z=1 and x+y+z=1x+y+z=1 we get x=0x=0, then setting z=tz=t: (x,y,z)=(0, 1−t, t),t∈R(x,y,z)=(0,\,1-t,\,t),\qquad t\in\mathbb{R}. This is a line of solutions.

d)

For a=0a=0, Δ=0\Delta=0 as well. R2−2R1R_{2}-2R_{1} gives y−2z=1y-2z=1 and R3−R1R_{3}-R_{1} gives −y+2z=2-y+2z=2, that is y−2z=−2y-2z=-2. The two contradict each other: the coefficient matrix has rank 2, the augmented matrix has rank 3, and by the consistency theorem the system has no solution. Geometrically: in case c) the three planes meet along a common line; in case d) they form a prism: each pair of planes meets along a line, but the three lines of intersection are parallel and distinct, so no point lies on all three planes.

Part B: Matrices, determinants and rank (/25)

Exercise 4: An invertible matrix and the determinants built from it

Let A=(123014560)A=\begin{pmatrix} 1&2&3\\ 0&1&4\\ 5&6&0\end{pmatrix}. This matrix has the special feature of having determinant 1, hence an inverse with integer entries only: take advantage of it to check your work without a calculator.

  • a) Compute det⁡A\det A by cofactor expansion along a row or a column of your choice.
  • b) Compute A−1A^{-1}, by Gauss-Jordan elimination or with the adjoint matrix. Check your result with a product.
  • c) Solve the system Ax⃗=b⃗A\vec{x}=\vec{b} with b⃗=(1,2,3)T\vec{b}=(1,2,3)^{T}, using A−1A^{-1}.
  • d) Without computing any new matrix, give det⁡(2A)\det(2A), det⁡(A−1)\det\left(A^{-1}\right), det⁡(A3)\det\left(A^{3}\right) and det⁡(adj⁡A)\det\left(\operatorname{adj}A\right). Then, if BB is a 3×33\times 3 matrix with det⁡B=2\det B=2, give det⁡(A−1B2AT)\det\left(A^{-1}B^{2}A^{T}\right).

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  • a) det⁡A=1\det A=1
  • b) A−1=(−2418520−15−4−541)A^{-1}=\begin{pmatrix}-24&18&5\\20&-15&-4\\-5&4&1\end{pmatrix}
  • c) (27, −22, 6)(27,\ -22,\ 6)
  • d) 8, 1, 1, 1; then 4
a)

Expansion along the first column (it contains a zero): det⁡A=1∣1460∣−0+5∣2314∣=1(0−24)+5(8−3)=−24+25=1\det A=1\begin{vmatrix}1&4\\ 6&0\end{vmatrix}-0+5\begin{vmatrix}2&3\\ 1&4\end{vmatrix}=1(0-24)+5(8-3)=-24+25=1.

b)

With cofactors, A−1=1det⁡Aadj⁡A=adj⁡AA^{-1}=\dfrac{1}{\det A}\operatorname{adj}A=\operatorname{adj}A since det⁡A=1\det A=1. We obtain A−1=(−2418520−15−4−541)A^{-1}=\begin{pmatrix} -24&18&5\\ 20&-15&-4\\ -5&4&1\end{pmatrix}. Check: AA−1=IAA^{-1}=I; for instance the first two entries of the first row of the product are (1)(−24)+(2)(20)+(3)(−5)=−24+40−15=1(1)(-24)+(2)(20)+(3)(-5)=-24+40-15=1 and (1)(18)+(2)(−15)+(3)(4)=18−30+12=0(1)(18)+(2)(-15)+(3)(4)=18-30+12=0.

c)

x⃗=A−1b⃗\vec{x}=A^{-1}\vec{b}: x⃗=(−2418520−15−4−541)(123)=(−24+36+1520−30−12−5+8+3)=(27−226)\vec{x}=\begin{pmatrix} -24&18&5\\ 20&-15&-4\\ -5&4&1\end{pmatrix}\begin{pmatrix}1\\ 2\\ 3\end{pmatrix}=\begin{pmatrix}-24+36+15\\ 20-30-12\\ -5+8+3\end{pmatrix}=\begin{pmatrix}27\\ -22\\ 6\end{pmatrix}. Direct check: 27+2(−22)+3(6)=27−44+18=127+2(-22)+3(6)=27-44+18=1, 0−22+24=20-22+24=2, 5(27)+6(−22)=135−132=35(27)+6(-22)=135-132=3.

d)

Apply the properties, never recomputing a matrix. det⁡(2A)=23det⁡A=8\det(2A)=2^{3}\det A=8. det⁡(A−1)=1det⁡A=1\det\left(A^{-1}\right)=\dfrac{1}{\det A}=1. det⁡(A3)=(det⁡A)3=1\det\left(A^{3}\right)=(\det A)^{3}=1. det⁡(adj⁡A)=(det⁡A)n−1=12=1\det(\operatorname{adj}A)=(\det A)^{n-1}=1^{2}=1. Finally det⁡(A−1B2AT)=1det⁡A⋅(det⁡B)2⋅det⁡A=(det⁡B)2=4\det\left(A^{-1}B^{2}A^{T}\right)=\frac{1}{\det A}\cdot(\det B)^{2}\cdot\det A=(\det B)^{2}=4, the factor det⁡A\det A cancelling with its reciprocal: the result does not depend on AA.

