3 research outputs found
Hardness and Approximation of Submodular Minimum Linear Ordering Problems
The minimum linear ordering problem (MLOP) generalizes well-known
combinatorial optimization problems such as minimum linear arrangement and
minimum sum set cover. MLOP seeks to minimize an aggregated cost due
to an ordering of the items (say ), i.e., , where is the set of items
mapped by to indices . Despite an extensive literature on MLOP
variants and approximations for these, it was unclear whether the graphic
matroid MLOP was NP-hard. We settle this question through non-trivial
reductions from mininimum latency vertex cover and minimum sum vertex cover
problems. We further propose a new combinatorial algorithm for approximating
monotone submodular MLOP, using the theory of principal partitions. This is in
contrast to the rounding algorithm by Iwata, Tetali, and Tripathi [ITT2012],
using Lov\'asz extension of submodular functions. We show a
-approximation for monotone submodular MLOP where
satisfies . Our theory provides new approximation bounds for special cases of the
problem, in particular a -approximation for the
matroid MLOP, where is the rank function of a matroid. We further show
that minimum latency vertex cover (MLVC) is -approximable, by
which we also lower bound the integrality gap of its natural LP relaxation,
which might be of independent interest