We first show that the span of \(\{p_1,p_2,p_3\}\) is \(P_2\) To this end, let \(a,b,c\in \mathbb{R}\). We need to show that there exist \(\alpha,\beta,\gamma\in \mathbb{R}\) such that \[\alpha p_1 + \beta p_2 + \gamma p_3 = ax^2 + bx + c.\]

Simplifying the left-hand side gives \[ \gamma x^2 + (-\alpha + \beta)x + (\alpha +2\beta + \gamma) = ax^2 + bx + c.\]

Comparing coefficients on both sides, we get \begin{eqnarray*} \gamma & = & a \\ -\alpha + \beta & = & b \\ \alpha + 2\beta + \gamma & = & c. \\ \end{eqnarray*} Adding the last two equations gives \(3\beta + \gamma = b+c\). Since \(\gamma = a\), we have \(\beta = \frac{-a+b+c}{3}\). Hence, \(\alpha = \frac{-a - 2b + c}{3}\).

To obtain \(x^2 + 2x\), we need to set \(a = 1\), \(b=2\), and \(c=0\). Therefore, \(x^2 + 2x = -\frac{5}{3}p_1 + \frac{1}{3}p_2 + p_3\).

Note that to get the identically zero polynomial, one needs \(a = b = c = 0\). But this forces \(\alpha = \beta = \gamma = 0\). Thus, \(\{p_1,p_2,p_3\}\) is a linearly independent set and is therefore a basis for \(P_2\).

Note that \(\begin{bmatrix} 1\\1\\1\end{bmatrix}\) is in the column space of \(A\) if and only if there exist real numbers \(\alpha\) and \(\beta\) such that \[ \begin{bmatrix} 1\\1\\1\end{bmatrix} = \alpha \begin{bmatrix} 1 \\ 0 \\ -1\end{bmatrix} + \beta \begin{bmatrix} 1 \\ 2 \\ 0\end{bmatrix}, \] or equivalently, \[ \begin{bmatrix} 1\\1\\1\end{bmatrix} = \begin{bmatrix} \alpha + \beta \\ 2\beta \\ -\alpha\end{bmatrix}. \] Comparing the second and third entries of both sides, we must have \(\alpha = -1\) and \(\beta = \frac{1}{2}\). However, \(\alpha + \beta \neq 1\). Hence, there do not exist \(\alpha\) and \(\beta\) such that the two sides are equal. Therefore, \(\begin{bmatrix} 1\\1\\1\end{bmatrix}\) is not in the column space of \(A\).

The RREF of \(A\) is \(R = \begin{bmatrix} 1 & 0 & 0 & -1\\ 0 & 1 & 0 & -\frac{1}{2}\\ 0 & 0 & 1 & \frac{1}{2} \end{bmatrix}\). Since each column of \(R^\mathsf{T}\) contains a pivot, all three columns of \(A^\mathsf{T}\) (or \(R^\mathsf{T}\)) give a basis for \({\cal R}(A)\).

Since only the first three columns of \(R\) are pivot columns, the first three columns of the original matrix \(A\) give a basis for \({\cal C}(A)\).

Since the fourth column of \(R\) is the only non-pivot column, all the solutions to the system \(Ax = 0\) are of the form \(\begin{bmatrix} x_1\\x_2\\x_3\\x_4\end{bmatrix} =\begin{bmatrix} s \\ \frac{s}{2} \\ -\frac{s}{2} \\ s\end{bmatrix} =s\begin{bmatrix} 1 \\ \frac{1}{2} \\ -\frac{1}{2} \\ 1\end{bmatrix}\) where \(s \in \mathbb{R}\). Hence, a basis for \(N(A)\) is given by the single vector \(\begin{bmatrix} 1 \\ \frac{1}{2} \\ -\frac{1}{2} \\ 1\end{bmatrix}\).

Using the rank-nullity theorem, we have \(\operatorname{rank}(A) + \operatorname{nullity}(A) = 4\). For a \(2\times 4\) matrix, the rank is at most \(2\) since in the RREF, there can be at most two pivots.