Exercise 5: Determinants: compute fast, then reason

A determinant is rarely computed by blind expansion. The four questions go from raw computation to using the determinant as a tool for geometric decisions.

  • a) Compute ∣1201231001211012∣\begin{vmatrix} 1&2&0&1\\ 2&3&1&0\\ 0&1&2&1\\ 1&0&1&2\end{vmatrix} by creating zeros with elementary row operations before expanding.
  • b) Let MM be a 3×33\times 3 matrix with det⁡M=5\det M=5. Give det⁡(2M)\det(2M), det⁡(MTM)\det\left(M^{T}M\right), det⁡(M−1)\det\left(M^{-1}\right) and det⁡(adj⁡M)\det(\operatorname{adj}M), citing the property used each time.
  • c) Find all values of λ\lambda for which the matrix N=(λ111λ111λ)N=\begin{pmatrix} \lambda&1&1\\ 1&\lambda&1\\ 1&1&\lambda\end{pmatrix} is singular. Factor the determinant.
  • d) Are the vectors u⃗=(1,2,3)\vec{u}=(1,2,3), v⃗=(2,−1,1)\vec{v}=(2,-1,1) and w⃗=(4,3,7)\vec{w}=(4,3,7) coplanar? If so, express w⃗\vec{w} as a linear combination of u⃗\vec{u} and v⃗\vec{v}.

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  • a) −14-14
  • b) 40, 25, 15\frac15, 25
  • c) (λ+2)(λ−1)2(\lambda+2)(\lambda-1)^2: −2-2 and 11
  • d) Coplanar: w⃗=2u⃗+v⃗\vec w=2\vec u+\vec v
a)

Replace R2R_{2} by R2−2R1R_{2}-2R_{1} and R4R_{4} by R4−R1R_{4}-R_{1}, which does not change the determinant: ∣12010−11−201210−211∣=1⋅∣−11−2121−211∣\begin{vmatrix} 1&2&0&1\\ 0&-1&1&-2\\ 0&1&2&1\\ 0&-2&1&1\end{vmatrix}=1\cdot\begin{vmatrix} -1&1&-2\\ 1&2&1\\ -2&1&1\end{vmatrix}. This 3×33\times 3 determinant equals −1(2−1)−1(1+2)+(−2)(1+4)=−1−3−10=−14-1(2-1)-1(1+2)+(-2)(1+4)=-1-3-10=-14. So the required determinant is −14-14.

b)

det⁡(2M)=23det⁡M=8⋅5=40\det(2M)=2^{3}\det M=8\cdot 5=40 (multiplying an n×nn\times n matrix by kk multiplies the determinant by knk^{n}, one row at a time). det⁡(MTM)=det⁡MT⋅det⁡M=(det⁡M)2=25\det\left(M^{T}M\right)=\det M^{T}\cdot\det M=(\det M)^{2}=25 (multiplicativity, and det⁡MT=det⁡M\det M^{T}=\det M). det⁡(M−1)=15\det\left(M^{-1}\right)=\dfrac{1}{5} (because det⁡M⋅det⁡M−1=det⁡I=1\det M\cdot\det M^{-1}=\det I=1). det⁡(adj⁡M)=(det⁡M)n−1=52=25\det(\operatorname{adj}M)=(\det M)^{n-1}=5^{2}=25.

c)

Add the last two rows to the first: every entry of R1R_{1} becomes λ+2\lambda+2, which factors out. What remains is det⁡N=(λ+2)∣1111λ111λ∣=(λ+2)(λ−1)2\det N=(\lambda+2)\begin{vmatrix} 1&1&1\\ 1&\lambda&1\\ 1&1&\lambda\end{vmatrix}=(\lambda+2)(\lambda-1)^{2}. The matrix is singular for λ=−2\lambda=-2 and for λ=1\lambda=1 (a double root). For λ=1\lambda=1 the three rows are identical (rank 1); for λ=−2\lambda=-2 the sum of the three rows is zero (rank 2).

d)

The scalar triple product decides coplanarity: ∣1232−11437∣=1(−7−3)−2(14−4)+3(6+4)=−10−20+30=0\begin{vmatrix} 1&2&3\\ 2&-1&1\\ 4&3&7\end{vmatrix}=1(-7-3)-2(14-4)+3(6+4)=-10-20+30=0. The determinant is zero, so the three vectors are coplanar (the parallelepiped they span is flat). We then look for α,β\alpha,\beta with w⃗=αu⃗+βv⃗\vec{w}=\alpha\vec{u}+\beta\vec{v}: the first two components give α+2β=4\alpha+2\beta=4 and 2α−β=32\alpha-\beta=3, hence β=1\beta=1 and α=2\alpha=2. The third component confirms it: 3(2)+1=73(2)+1=7. So w⃗=2u⃗+v⃗\vec{w}=2\vec{u}+\vec{v}.

Exercise 6: Rank, consistency and the structure of the solution set

Let A=(12−132410122−3)A=\begin{pmatrix} 1&2&-1&3\\ 2&4&1&0\\ 1&2&2&-3\end{pmatrix}. This matrix is not square: no determinant and no inverse here, only the rank and the consistency theorem.

