2,308 research outputs found
A note on Probably Certifiably Correct algorithms
Many optimization problems of interest are known to be intractable, and while
there are often heuristics that are known to work on typical instances, it is
usually not easy to determine a posteriori whether the optimal solution was
found. In this short note, we discuss algorithms that not only solve the
problem on typical instances, but also provide a posteriori certificates of
optimality, probably certifiably correct (PCC) algorithms. As an illustrative
example, we present a fast PCC algorithm for minimum bisection under the
stochastic block model and briefly discuss other examples
Moment-Based Relaxation of the Optimal Power Flow Problem
The optimal power flow (OPF) problem minimizes power system operating cost
subject to both engineering and network constraints. With the potential to find
global solutions, significant research interest has focused on convex
relaxations of the non-convex AC OPF problem. This paper investigates
``moment-based'' relaxations of the OPF problem developed from the theory of
polynomial optimization problems. At the cost of increased computational
requirements, moment-based relaxations are generally tighter than the
semidefinite relaxation employed in previous research, thus resulting in global
solutions for a broader class of OPF problems. Exploration of the feasible
space for test systems illustrates the effectiveness of the moment-based
relaxation.Comment: 7 pages, 4 figures. Abstract accepted, full paper in revie
Convex Relaxations for Permutation Problems
Seriation seeks to reconstruct a linear order between variables using
unsorted, pairwise similarity information. It has direct applications in
archeology and shotgun gene sequencing for example. We write seriation as an
optimization problem by proving the equivalence between the seriation and
combinatorial 2-SUM problems on similarity matrices (2-SUM is a quadratic
minimization problem over permutations). The seriation problem can be solved
exactly by a spectral algorithm in the noiseless case and we derive several
convex relaxations for 2-SUM to improve the robustness of seriation solutions
in noisy settings. These convex relaxations also allow us to impose structural
constraints on the solution, hence solve semi-supervised seriation problems. We
derive new approximation bounds for some of these relaxations and present
numerical experiments on archeological data, Markov chains and DNA assembly
from shotgun gene sequencing data.Comment: Final journal version, a few typos and references fixe
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