Frobenius norm of product of matrix The Frobenius norm of a $m \times n$ matrix $F$ is defined as
$$\| F \|_F^2 := \sum_{i=1}^m\sum_{j=1}^n |f_{i,j}|^2$$
If I have $FG$, where $G$ is a $n \times p$ matrix, can we say the following?
$$\| F G \|_F^2 = \|F\|_F^2 \|G\|_F^2$$
Also, what does Frobenius norm mean? Is it analogous to the magnitude of a vector, but for matrix? 
 A: Actually there is
$$||FG||^2_F \leqslant||F||^2_F||G||^2_F$$
The proof is as follows.
\begin{align}
\|FG\|^2_F&=\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{p}\left|\sum\limits_{k=1}^nf_{ik}g_{kj}\right|^2
\\
&\leqslant\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{p}\left(\sum\limits_{k=1}^n|f_{ik}|^2\sum\limits_{k=1}^n|g_{kj}|^2\right)\tag{Cauchy-Schwarz}
\\
&=\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{p}\left(\sum\limits_{k,l=1}^n|f_{ik}|^2|g_{lj}|^2\right)
\\
&=\sum\limits_{i=1}^{m}\sum\limits_{k=1}^{n}|f_{ik}|^2\sum\limits_{l=1}^{n}\sum\limits_{j=1}^{p}|g_{lj}|^2
\\
&=\|F\|^2_F\|G\|^2_F
\end{align}
Frobenius norm is like vector norm and similar to $l_2$.
A: Let $A$ be $m \times r$ and $B$ be $r \times n$. A better bound here is
$$
\| A B \|_F \le \|A\| \|B\|_F \quad (*)
$$
where $\|A\|$ is the $\ell_2$ operator norm:
$$
\|A\| = \max_{\|x\|_2\, \le\,  1}   \|A x\|_2.
$$
It is also equal to the largest singular value of A. From this definition $\|A x\|_2 \le \|A\| \|x\|_2$ for any vector $x$ in $\mathbb R^r.$
Since $\|A\| \le \|A\|_F$, inequality (*) is a strictly better inequality than the sub-multiplicative inequality for the Frobenius norm.
To see the inequality, let $B = [b_1 \mid b_2 \mid \cdots \mid b_n]$ be the column decomposition of $B$. Then, $A B = [Ab_1 \mid A b_2 \mid \dots \mid Ab_n]$ is the column decomposition of $AB$. It follows that
\begin{align*}
  \| A B \|_F^2 = \sum_{j=1}^n \|A b_j\|^2 \le \|A\|^2 \sum_j \|b_j\|^2 = \|A\|^2 \|B\|_F^2.
\end{align*}

EDIT in response to the question in the comments, ``Is there a lower bound  for the Frobenius norm of the product of two matrices?''. In general, no, except for the obvious lower bound of zero. Consider the following two matrices
\begin{align}
A = \begin{pmatrix}
a & b \\ 0 & 0
\end{pmatrix}, \quad 
B = \begin{pmatrix}
-b & 0 \\ a & 0
\end{pmatrix}.
\end{align} 
Then $\|A\|_F = \|B\|_F = \sqrt{a^2 + b^2}$, while $\|AB\|_F = 0$.
What if the two matrices are symmetric? Consider 
\begin{align}
A = \begin{pmatrix}
a & b \\ b & a
\end{pmatrix}, \quad 
B = \begin{pmatrix}
-b & a \\ a & -b
\end{pmatrix}, \quad 
A B = \begin{pmatrix}
0 & a^2-b^2 \\ a^2-b^2 & 0
\end{pmatrix}.
\end{align} 
Then, $\|A\|_F^2 = \|B\|_F^2 = 2(a^2 + b^2)$ while $\|AB\|_F^2 = 2(a^2 - b^2)^2$ which can be made arbitrarily smaller than either of $\|A\|_F^2$ or $\|B\|_F^2$. For example, take $a=b$.
