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MIT 18.06 Problem Set 3

Column and null spaces, the rank of a matrix and deciding whether a system is solvable.

1. MIT 18.06 Problem Set 3 — how this assessment works

This lesson reproduces the Problem Set “MIT 18.06 Problem Set 3” from MIT OpenCourseWare 18.06 Linear Algebra (Spring 2010) (https://ocw.mit.edu/courses/18-06-linear-algebra-spring-2010/), licensed under CC BY-NC-SA 4.0. Work through 14 problems in order: type your reasoning in the answer box or write it on the handwriting pad, then submit for AI grading.

How the grading works Every problem is graded by an AI mathematics teacher that reads the official solution shipped with the course. It checks your work criterion by criterion, awards partial credit for correct steps, and returns written feedback. Equivalent mathematics and valid alternative methods are accepted — a different route to the same result can still earn full marks.

If your answer is incomplete, the feedback names exactly which steps are missing, so you can correct them and try again.

2. MIT 18.06 PSET3 Problem 1 — Parameterizing the plane $x - 3y - z = 12$ (4 pts)

The plane x−3y−z=12x-3y-z=12 is parallel to the plane x−3y−z=0x-3y-z=0 in Problem 17. One particular point on this plane is (12,0,0)(12, 0, 0). All points on the plane have the form (fill in the first components)

[xyz]=[00]+y[10]+z[01].[ \begin{array}{c} x \\ y \\ z \end{array} ]=[ \begin{array}{c} \\ 0 \\ 0 \end{array} ]+y [ \begin{array}{c} \\ 1 \\ 0 \end{array} ]+z [ \begin{array}{c} \\ 0 \\ 1 \end{array} ].

3. MIT 18.06 PSET3 Problem 2 — A matrix with prescribed column space and nullspace (4 pts)

(If possible...) Construct a matrix whose column space contains (1,1,0)(1, 1, 0) and (0,1,1)(0, 1, 1) and whose nullspace contains (1,0,1)(1, 0, 1) and (0,0,1)(0, 0, 1).

4. MIT 18.06 PSET3 Problem 3 — Nullspace of $C = \begin{bmatrix} A \\ B \end{bmatrix}$ (12 pts)

How is the nullspace N(C)\mathbf{N}(C) related to the spaces N(A)\mathbf{N}(A) and N(B)\mathbf{N}(B), if C=[AB]C=\begin{bmatrix} A \\ B \end{bmatrix}?

5. MIT 18.06 PSET3 Problem 4 — Kirchhoff's Law: node equations and special solutions (12 pts)

Kirchhoff's Law says that \emph{current in=current out} at every node. This network has six currents y1,…,y6y_1, \ldots, y_6 (the arrows show the positive direction, each yiy_i could be positive or negative). Find the four equations Ay=0A\mathbf{y}=0 for Kirchhoff's Law at the four nodes. Find three special solutions in the nullspace of AA.

6. MIT 18.06 PSET3 Problem 5 — $AB = I$ implies $A$ is invertible (4 pts)

Suppose AA and BB are nn by nn matrices, and AB=IAB=I. Prove from rank⁡(AB)≤rank⁡(A)\operatorname{rank}(AB) \leq \operatorname{rank}(A) that the rank of AA is nn. So AA is invertible and BB must be its two-sided inverse (Section 2.5). Therefore BA=IBA=I (which is not so obvious!).

7. MIT 18.06 PSET3 Problem 6 — Factor $A$ as (pivot columns)($R$'s first $r$ rows) (4 pts)

\emph{Neat fact} \textbf{Every} mm \textbf{by} nn \textbf{matrix of rank} rr \textbf{reduces to (}mm \textbf{by} rr\textbf{) times (}rr \textbf{by} nn\textbf{):}

A=(pivot columns of A)(first r rows of R)=(COL)(ROW).A=(\text{pivot columns of } A)(\text{first } r \text{ rows of } R)=(\mathbf{COL})(\mathbf{ROW}).

Write the 3 by 4 matrix AA in equation (1) at the start of this section as the product of the 3 by 2 matrix from the pivot columns and the 2 by 4 matrix from RR.

8. MIT 18.06 PSET3 Problem 7 — Block shapes: right/left inverses, $\operatorname{rref}(R^T)$, $\operatorname{rref}(R^T R)$ (12 pts)

Suppose RR is mm by nn of rank rr, with pivot columns first:

[IF00][ \begin{array}{cc} I & F \\ 0 & 0 \end{array} ]

(a) What are the shapes of those four blocks?

(b) Find a \emph{right-inverse} BB with RB=IRB=I if r=mr=m.