Thus \(2 + \operatorname{nullity}(A) \geq \operatorname{rank}(A) + \operatorname{nullity}(A)\), implying that \(2 + \operatorname{nullity}(A) \geq 4\). Hence, the nullity of \(A\) is at least \(2\). Note that the nullity can indeed equal 2 and \(A = \begin{bmatrix} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \end{bmatrix}\) is an example. Therefore, 2 is the smallest possible value for the nullity of \(A\).

First note that \(AB = 0\). Hence, every column of \(B\) is in \(N(A)\), implying that \({\cal C}(B)\) is a subspace of \(N(A)\). The RREF of \(B\) is \(\begin{bmatrix} 1 & 0 & -1 \\ 0 & 1 & 1\\0 & 0 & 0 \end{bmatrix}\). Hence, the rank of \(B\) is 2, meaning that the column space of \(B\) has dimension 2. The RREF of \(A\) is \(\begin{bmatrix} 1 & - 2 & 1\\0 & 0 & 0\end{bmatrix}\). Hence, the rank of \(A\) is 1 and by the rank-nullity theorem, the nullity of \(A\) is 2. Hence, the dimension of \({\cal C}(B)\) is the same as the dimension of \(N(A)\). Since the former is a subspace of the latter, they must be equal.

We first find all the solutions to \(Ax = 0\), where \(x = \begin{bmatrix} x_1\\ \vdots \\ x_5\end{bmatrix}\) by row-reducing \(A\): \begin{eqnarray*} \begin{bmatrix} 1 & 0 & 1 & 0 & 1 \\ 0 & 1 & 1 & 0 & 1\\ 0 & 1 & 0 & 1 & 1\\ \end{bmatrix} & \stackrel{R_2 \leftarrow R_2 + R_3}{\longrightarrow} \begin{bmatrix} 1 & 0 & 1 & 0 & 1 \\ 0 & 0 & 1 & 1 & 0 \\ 0 & 1 & 0 & 1 & 1\\ \end{bmatrix} \\ & \stackrel{R_2 \leftrightarrow R_3}{\longrightarrow} \begin{bmatrix} 1 & 0 & 1 & 0 & 1 \\ 0 & 1 & 0 & 1 & 1\\ 0 & 0 & 1 & 1 & 0 \\ \end{bmatrix} \\ & \stackrel{R_1 \leftrightarrow R_3}{\longrightarrow} \begin{bmatrix} 1 & 0 & 0 & 1 & 1 \\ 0 & 1 & 0 & 1 & 1\\ 0 & 0 & 1 & 1 & 0 \\ \end{bmatrix} \\ \end{eqnarray*} Since the fourth and fifth columns are the only nonpivot columns, the solutions to \(Ax = 0\), after setting \(x_4 = s\) and \(x_5 = t\), are given by \[\begin{bmatrix} s \\ s \\s \\s \\0\end{bmatrix} + \begin{bmatrix} t \\ t\\ 0 \\ 0 \\t \end{bmatrix} = s\begin{bmatrix} 1 \\ 1 \\1 \\1 \\0\end{bmatrix} + t\begin{bmatrix} 1 \\ 1\\ 0 \\ 0 \\1 \end{bmatrix} \] where \(s, t \in GF(2)\). Hence, a basis for \(N(A)\) is \(\left\{\begin{bmatrix} 1 \\ 1 \\1 \\1 \\0\end{bmatrix}, \begin{bmatrix} 1 \\ 1\\ 0 \\ 0 \\1 \end{bmatrix}\right \}\).

By definition, \(b \in {\cal C}(A)\) if and only if there exist scalars \(x_1,\ldots, x_n \in \mathbb{F}\) such that \[b = x_1 A_1 + \cdots + x_n A_n\] where \(A_j\) denotes the \(j\)th column of \(A\).

But \(x_1 A_1 + \cdots + x_n A_n\) can be written as \(A \begin{bmatrix} x_1\\ \vdots \\x_n\end{bmatrix}.\) Thus, \(b \in {\cal C}(A)\) if and only if the system \(Ax = b\) has a solution.