  • a) Reduce AA to row echelon form and give its rank.
  • b) Find the null space of AA (the set of x⃗\vec{x} such that Ax⃗=0⃗A\vec{x}=\vec{0}), with a basis and its dimension.
  • c) Check the rank theorem on this example.
  • d) Show that the system Ax⃗=b⃗A\vec{x}=\vec{b} is consistent only if the components of b⃗\vec{b} satisfy a certain relation. Give this relation, then solve the system completely for b⃗=(1,3,2)T\vec{b}=(1,3,2)^{T}.

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  • a) Rank 2
  • b) Basis (−2,1,0,0)(-2,1,0,0), (−1,0,2,1)(-1,0,2,1): dimension 2
  • c) 2+2=42+2=4
  • d) b1+b3−b2=0b_1+b_3-b_2=0; (43,0,13,0)+\left(\frac43,0,\frac13,0\right)+ null space
a)

R2−2R1R_{2}-2R_{1} gives (0,0,3,−6)(0,0,3,-6) and R3−R1R_{3}-R_{1} gives (0,0,3,−6)(0,0,3,-6): the two new rows are identical. The echelon form has only two non-zero rows, so rank⁡(A)=2\operatorname{rank}(A)=2. In other words, the three rows of AA satisfy the relation R1+R3−R2=0⃗R_{1}+R_{3}-R_{2}=\vec{0}.

b)

The homogeneous system reduces to x1+2x2−x3+3x4=0x_{1}+2x_{2}-x_{3}+3x_{4}=0 and 3x3−6x4=03x_{3}-6x_{4}=0. The second gives x3=2x4x_{3}=2x_{4}; substituting, x1=−2x2+x3−3x4=−2x2−x4x_{1}=-2x_{2}+x_{3}-3x_{4}=-2x_{2}-x_{4}. The free variables are x2x_{2} and x4x_{4}: x⃗=x2(−2100)+x4(−1021)\vec{x}=x_{2}\begin{pmatrix}-2\\ 1\\ 0\\ 0\end{pmatrix}+x_{4}\begin{pmatrix}-1\\ 0\\ 2\\ 1\end{pmatrix}. These two vectors are independent (look at components 2 and 4), so they form a basis of the null space, which has dimension 2.

c)

The rank theorem states rank⁡(A)+dim⁡(null space)=n\operatorname{rank}(A)+\dim(\text{null space})=n, the number of columns. Here 2+2=42+2=4, and AA does have 4 columns. The count checks out.

d)

Since R1+R3−R2=0⃗R_{1}+R_{3}-R_{2}=\vec{0} on the coefficient side, the same combination must cancel the right-hand side: the consistency condition is b1+b3−b2=0b_{1}+b_{3}-b_{2}=0. For b⃗=(1,3,2)T\vec{b}=(1,3,2)^{T}: 1+2−3=01+2-3=0, so the system is consistent. Solving: 3x3−6x4=b2−2b1=13x_{3}-6x_{4}=b_{2}-2b_{1}=1, so x3=13+2x4x_{3}=\frac{1}{3}+2x_{4}, then x1=1−2x2+x3−3x4=43−2x2−x4x_{1}=1-2x_{2}+x_{3}-3x_{4}=\frac{4}{3}-2x_{2}-x_{4}. The general solution is x⃗=(4/301/30)+x2(−2100)+x4(−1021)\vec{x}=\begin{pmatrix}4/3\\ 0\\ 1/3\\ 0\end{pmatrix}+x_{2}\begin{pmatrix}-2\\ 1\\ 0\\ 0\end{pmatrix}+x_{4}\begin{pmatrix}-1\\ 0\\ 2\\ 1\end{pmatrix}, that is, one particular solution plus the whole null space. This is the general structure of the solution set of a consistent system.

Part C: Vector spaces and transformations (/25)

Exercise 7: Subspaces, bases and Grassmann's formula

The first three questions are about R3\mathbb{R}^{3}, the last one about the space P2P_{2} of polynomials of degree at most 2. The point is to show that the reasoning is exactly the same.

  • a) Is the set W={(x,y,z)∈R3 : x+2y−z=0}W=\left\{(x,y,z)\in\mathbb{R}^{3}\ :\ x+2y-z=0\right\} a subspace? Justify with the subspace test, and give a basis and its dimension.
  • b) Same question for U={(x,y,z)∈R3 : x+y=1}U=\left\{(x,y,z)\in\mathbb{R}^{3}\ :\ x+y=1\right\}. One argument is enough.
  • c) Let F=span⁡{(1,1,0),(0,1,1)}F=\operatorname{span}\{(1,1,0),(0,1,1)\} and G=span⁡{(1,0,1),(1,1,1)}G=\operatorname{span}\{(1,0,1),(1,1,1)\}. Give dim⁡F\dim F, dim⁡G\dim G, dim⁡(F+G)\dim(F+G), then dim⁡(F∩G)\dim(F\cap G) by Grassmann's formula. Finally, give a direction vector of F∩GF\cap G.
  • d) Is the set {1+x, x+x2, 1+x2}\left\{1+x,\ x+x^{2},\ 1+x^{2}\right\} a basis of P2P_{2}? If so, give the coordinates of p(x)=2+3x+4x2p(x)=2+3x+4x^{2} relative to this basis.

