  We consider the problem of multi-objective maximization of monotone
submodular functions subject to cardinality constraint, often formulated as
$\max_{|A|=k}\min_{i\in\{1,\dots,m\}}f_i(A)$. While it is widely known that
greedy methods work well for a single objective, the problem becomes much
harder with multiple objectives. In fact, Krause et al.\ (2008) showed that
when the number of objectives $m$ grows as the cardinality $k$ i.e.,
$m=\Omega(k)$, the problem is inapproximable (unless $P=NP$). On the other
hand, when $m$ is constant Chekuri et al.\ (2010) showed a randomized
$(1-1/e)-\epsilon$ approximation with runtime (number of queries to function
oracle) $n^{m/\epsilon^3}$. %In fact, the result of Chekuri et al.\ (2010) is
for the far more general case of matroid constant.
  We focus on finding a fast and practical algorithm that has (asymptotic)
approximation guarantees even when $m$ is super constant. We first modify the
algorithm of Chekuri et al.\ (2010) to achieve a $(1-1/e)$ approximation for
$m=o(\frac{k}{\log^3 k})$. This demonstrates a steep transition from constant
factor approximability to inapproximability around $m=\Omega(k)$. Then using
Multiplicative-Weight-Updates (MWU), we find a much faster
$\tilde{O}(n/\delta^3)$ time asymptotic $(1-1/e)^2-\delta$ approximation. While
the above results are all randomized, we also give a simple deterministic
$(1-1/e)-\epsilon$ approximation with runtime $kn^{m/\epsilon^4}$. Finally, we
run synthetic experiments using Kronecker graphs and find that our MWU inspired
heuristic outperforms existing heuristics.