A: We will prove that $$ \Vert FG \Vert_f^2 \leq \Vert F \Vert_f^2 \cdot \Vert G \Vert_f^2.$$ We have $$\Vert FG \Vert_f^2 = \mathsf{Tr}(FG  G^TF^T)  = \mathsf{Tr} (F^TFGG^T),$$ by the cyclic property of trace function. To prove the theorem, it is enough if we show that $$\mathsf{Tr}(F^TFGG^T) \leq \mathsf{Tr}(F^TF) \mathsf{Tr}(GG^T).$$ Observe that $F^TF$ and $GG^T$ are positive semidefinite and symmetric matrices. Hence, they are diagonalizable and can be written as $$ F^TF = U\Sigma_F U^\dagger$$ $$GG^T = V\Sigma_G V^\dagger,$$ where $U,V$ are unitary matrices and $\Sigma_F,\Sigma_G$ are diagonal matrices with eigenvalues (non-negative) of $F^TF$ and $GG^T$ respectively as diagonal entries. Again, by using the cyclic property of trace function, we can write the left hand side as $$\mathsf{Tr}(F^TFGG^T) = \mathsf{Tr}(U\Sigma_F U^\dagger V\Sigma_G V^\dagger) = \mathsf{Tr}(V^\dagger U\Sigma_F U^\dagger V\Sigma_G ).$$ 
It is easy to show the following properties of diagonal matrices: Let $D$ be a diagonal matrix with non-negative diagonal entries. 
1) Under any unitary transformation of $D$, the resulting matrix has non-negative diagonal entries. 
2) If $M$ is a square matrix with non-negative diagonal entries, then $\mathsf{Tr}(MD)\leq \mathsf{Tr}(M) \cdot \mathsf{Tr} (D)$. 
The above properties directly imply that $$\mathsf{Tr}(V^\dagger U\Sigma_F U^\dagger V\Sigma_G ) \leq \mathsf{Tr}(V^\dagger U\Sigma_F U^\dagger V) \cdot \mathsf{Tr}( \Sigma_G ) =\mathsf{Tr}( \Sigma_F ) \cdot \mathsf{Tr}( \Sigma_G ) = \mathsf{Tr}(F^TF) \mathsf{Tr}(GG^T),$$ where the last two equalities follow from the fact that trace is preserved under unitary transformation. 
A: In case anyone is curious, there is also a lower bound in a form similar to @passerby51's answer. This result is also used in an ICLR paper, which may be very useful. Let $\mathbf{A}$ be $m \times r$ and $\mathbf{B}$ be $r\times n$. The bounds are
$$\sigma_{\min }(\mathbf{A})\|\mathbf{B}\|_{F} \leq \|\mathbf{A B}\|_{F} \leq \sigma_{\max }(\mathbf{A})\|\mathbf{B}\|_{F},$$
where $\sigma_\min$ and $\sigma_\max$ denote the minimum and maximum singular value, respectively.
To prove it, we start with the definition of Frobenius norm,
$$
\|\mathbf{A B}\|_{F}^{2}=\operatorname{trace}\left(\mathbf{A B} \mathbf{B}^{\top} \mathbf{A}^{\top}\right)=\operatorname{trace}\left(\mathbf{A}^{\top} \mathbf{A B} \mathbf{B}^{\top}\right)
$$
By using the inequalities for matrix trace (see reference below or here), i.e., $ \lambda_{\min }(A) \operatorname{tr}(B) \leq \operatorname{tr}(A B) \leq \lambda_{\max }(A) \operatorname{tr}(B)$, we have
$$
\lambda_{\min }\left(\mathbf{A}^{\top} \mathbf{A}\right) \operatorname{trace}\left(\mathbf{B} \mathbf{B}^{\top}\right) \leq \operatorname{trace}\left(\mathbf{A}^{\top} \mathbf{A B} \mathbf{B}^{\top}\right) \leq \lambda_{\max }\left(\mathbf{A}^{\top} \mathbf{A}\right) \operatorname{trace}\left(\mathbf{B} \mathbf{B}^{\top}\right)
$$
where $\lambda_\min$ and $\lambda_\max$ denote the minimum and maximum eigenvalues, respectively.
This implies,
$$
\sigma_{\min }^2(\mathbf{A})\|\mathbf{B}\|_{F}^2 \leq \|\mathbf{A B}\|_{F}^2 \leq \sigma_{\max }^2(\mathbf{A})\|\mathbf{B}\|_{F}^2.
$$
Using the fact that all items here are non-negative, we can complete the proof.
Reference:
Y. Fang, et al., Inequalities for the trace of matrix product.