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  • a) Yes: basis (−2,1,0)(-2,1,0), (1,0,1)(1,0,1), dimension 2
  • b) No: 0⃗∉U\vec 0\notin U
  • c) 2, 2, 3, then 1; (1,2,1)(1,2,1)
  • d) A basis; (12,52,32)\left(\frac12,\frac52,\frac32\right)
a)

Yes. WW is the solution set of a homogeneous linear equation: it contains 0⃗\vec{0}, and if u⃗,v⃗∈W\vec{u},\vec{v}\in W and α,β∈R\alpha,\beta\in\mathbb{R}, then αu⃗+βv⃗\alpha\vec{u}+\beta\vec{v} still satisfies the equation by linearity. It is therefore a subspace. Setting x=−2y+zx=-2y+z, we get (x,y,z)=y(−2,1,0)+z(1,0,1)(x,y,z)=y(-2,1,0)+z(1,0,1): a basis is {(−2,1,0),(1,0,1)}\{(-2,1,0),(1,0,1)\} and dim⁡W=2\dim W=2 (it is a plane through the origin).

b)

No. The zero vector (0,0,0)(0,0,0) does not satisfy x+y=1x+y=1, and every subspace must contain the zero vector: UU is not a subspace. (It is an affine plane, parallel to a subspace but shifted.) The zero vector test is the first reflex, and it settles the question at once.

c)

The two generators of FF are not proportional, so dim⁡F=2\dim F=2; likewise dim⁡G=2\dim G=2. For F+GF+G, it is enough to find three independent vectors among the four: ∣110011101∣=1(1)−1(−1)+0=2≠0\begin{vmatrix} 1&1&0\\ 0&1&1\\ 1&0&1\end{vmatrix}=1(1)-1(-1)+0=2\neq 0, so F+GF+G contains three independent vectors of R3\mathbb{R}^{3}: F+G=R3F+G=\mathbb{R}^{3} and dim⁡(F+G)=3\dim(F+G)=3. Grassmann's formula then gives dim⁡(F∩G)=dim⁡F+dim⁡G−dim⁡(F+G)=2+2−3=1\dim(F\cap G)=\dim F+\dim G-\dim(F+G)=2+2-3=1. For a direction vector, write a(1,1,0)+b(0,1,1)=c(1,0,1)+d(1,1,1)a(1,1,0)+b(0,1,1)=c(1,0,1)+d(1,1,1), which gives a=c+da=c+d, a+b=da+b=d, b=c+db=c+d. So b=ab=a, then d=2ad=2a and c=−ac=-a. With a=1a=1: the vector (1,2,1)(1,2,1), which is indeed in FF (the sum of the two generators) and in GG (since −(1,0,1)+2(1,1,1)=(1,2,1)-\left(1,0,1\right)+2\left(1,1,1\right)=(1,2,1)).

d)

In the standard basis {1,x,x2}\{1,x,x^{2}\}, the three polynomials have coordinate vectors (1,1,0)(1,1,0), (0,1,1)(0,1,1) and (1,0,1)(1,0,1). The determinant of these three columns is 2, which is non-zero: the set is linearly independent, and since dim⁡P2=3\dim P_{2}=3, it is a basis. For the coordinates of pp, solve α(1+x)+β(x+x2)+γ(1+x2)=2+3x+4x2\alpha(1+x)+\beta(x+x^{2})+\gamma(1+x^{2})=2+3x+4x^{2}, that is α+γ=2\alpha+\gamma=2, α+β=3\alpha+\beta=3, β+γ=4\beta+\gamma=4. Adding all three: 2(α+β+γ)=92(\alpha+\beta+\gamma)=9, so α+β+γ=92\alpha+\beta+\gamma=\frac{9}{2}, hence γ=92−3=32\gamma=\frac{9}{2}-3=\frac{3}{2}, α=92−4=12\alpha=\frac{9}{2}-4=\frac{1}{2} and β=92−2=52\beta=\frac{9}{2}-2=\frac{5}{2}. The coordinates are (12,52,32)\left(\frac{1}{2},\frac{5}{2},\frac{3}{2}\right).

Exercise 8: Orthogonal projection onto a line, and the reflection that follows from it

Let LL be the line in the plane with equation y=2xy=2x, and let PP be the matrix of the orthogonal projection onto LL. A projection is not invertible, and that is precisely what makes it instructive.

-2-11234-112345L: y = 2x
  • a) Find the matrix PP of the orthogonal projection onto LL (use the formula for the projection onto a direction vector).
  • b) Check that P2=PP^{2}=P and explain what this means geometrically. Compute det⁡P\det P and say why PP cannot be invertible.
  • c) Give ker⁡P\ker P and im⁡P\operatorname{im}P (each with a direction vector), and check the rank theorem in R2\mathbb{R}^{2}.
  • d) Show that Q=2P−IQ=2P-I is the matrix of the reflection about LL. Check that Q2=IQ^{2}=I and det⁡Q=−1\det Q=-1, and recover the angle of LL from QQ.