We need to write \(u\) as a linear combination of the elements in the ordered basis. Hence, we seek \(\alpha, \beta \in \mathbb{R}\) such that \[u = \alpha \begin{bmatrix} 1 \\ 2 \end{bmatrix}+ \beta \begin{bmatrix} 1 \\ 0 \end{bmatrix}.\] Then, \([u]_{\Gamma} = \begin{bmatrix} \alpha \\ \beta \end{bmatrix}.\)

Note that the equation can be rewritten as \[\begin{bmatrix} 0 \\ -2\end{bmatrix} = \begin{bmatrix} \alpha + \beta \\ 2\alpha \end{bmatrix}. \] Comparing the second components on both sides, we obtain \(\alpha = -1\). Thus, \(\beta = 1\). Hence, \([u]_{\Gamma} = \begin{bmatrix} -1 \\ 1\end{bmatrix}\).

We need to write \(u\) as a linear combination of the elements in the ordered basis. Hence, we seek \(\alpha, \beta, \gamma \in \mathbb{R}\) such that \[u = \alpha (x^2-1) + \beta (x+1) + \gamma x.\] Then, \([u]_{\Gamma} = \begin{bmatrix} \alpha \\ \beta \\ \gamma \end{bmatrix}.\)

Note that the equation can be rewritten as \[x-1 = \alpha x^2 + (\beta+\gamma)x + (-\alpha + \beta). \] Comparing coefficients on both sides, we get \begin{eqnarray*} 0 & = & \alpha \\ 1 & = & \beta + \gamma \\ -1 & = & -\alpha + \beta. \end{eqnarray*} Thus, \(\alpha = 0\). Then, from the third equation, we obtain that \(\beta = -1\). The second equation then gives \(\gamma = 1-\beta = 2\). Hence, \([u]_{\Gamma} = \begin{bmatrix} 0 \\ -1 \\ 2\end{bmatrix}\).

From the second equation, we see that \(b = c - 2d\). Substituting this into the first equation, we obtain \(a = -2c + d\). Hence, the subspace, call it \(W\), can be written as \(\left \{ \begin{bmatrix} -2c+d & c-2d \\ c & d \end{bmatrix} : c,d \in \mathbb{R}\right \}\).

Hence, every matrix in \(W\) can be written as \(c \begin{bmatrix} -2 & 1 \\ 1 & 0\end{bmatrix} + d\begin{bmatrix} 1 & -2 \\ 0 & 1\end{bmatrix}\). Thus, \(W\) is given by the span of \(\left\{ \begin{bmatrix} -2 & 1 \\ 1 & 0\end{bmatrix}, \begin{bmatrix} 1 & -2 \\ 0 & 1\end{bmatrix}\right\}\).

Since the two matrices are not scalar multiples of each other, they form a linearly independent set and thus give a basis for \(W\). Therefore, the dimension of \(W\) is 2.

To show that \(W\) is a proper subspace of \(\mathbb{R}^3\), we need to show that there exists some \(x \in \mathbb{R}^3\) that is not in \(W\).

However, we will not attempt to directly find such an \(x\). Instead, we show that the dimension of \(W\) is less than 3. Since the dimension of \(\mathbb{R}^3\) is 3, it follows that \(W\) cannot possibly be \(\mathbb{R}^3\).

Recall that the span of \(\{t,u,v,w\}\) is given by the set of linear combinations of \(t\), \(u\), \(v\), and \(w\). Note that if we let \(A\) be the matrix \([t~u~v~w]\), then the column space of \(A\) is precisely the set of linear combinations of \(t\), \(u\), \(v\), and \(w\). Thus, the dimension of the span of \(\{t,u,v,w\}\) is given by the rank of \(A\), which is the matrix \[\begin{bmatrix} 1 & 1 & -3 & 2\\ 0 &-1 & 2 & -1 \\ -2 & 2 & -2 & 0\end{bmatrix}.\] Row-reducing the above matrix to RREF, we obtain \[\begin{bmatrix} 1 & 0 & -1 & 1\\ 0 & 1 & -2 & 1 \\ 0 & 0 & 0 & 0\end{bmatrix}.\] Since there are only two pivot columns, the rank is \(2\).

Thus, is the dimension of the span of \(\{t,u,v,w\}\) is \(2\). Hence, \(W\) has dimension less than 3.