2,357 research outputs found
Fast Monotone Summation over Disjoint Sets
We study the problem of computing an ensemble of multiple sums where the
summands in each sum are indexed by subsets of size of an -element
ground set. More precisely, the task is to compute, for each subset of size
of the ground set, the sum over the values of all subsets of size that are
disjoint from the subset of size . We present an arithmetic circuit that,
without subtraction, solves the problem using arithmetic
gates, all monotone; for constant , this is within the factor
of the optimal. The circuit design is based on viewing the summation as a "set
nucleation" task and using a tree-projection approach to implement the
nucleation. Applications include improved algorithms for counting heaviest
-paths in a weighted graph, computing permanents of rectangular matrices,
and dynamic feature selection in machine learning
Constrained Non-Monotone Submodular Maximization: Offline and Secretary Algorithms
Constrained submodular maximization problems have long been studied, with
near-optimal results known under a variety of constraints when the submodular
function is monotone. The case of non-monotone submodular maximization is less
understood: the first approximation algorithms even for the unconstrainted
setting were given by Feige et al. (FOCS '07). More recently, Lee et al. (STOC
'09, APPROX '09) show how to approximately maximize non-monotone submodular
functions when the constraints are given by the intersection of p matroid
constraints; their algorithm is based on local-search procedures that consider
p-swaps, and hence the running time may be n^Omega(p), implying their algorithm
is polynomial-time only for constantly many matroids. In this paper, we give
algorithms that work for p-independence systems (which generalize constraints
given by the intersection of p matroids), where the running time is poly(n,p).
Our algorithm essentially reduces the non-monotone maximization problem to
multiple runs of the greedy algorithm previously used in the monotone case.
Our idea of using existing algorithms for monotone functions to solve the
non-monotone case also works for maximizing a submodular function with respect
to a knapsack constraint: we get a simple greedy-based constant-factor
approximation for this problem.
With these simpler algorithms, we are able to adapt our approach to
constrained non-monotone submodular maximization to the (online) secretary
setting, where elements arrive one at a time in random order, and the algorithm
must make irrevocable decisions about whether or not to select each element as
it arrives. We give constant approximations in this secretary setting when the
algorithm is constrained subject to a uniform matroid or a partition matroid,
and give an O(log k) approximation when it is constrained by a general matroid
of rank k.Comment: In the Proceedings of WINE 201
Strong noise sensitivity and random graphs
The noise sensitivity of a Boolean function describes its likelihood to flip
under small perturbations of its input. Introduced in the seminal work of
Benjamini, Kalai and Schramm [Inst. Hautes \'{E}tudes Sci. Publ. Math. 90
(1999) 5-43], it was there shown to be governed by the first level of Fourier
coefficients in the central case of monotone functions at a constant critical
probability . Here we study noise sensitivity and a natural stronger
version of it, addressing the effect of noise given a specific witness in the
original input. Our main context is the Erd\H{o}s-R\'{e}nyi random graph, where
already the property of containing a given graph is sufficiently rich to
separate these notions. In particular, our analysis implies (strong) noise
sensitivity in settings where the BKS criterion involving the first Fourier
level does not apply, for example, when polynomially fast in the
number of variables.Comment: Published at http://dx.doi.org/10.1214/14-AOP959 in the Annals of
Probability (http://www.imstat.org/aop/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Robust Submodular Maximization: A Non-Uniform Partitioning Approach
We study the problem of maximizing a monotone submodular function subject to
a cardinality constraint , with the added twist that a number of items
from the returned set may be removed. We focus on the worst-case setting
considered in (Orlin et al., 2016), in which a constant-factor approximation
guarantee was given for . In this paper, we solve a key
open problem raised therein, presenting a new Partitioned Robust (PRo)
submodular maximization algorithm that achieves the same guarantee for more
general . Our algorithm constructs partitions consisting of
buckets with exponentially increasing sizes, and applies standard submodular
optimization subroutines on the buckets in order to construct the robust
solution. We numerically demonstrate the performance of PRo in data
summarization and influence maximization, demonstrating gains over both the
greedy algorithm and the algorithm of (Orlin et al., 2016).Comment: Accepted to ICML 201
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