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  • a) P=15(1224)P=\frac15\begin{pmatrix}1&2\\2&4\end{pmatrix}
  • b) P2=PP^2=P, det⁡P=0\det P=0
  • c) ker⁡P=span⁡{(2,−1)}\ker P=\operatorname{span}\{(2,-1)\}, im⁡P=L\operatorname{im}P=L
  • d) Q=15(−3443)Q=\frac15\begin{pmatrix}-3&4\\4&3\end{pmatrix}; θ≈63.43∘\theta\approx 63.43^\circ
a)

A direction vector of LL is u⃗=(1,2)\vec{u}=(1,2), with squared norm 5. The projection of x⃗\vec{x} onto u⃗\vec{u} is x⃗⋅u⃗∥u⃗∥2u⃗\dfrac{\vec{x}\cdot\vec{u}}{\|\vec{u}\|^{2}}\vec{u}, which gives the matrix P=15(1224)P=\frac{1}{5}\begin{pmatrix}1&2\\ 2&4\end{pmatrix}. Check: P(1,2)T=15(1+4, 2+8)T=(1,2)TP(1,2)^{T}=\frac{1}{5}(1+4,\ 2+8)^{T}=(1,2)^{T}: a vector of LL is indeed left in place.

b)

P2=125(1224)(1224)=125(5101020)=15(1224)=PP^{2}=\dfrac{1}{25}\begin{pmatrix}1&2\\ 2&4\end{pmatrix}\begin{pmatrix}1&2\\ 2&4\end{pmatrix}=\dfrac{1}{25}\begin{pmatrix}5&10\\ 10&20\end{pmatrix}=\dfrac{1}{5}\begin{pmatrix}1&2\\ 2&4\end{pmatrix}=P. Geometrically: projecting a point that is already projected no longer moves it; the operation is idempotent. det⁡P=125(4−4)=0\det P=\dfrac{1}{25}(4-4)=0, so PP is not invertible, and this is unavoidable: the projection crushes the whole plane onto a line, so it is not one-to-one and no matrix can undo the crushing.

c)

ker⁡P\ker P is the direction perpendicular to LL, spanned by (2,−1)(2,-1): indeed P(2,−1)T=15(2−2, 4−4)T=(0,0)TP(2,-1)^{T}=\frac{1}{5}(2-2,\ 4-4)^{T}=(0,0)^{T}. im⁡P=L\operatorname{im}P=L, spanned by (1,2)(1,2). Rank theorem: dim⁡ker⁡P+rank⁡P=1+1=2=dim⁡R2\dim\ker P+\operatorname{rank}P=1+1=2=\dim\mathbb{R}^{2}.

d)

Q=2P−I=25(1224)−(1001)=15(−3443)Q=2P-I=\frac{2}{5}\begin{pmatrix}1&2\\ 2&4\end{pmatrix}-\begin{pmatrix}1&0\\ 0&1\end{pmatrix}=\frac{1}{5}\begin{pmatrix}-3&4\\ 4&3\end{pmatrix}. It is indeed a reflection: for any x⃗\vec{x}, Qx⃗=2Px⃗−x⃗Q\vec{x}=2P\vec{x}-\vec{x}, which is the mirror image of x⃗\vec{x} through the point Px⃗P\vec{x}, its projection, hence the mirror image of x⃗\vec{x} about LL. We check Q2=125(9+16−12+12−12+1216+9)=IQ^{2}=\dfrac{1}{25}\begin{pmatrix}9+16&-12+12\\ -12+12&16+9\end{pmatrix}=I (a reflection applied twice is the identity) and det⁡Q=125(−9−16)=−1\det Q=\dfrac{1}{25}(-9-16)=-1 (it reverses orientation). Finally, the matrix of the reflection about a line that makes the angle θ\theta with the xx-axis is (cos⁡2θsin⁡2θsin⁡2θ−cos⁡2θ)\begin{pmatrix}\cos 2\theta&\sin 2\theta\\ \sin 2\theta&-\cos 2\theta\end{pmatrix}; comparing, cos⁡2θ=−35\cos 2\theta=-\frac{3}{5} and sin⁡2θ=45\sin 2\theta=\frac{4}{5}, so 2θ≈126.87∘2\theta\approx 126.87^{\circ} and θ≈63.43∘\theta\approx 63.43^{\circ}, which is indeed arctan⁡2\arctan 2.

-2-11234-112345L: y = 2xvPv = (1, 2)Qv

Exercise 9: A transformation on polynomials: kernel, image and nilpotence

On the space P2P_{2} of polynomials of degree at most 2, define the finite difference operator T(p)(x)=p(x+1)−p(x)T(p)(x)=p(x+1)-p(x). This operator is the discrete counterpart of differentiation, and it behaves very much like it.

  • a) Check that TT is linear, then give its matrix MM relative to the basis {1, x, x2}\left\{1,\,x,\,x^{2}\right\}.
  • b) Find ker⁡T\ker T and im⁡T\operatorname{im}T, with a basis of each. Check the rank theorem.
  • c) Compute M2M^{2} and M3M^{3}. Interpret the result in terms of successive differences of a polynomial of degree 2.
  • d) Give the eigenvalues of TT. Is the operator TT diagonalizable? Justify carefully.

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  • a) M=(011002000)M=\begin{pmatrix}0&1&1\\0&0&2\\0&0&0\end{pmatrix}
  • b) Kernel: the constants; image: P1P_1
  • c) M3=0M^3=0
  • d) Only eigenvalue 0: not diagonalizable
a)

Linearity: T(αp+βq)(x)=(αp+βq)(x+1)−(αp+βq)(x)=α[p(x+1)−p(x)]+β[q(x+1)−q(x)]=αT(p)+βT(q)T(\alpha p+\beta q)(x)=(\alpha p+\beta q)(x+1)-(\alpha p+\beta q)(x)=\alpha\left[p(x+1)-p(x)\right]+\beta\left[q(x+1)-q(x)\right]=\alpha T(p)+\beta T(q). Images of the basis vectors: T(1)=1−1=0T(1)=1-1=0; T(x)=(x+1)−x=1T(x)=(x+1)-x=1; T(x2)=(x+1)2−x2=2x+1T\left(x^{2}\right)=(x+1)^{2}-x^{2}=2x+1. Writing their coordinates as columns, M=(011002000)M=\begin{pmatrix} 0&1&1\\ 0&0&2\\ 0&0&0\end{pmatrix}. Note that TT lowers the degree by exactly one, like differentiation.