(c) Find a \emph{left-inverse} CC with CR=ICR=I if r=nr=n.

(d) What is the reduced row echelon form of RTR^T (with shapes)?

(e) What is the reduced row echelon form of RTRR^T R (with shapes)?

Prove that RTRR^T R has the same nullspace as RR. Later we show that ATAA^T A always has the same nullspace as AA (a valuable fact).

9. MIT 18.06 PSET3 Problem 8 — Row-and-column reduced form (12 pts)

Suppose you allow elementary \emph{column} operations on AA as well as elementary row operations (which get to RR). What is the “row-and-column reduced form” for an mm by nn matrix of rank rr?

10. MIT 18.06 PSET3 Problem 9 — (Optional) $\operatorname{rank}(AB) \leq \operatorname{rank}(B)$ and rank-destroying multipliers

(§3.3, #17 – Optional) (a) Suppose column jj of BB is a combination of previous columns of BB. Show that column jj of ABAB is the same combination of previous column of ABAB. Then ABAB cannot have new pivot columns, so rank⁡(AB)≤rank⁡(B)\operatorname{rank}(AB) \leq \operatorname{rank}(B).

(b) Find A1A_1 and A2A_2 so that rank⁡(A1B)=1\operatorname{rank}(A_1 B)=1 and rank⁡(A2B)=0\operatorname{rank}(A_2 B)=0 for B=[1111]B=\begin{bmatrix} 1 & 1 \\ 1 & 1 \end{bmatrix}.

11. MIT 18.06 PSET3 Problem 10 — Four false statements about $\mathbf{x}_p$ and $\mathbf{x}_n$ (4 pts)

Explain why these are all false:

(a) The complete solution is any linear combination of xp\mathbf{x}_p and xn\mathbf{x}_n.

(b) A system Ax=bA\mathbf{x}=\mathbf{b} has at most one particular solution.

(c) The solution xp\mathbf{x}_p with all free variables zero is the shortest solution (minimum length ∥x∥\|x\|). Find a 2 by 2 counterexample.

(d) If AA is invertible there is no solution xn\mathbf{x}_n in the nullspace.

12. MIT 18.06 PSET3 Problem 11 — Rank relations for the four solution cases (4 pts)

Write down all known relations between rr and mm and nn if Ax=bA\mathbf{x}=\mathbf{b} has (a) no solution for some b\mathbf{b} (b) infinitely many solutions for every b\mathbf{b} (c) exactly one solution for some b\mathbf{b}, no solution for other b\mathbf{b} (d) exactly one solution for every b\mathbf{b}.

13. MIT 18.06 PSET3 Problem 12 — Gauss-Jordan to $R$: finding $\mathbf{x}_n$ and $\mathbf{x}_p$ (4 pts)

Apply Gauss-Jordan elimination to Ux=0U\mathbf{x}=0 and Ux=cU\mathbf{x}=\mathbf{c}. Reach Rx=0R\mathbf{x}=0 and Rx=dR\mathbf{x}=\mathbf{d}:

[U0]=[12300040]and[Uc]=[12350048].[U \quad 0]=\begin{bmatrix} 1 & 2 & 3 & \mathbf{0} \\ 0 & 0 & 4 & \mathbf{0} \end{bmatrix} \qquad \text{and} \qquad [U \quad \mathbf{c}]=\begin{bmatrix} 1 & 2 & 3 & \mathbf{5} \\ 0 & 0 & 4 & \mathbf{8} \end{bmatrix}.

Solve Rx=0R\mathbf{x}=0 to find xn\mathbf{x}_n (its free variable is x2=1x_2=1). Solve Rx=dR\mathbf{x}=\mathbf{d} to find xp\mathbf{x}_p (its free variable is x2=0x_2=0).

14. MIT 18.06 PSET3 Problem 13 — Solving $K\mathbf{x} = (10, \ldots, 10)$: a discrete parabola (12 pts)

Suppose KK is the 9 by 9 second difference matrix (2's on the diagonal,-1's on the diagonal above and also below). Solve the equation Kx=b=(10,…,10)K\mathbf{x}=\mathbf{b}=(10, \ldots, 10). If you graph x1,…,x9x_1, \ldots, x_9 above the points 1,…,91, \ldots, 9 on the xx axis, I think the nine points fall on a parabola.

15. MIT 18.06 PSET3 Problem 14 — Same solutions for every $\mathbf{b}$ implies $A = C$ (12 pts)

Suppose Ax=bA\mathbf{x}=\mathbf{b} and Cx=bC\mathbf{x}=\mathbf{b} have the same (complete) solutions for every b\mathbf{b}. Is it true that A=CA=C?

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