243 research outputs found
Complexity of Discrete Energy Minimization Problems
Discrete energy minimization is widely-used in computer vision and machine
learning for problems such as MAP inference in graphical models. The problem,
in general, is notoriously intractable, and finding the global optimal solution
is known to be NP-hard. However, is it possible to approximate this problem
with a reasonable ratio bound on the solution quality in polynomial time? We
show in this paper that the answer is no. Specifically, we show that general
energy minimization, even in the 2-label pairwise case, and planar energy
minimization with three or more labels are exp-APX-complete. This finding rules
out the existence of any approximation algorithm with a sub-exponential
approximation ratio in the input size for these two problems, including
constant factor approximations. Moreover, we collect and review the
computational complexity of several subclass problems and arrange them on a
complexity scale consisting of three major complexity classes -- PO, APX, and
exp-APX, corresponding to problems that are solvable, approximable, and
inapproximable in polynomial time. Problems in the first two complexity classes
can serve as alternative tractable formulations to the inapproximable ones.
This paper can help vision researchers to select an appropriate model for an
application or guide them in designing new algorithms.Comment: ECCV'16 accepte
Hypergraphs with Edge-Dependent Vertex Weights: p-Laplacians and Spectral Clustering
We study p-Laplacians and spectral clustering for a recently proposed
hypergraph model that incorporates edge-dependent vertex weights (EDVW). These
weights can reflect different importance of vertices within a hyperedge, thus
conferring the hypergraph model higher expressivity and flexibility. By
constructing submodular EDVW-based splitting functions, we convert hypergraphs
with EDVW into submodular hypergraphs for which the spectral theory is better
developed. In this way, existing concepts and theorems such as p-Laplacians and
Cheeger inequalities proposed under the submodular hypergraph setting can be
directly extended to hypergraphs with EDVW. For submodular hypergraphs with
EDVW-based splitting functions, we propose an efficient algorithm to compute
the eigenvector associated with the second smallest eigenvalue of the
hypergraph 1-Laplacian. We then utilize this eigenvector to cluster the
vertices, achieving higher clustering accuracy than traditional spectral
clustering based on the 2-Laplacian. More broadly, the proposed algorithm works
for all submodular hypergraphs that are graph reducible. Numerical experiments
using real-world data demonstrate the effectiveness of combining spectral
clustering based on the 1-Laplacian and EDVW
Optimal Interdiction of Unreactive Markovian Evaders
The interdiction problem arises in a variety of areas including military
logistics, infectious disease control, and counter-terrorism. In the typical
formulation of network interdiction, the task of the interdictor is to find a
set of edges in a weighted network such that the removal of those edges would
maximally increase the cost to an evader of traveling on a path through the
network.
Our work is motivated by cases in which the evader has incomplete information
about the network or lacks planning time or computational power, e.g. when
authorities set up roadblocks to catch bank robbers, the criminals do not know
all the roadblock locations or the best path to use for their escape.
We introduce a model of network interdiction in which the motion of one or
more evaders is described by Markov processes and the evaders are assumed not
to react to interdiction decisions. The interdiction objective is to find an
edge set of size B, that maximizes the probability of capturing the evaders.
We prove that similar to the standard least-cost formulation for
deterministic motion this interdiction problem is also NP-hard. But unlike that
problem our interdiction problem is submodular and the optimal solution can be
approximated within 1-1/e using a greedy algorithm. Additionally, we exploit
submodularity through a priority evaluation strategy that eliminates the linear
complexity scaling in the number of network edges and speeds up the solution by
orders of magnitude. Taken together the results bring closer the goal of
finding realistic solutions to the interdiction problem on global-scale
networks.Comment: Accepted at the Sixth International Conference on integration of AI
and OR Techniques in Constraint Programming for Combinatorial Optimization
Problems (CPAIOR 2009
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