b)

ker⁡T\ker T: T(p)=0T(p)=0 means p(x+1)=p(x)p(x+1)=p(x) for all xx, which a non-constant polynomial cannot do (it would take the same value infinitely many times). So ker⁡T={constant polynomials}\ker T=\{\text{constant polynomials}\}, with basis {1}\{1\} and dimension 1. im⁡T\operatorname{im}T is spanned by T(1)=0T(1)=0, T(x)=1T(x)=1 and T(x2)=2x+1T(x^{2})=2x+1, hence by {1, 2x+1}\{1,\ 2x+1\}, that is by {1,x}\{1,x\}: it is P1P_{1}, of dimension 2. Rank theorem: 1+2=3=dim⁡P21+2=3=\dim P_{2}.

c)

M2=(002000000),M3=0M^{2}=\begin{pmatrix} 0&0&2\\ 0&0&0\\ 0&0&0\end{pmatrix},\qquad M^{3}=0. Interpretation: the first difference of a polynomial of degree 2 has degree 1, the second difference is a constant (22 for x2x^{2}), and the third difference is zero. This is exactly what happens with successive derivatives, and it is the principle behind finite difference tables. The operator is said to be nilpotent of index 3.

d)

MM is upper triangular with an all-zero diagonal, so its characteristic polynomial is det⁡(M−λI)=−λ3\det(M-\lambda I)=-\lambda^{3} and the only eigenvalue is λ=0\lambda=0, with algebraic multiplicity 3. The corresponding eigenspace is ker⁡(M−0⋅I)=ker⁡M\ker(M-0\cdot I)=\ker M, which has dimension 1 by b). The geometric multiplicity (1) is strictly less than the algebraic multiplicity (3): TT is not diagonalizable. This is no accident: a non-zero nilpotent operator can never be diagonalizable, since if it were, its diagonal form would be zero, and so would the operator itself.

Part D: Diagonalization and complex numbers (/25)

Exercise 10: Diagonalize, raise to a power, then take a square root

Let A=(3122)A=\begin{pmatrix} 3&1\\ 2&2\end{pmatrix}. Diagonalization is not only for computing powers: it also makes it possible to define operations that could not be carried out directly on the matrix.

  • a) Compute the characteristic polynomial of AA and its eigenvalues. Check them with the trace and the determinant.
  • b) Give an eigenvector for each eigenvalue, then matrices PP and DD such that A=PDP−1A=PDP^{-1}. Give P−1P^{-1}.
  • c) Derive an explicit formula for AnA^{n}, and check it for n=1n=1.
  • d) Find a matrix RR with real entries such that R2=AR^{2}=A. How many such real matrices are there, and why?

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  • a) λ=1\lambda=1 and λ=4\lambda=4
  • b) P=(11−21)P=\begin{pmatrix}1&1\\-2&1\end{pmatrix}, D=diag⁡(1,4)D=\operatorname{diag}(1,4)
  • c) An=13(1+2⋅4n−1+4n−2+2⋅4n2+4n)A^n=\frac13\begin{pmatrix}1+2\cdot 4^n&-1+4^n\\-2+2\cdot 4^n&2+4^n\end{pmatrix}
  • d) R=13(5124)R=\frac13\begin{pmatrix}5&1\\2&4\end{pmatrix}; 4 real square roots
a)

det⁡(A−λI)=∣3−λ122−λ∣=(3−λ)(2−λ)−2=λ2−5λ+4=(λ−1)(λ−4)\det(A-\lambda I)=\begin{vmatrix}3-\lambda&1\\ 2&2-\lambda\end{vmatrix}=(3-\lambda)(2-\lambda)-2=\lambda^{2}-5\lambda+4=(\lambda-1)(\lambda-4). The eigenvalues are λ1=1\lambda_{1}=1 and λ2=4\lambda_{2}=4. Check: their sum is 5, the trace of AA; their product is 4, the determinant of AA (6−2=46-2=4).

b)

For λ=1\lambda=1: (A−I)v⃗=0⃗(A-I)\vec{v}=\vec{0} gives 2v1+v2=02v_{1}+v_{2}=0, hence v⃗1=(1,−2)\vec{v}_{1}=(1,-2). For λ=4\lambda=4: −v1+v2=0-v_{1}+v_{2}=0, hence v⃗2=(1,1)\vec{v}_{2}=(1,1). The two eigenvalues are distinct, so the eigenvectors are independent and AA is diagonalizable: P=(11−21),D=(1004),P−1=13(1−121)P=\begin{pmatrix}1&1\\ -2&1\end{pmatrix},\quad D=\begin{pmatrix}1&0\\ 0&4\end{pmatrix},\quad P^{-1}=\frac{1}{3}\begin{pmatrix}1&-1\\ 2&1\end{pmatrix}, since det⁡P=3\det P=3.

c)

