Given a hermitian, but not necessarily positive, sparse matrix $C = (c_{ij}) \in \mathbb{C}^{n \times n}$ and $n \ggg 1$ ($n \approx 2^{100}$) with eigenvalues $\lambda_1 \le \lambda_2 \le \dots \le \lambda_n$.

Is there an upper bound for $\mathrm{tr} \sqrt{C^\dagger C} = \sum_i |\lambda_i| $? Preferable in terms of the dimension $n$, or the norm $\|C\|_\infty := \max_{1 \leq i, j \leq n} |c_{ij}|$, or the norm $\| C \|_F := \sqrt{\sum_{i=1}^m \sum_{j=1}^n |c_{ij} |^2 }$.

Essentially, I'd like to relate the trace or eigenvalues with something related to the dimension $n$ and the absolute values of the matrix elements $c_{ij}$.

The trivial solution would be $\mathrm{tr} \sqrt{C^\dagger C} \le \sum_i \|C\|_\infty = n \|C\|_\infty$, but n is huge, and I expect most matrix elements, and almost all diagonal elements, to be zero, thus it's not a very good solution.

So far my best shot is the result by Brauer (1946) (http://www.sciencedirect.com/science/article/pii/002437958090258X):

$|\lambda_n| \le \text{min} ( \text{max}_l \sum_i |c_{li}|, \text{max}_k \sum_j |c_{jk}|) \Rightarrow \mathrm{tr} \sqrt{C^\dagger C} \in \mathcal{O}(n^2)$

I think there might be some room for improvement.