I'm aware of analogous threads; I hope that mine is specific enough not to be esteemed one.

$\mathbf{a^i}$ is a row vector. $A, B$ are matrices. Prove: $1$. $\mathbf{a^i}B$ is a linear combination of the rows of $B$.
$2.$ Row space of $AB \subseteq$ row space of $B$. $\qquad$ $3.$ Column space of $AB \subseteq$ Column space of $A$.
$4.$ If $\mathbf{a_i}$ is a column vector, then $A\mathbf{a_i}$ is a linear combination of the columns of $A$.
$5. \operatorname{rank}(A\color{#B8860B}{B}) \color{#B8860B}{\le} \operatorname{rank}\color{#B8860B}{B} \qquad \qquad$ $6.\operatorname{rank}(AB) \leq \operatorname{rank} A$.
In general, $x \leq a \text{ & } x \le b \implies x \le \min\{a, b\}$.
So by $5 \, \& \, 6$, $\operatorname{rank}(AB) \leq \min\{\operatorname{rank}A,\operatorname{rank} B\}$.

$\bbox[2px,border:2px solid grey]{\text{ Proof of #5 :}} \;$ The rank of a matrix is the dimension of its row space. Need to show :
If $\operatorname{rowsp}(AB) \subseteq\operatorname{rowsp}(B)$, then $\operatorname{dim rowspace}(AB) \le \operatorname{dim rowspace}(B). $
Pick a basis for $\operatorname{rowsp}(AB)$. Say there are $p$ vectors in this basis.
By $\#2$, row space of $AB \subseteq$ row space of $B$, $\color{green}{\text{so all of these $p$ vectors also $\in \operatorname{rowsp}(B)$}}$. Moreover, they must be linearly independent (hereafter dubbed l-ind).
${\Large{\color{red}{[}}} \;$ Since the dimension of a space $=$ the maximum number of l-ind vectors in that space, $\; {\Large{{\color{red}{]}}}}$
and $\color{green}{\text{$\operatorname{rowsp}(B)$ has $\ge p$ l-ind vectors}}$, thus $ \operatorname{dim rowspace}(B) \; \ge \; \operatorname{dim rowspace}(AB) = p. $

$\bbox[2px,border:2px solid grey]{\text{ Proof of #6 :}} \;$ Apply $ \operatorname{rank}M = \operatorname{rank}M^T$ and $\#5$: $ \operatorname{rank}(AB)^T = \operatorname{rank}(B^T\color{#B8860B}{A^T}) \quad \color{#B8860B}{\le} \quad \operatorname{rank}\color{#B8860B}{A^T} = \operatorname{rank}(A)$.

$Q1.$ Please elucidate the above proof of $5$? I'm bewildered. What's the strategy?

$Q2.$ On P209, Poole defines dimension as the number of vectors in a basis.
So shouldn't the red bracket refer to a basis? If so, why doesn't the proof simply declare:
By $2$, the basis for $\operatorname{rowsp}(AB)$ can be reused as a basis for $\operatorname{rowsp}(B).$ ?

$Q3.$ How'd one previse to invert $AB$ and apply $\#5$ (the key strategem) for #6?

$Q4.$ What's the intuition behind results $5$ and $6$? I'd be grateful for pictures.

Sources: P147, 4.48, Schaum's Outline to Lin Alg, web.mit.edu/18.06/www/Spring01/Sol-S01-5.ps

  • 1
    $\begingroup$ I think $p = \operatorname{dim rowspace}(B)$ is wrong. Instead, $p \le \operatorname{dim rowspace}(B)$. $\endgroup$ – NovaDenizen Dec 22 '13 at 14:16
  • $\begingroup$ @NovaDenizen Thank you! I emended my OP. Please let me know of any errors. $\endgroup$ – NNOX Apps Dec 29 '13 at 7:55

Whenever you have two subspaces $V$ and $W$ with $V \subseteq W$, the $\text{dim}(V) \le \text{dim}(W)$. The proof is pretty easy. Construct a basis $B = (v_1, ..., v_n)$ for $V$. Now extend this to a basis of $W$ so that $B' = (v_1, ..., v_n, w_1, ..., w_k)$. Since the number of basis vectors in $B$ is less than or equal to the number of basis vectors in $B'$, we have $\text{dim}(V) \le \text{dim}(W)$, by definition of "dimension" as the number of vectors in a basis for the space.

I've glossed over details, like the fact that you can extend the basis of $V$ to a basis of $W$, but this is easy to show or find elsewhere. I suggest Axler's book, "Linear Algebra Done Right."


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