An=PDnP−1A^{n}=PD^{n}P^{-1} with Dn=(1004n)D^{n}=\begin{pmatrix}1&0\\ 0&4^{n}\end{pmatrix}, which gives An=13(1+2⋅4n−1+4n−2+2⋅4n2+4n)A^{n}=\frac{1}{3}\begin{pmatrix} 1+2\cdot 4^{n}&-1+4^{n}\\ -2+2\cdot 4^{n}&2+4^{n}\end{pmatrix}. For n=1n=1: 13(9366)=(3122)=A\frac{1}{3}\begin{pmatrix}9&3\\ 6&6\end{pmatrix}=\begin{pmatrix}3&1\\ 2&2\end{pmatrix}=A. For n=3n=3 we would get 13(1296312666)=(43214222)\frac{1}{3}\begin{pmatrix}129&63\\ 126&66\end{pmatrix}=\begin{pmatrix}43&21\\ 42&22\end{pmatrix}.

d)

The idea: if A=PDP−1A=PDP^{-1}, then R=PDP−1R=P\sqrt{D}P^{-1} works, where D\sqrt{D} is the diagonal matrix of the square roots. Here R=P(1002)P−1=13(5124)R=P\begin{pmatrix}1&0\\ 0&2\end{pmatrix}P^{-1}=\frac{1}{3}\begin{pmatrix}5&1\\ 2&4\end{pmatrix}. Check: R2=19(2791818)=(3122)=AR^{2}=\frac{1}{9}\begin{pmatrix}27&9\\ 18&18\end{pmatrix}=\begin{pmatrix}3&1\\ 2&2\end{pmatrix}=A. As for how many: since each eigenvalue of DD is strictly positive, we can choose ±1\pm\sqrt{1} and ±4\pm\sqrt{4} independently, which gives 2×2=42\times 2=4 real matrices RR such that R2=AR^{2}=A (including −R-R, the negative of the one computed). Had one eigenvalue been negative, no real diagonalizable square root would have existed.

-3-2-11234-4-3-2-11234λ = 4λ = 1xA³x

Exercise 11: Complex numbers: exponential form, De Moivre and roots

The exponential form turns powers and roots into simple operations on the argument. Each of the four questions becomes trivial once the right change of form is made, and tedious without it.

  • a) Write z=1+i31−iz=\dfrac{1+i\sqrt{3}}{1-i} in exponential form. Give its standard form a+bia+bi as well.
  • b) Compute z12z^{12} using De Moivre's theorem.
  • c) Solve w3=−8iw^{3}=-8i in C\mathbb{C}, and give the three solutions in standard form.
  • d) Solve z4−2z2+4=0z^{4}-2z^{2}+4=0 in C\mathbb{C}. Give the common modulus of the four roots and their arguments.

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  • a) 2 ei 7π/12\sqrt2\,e^{i\,7\pi/12}
  • b) z12=−64z^{12}=-64
  • c) 3−i\sqrt3-i, 2i2i, −3−i-\sqrt3-i
  • d) Modulus 2\sqrt2, arguments ±π6\pm\frac\pi6, ±5π6\pm\frac{5\pi}{6}
a)

Treat the numerator and the denominator separately. ∣1+i3∣=1+3=2\left|1+i\sqrt{3}\right|=\sqrt{1+3}=2 and its argument is π3\frac{\pi}{3} (positive real and imaginary parts, tan⁡=3\tan=\sqrt{3}). ∣1−i∣=2|1-i|=\sqrt{2} and its argument is −π4-\frac{\pi}{4}. The quotient has modulus 22=2\dfrac{2}{\sqrt{2}}=\sqrt{2} and argument π3−(−π4)=7π12\dfrac{\pi}{3}-\left(-\dfrac{\pi}{4}\right)=\dfrac{7\pi}{12}: z=2 ei 7π/12z=\sqrt{2}\,e^{i\,7\pi/12}. Standard form, multiplying by the conjugate of the denominator: z=(1+i3)(1+i)2=1−32+i 1+32≈−0.366+1.366 iz=\dfrac{\left(1+i\sqrt{3}\right)(1+i)}{2}=\dfrac{1-\sqrt{3}}{2}+i\,\dfrac{1+\sqrt{3}}{2}\approx-0.366+1.366\,i.

b)

De Moivre: z12=(2)12ei⋅12⋅7π/12=26ei 7πz^{12}=\left(\sqrt{2}\right)^{12}e^{i\cdot 12\cdot 7\pi/12}=2^{6}e^{i\,7\pi}. Since 7π=π+6π≡π(mod2π)7\pi=\pi+6\pi\equiv\pi\pmod{2\pi}, we have ei7π=eiπ=−1e^{i7\pi}=e^{i\pi}=-1, so z12=−64z^{12}=-64. The result is real: the 12 successive rotations land exactly on the negative real axis.

c)

−8i-8i has modulus 8 and argument −π2-\dfrac{\pi}{2}. The cube roots have modulus 83=2\sqrt[3]{8}=2 and arguments −π/2+2kπ3\dfrac{-\pi/2+2k\pi}{3} for k=0,1,2k=0,1,2, that is −π6-\dfrac{\pi}{6}, π2\dfrac{\pi}{2} and 7π6\dfrac{7\pi}{6}. In standard form: w0=2e−iπ/6=3−i,w1=2eiπ/2=2i,w2=2ei 7π/6=−3−iw_{0}=2e^{-i\pi/6}=\sqrt{3}-i,\quad w_{1}=2e^{i\pi/2}=2i,\quad w_{2}=2e^{i\,7\pi/6}=-\sqrt{3}-i. Immediate check on the second one: (2i)3=8i3=−8i(2i)^{3}=8i^{3}=-8i. The three roots are evenly spaced, 2π3\frac{2\pi}{3} apart, on the circle of radius 2.

