We are interested in the statistics of the length of the longest increasing
subsequence of 2-rowed lexicographically sorted arrays chosen according to
distinct families of distributions D = (D_n)_n, and when n goes to infinity.
This framework encompasses well studied problems such as the so called Longest
Increasing Subsequence problem, the Longest Common Subsequence problem,
problems concerning directed bond percolation models, among others.
In the Matroid Secretary Problem, introduced by Babaioff et al. [SODA 2007],
the elements of a given matroid are presented to an online algorithm in random
order. When an element is revealed, the algorithm learns its weight and decides
whether or not to select it under the restriction that the selected elements
form an independent set in the matroid. The objective is to maximize the total
weight of the chosen elements. In the most studied version of this problem, the
algorithm has no information about the weights beforehand. We refer to this as
the zero information model.
We present an efficient algorithm to find non-empty minimizers of a symmetric
submodular function over any family of sets closed under inclusion. This for
example includes families defined by a cardinality constraint, a knapsack
constraint, a matroid independence constraint, or any combination of such
constraints. Our algorithm make $O(n^3)$ oracle calls to the submodular
function where $n$ is the cardinality of the ground set.