d)

This is a biquadratic equation, a quadratic in z2z^{2}: set u=z2u=z^{2}, so u2−2u+4=0u^{2}-2u+4=0 and u=2±4−162=1±i3u=\dfrac{2\pm\sqrt{4-16}}{2}=1\pm i\sqrt{3}. These two values have modulus 2 and arguments ±π3\pm\dfrac{\pi}{3}. It remains to take square roots: the modulus becomes 2\sqrt{2} and the arguments are ±π6\pm\dfrac{\pi}{6} and ±π6+π\pm\dfrac{\pi}{6}+\pi. The four roots are therefore 2 eiπ/6,2 ei 5π/6,2 e−iπ/6,2 e−i 5π/6\sqrt{2}\,e^{i\pi/6},\quad\sqrt{2}\,e^{i\,5\pi/6},\quad\sqrt{2}\,e^{-i\pi/6},\quad\sqrt{2}\,e^{-i\,5\pi/6}, all of modulus 2\sqrt{2}. They form a rectangle centred at the origin, not a square: the roots of an equation with real coefficients come in conjugate pairs, but here they are not the roots of one single number.

Exercise 12: Synthesis problem: a permutation matrix is a rotation

Let R=(001100010)R=\begin{pmatrix} 0&0&1\\ 1&0&0\\ 0&1&0\end{pmatrix}, the matrix that sends (x,y,z)(x,y,z) to (z,x,y)(z,x,y): it permutes the coordinates cyclically. This problem shows that a purely combinatorial object is in fact a rotation of space, and it calls on the dot product, the determinant, eigenvalues and complex numbers.

  • a) Check that RTR=IR^{T}R=I and compute det⁡R\det R. What can you conclude about the geometric nature of RR?
  • b) Find the axis of this rotation, that is, the set of fixed vectors.
  • c) The angle θ\theta of a rotation of space satisfies tr⁡(R)=1+2cos⁡θ\operatorname{tr}(R)=1+2\cos\theta. Deduce θ\theta.
  • d) Compute R3R^{3} and explain the result using b) and c). Finally, give the three complex eigenvalues of RR and check that their sum equals the trace.

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a)
b)
c)
d)
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Answers

  • a) Orthogonal, det⁡=1\det=1: a rotation
  • b) Axis (1,1,1)(1,1,1)
  • c) θ=120∘\theta=120^\circ
  • d) R3=IR^3=I; eigenvalues 11, e±2iπ/3e^{\pm 2i\pi/3}, sum 0
a)

The columns of RR are (0,1,0)(0,1,0), (0,0,1)(0,0,1) and (1,0,0)(1,0,0): they are unit vectors and pairwise orthogonal, so RTR=IR^{T}R=I and RR is orthogonal. Its determinant is det⁡R=1\det R=1 (going from the identity to RR takes two column swaps, that is two sign changes). An orthogonal matrix with determinant +1+1 in dimension 3 is a rotation about an axis through the origin: RR preserves lengths, angles and orientation.

b)

We look for v⃗\vec{v} with Rv⃗=v⃗R\vec{v}=\vec{v}, that is, the eigenspace for the eigenvalue 1. With v⃗=(x,y,z)\vec{v}=(x,y,z), the equation Rv⃗=(z,x,y)=(x,y,z)R\vec{v}=(z,x,y)=(x,y,z) gives z=xz=x, x=yx=y and y=zy=z, so x=y=zx=y=z. The axis is the line spanned by (1,1,1)(1,1,1), the main diagonal of the unit cube. This makes sense: permuting the coordinates cyclically amounts to turning the cube about that diagonal.

c)

tr⁡(R)=0+0+0=0\operatorname{tr}(R)=0+0+0=0, so 1+2cos⁡θ=01+2\cos\theta=0, hence cos⁡θ=−12\cos\theta=-\dfrac{1}{2} and θ=120∘\theta=120^{\circ} (that is 2π3\frac{2\pi}{3}). A third of a turn about the main diagonal does exchange the three coordinate axes among themselves.

d)

R3=IR^{3}=I: applying the cyclic permutation three times puts every coordinate back in its place. This is exactly what b) and c) predicted: three rotations of 120∘120^{\circ} about the same axis make a full turn. For the eigenvalues, the characteristic polynomial is det⁡(R−λI)=−λ3+1\det(R-\lambda I)=-\lambda^{3}+1, whose roots are the three cube roots of unity: λ1=1,λ2=e2iπ/3,λ3=e−2iπ/3\lambda_{1}=1,\qquad\lambda_{2}=e^{2i\pi/3},\qquad\lambda_{3}=e^{-2i\pi/3}. Their sum is 1+2cos⁡2π3=1−1=01+2\cos\frac{2\pi}{3}=1-1=0, which is indeed the trace. Along the way, the angle of the rotation reappears in the argument of the complex eigenvalues: a rotation through the angle θ\theta in dimension 3 always has eigenvalues 11 and e±iθe^{\pm i\theta}.

e₁e₂e₃axis x = y = z
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