2,795 research outputs found
Faster tuple lattice sieving using spherical locality-sensitive filters
To overcome the large memory requirement of classical lattice sieving
algorithms for solving hard lattice problems, Bai-Laarhoven-Stehl\'{e} [ANTS
2016] studied tuple lattice sieving, where tuples instead of pairs of lattice
vectors are combined to form shorter vectors. Herold-Kirshanova [PKC 2017]
recently improved upon their results for arbitrary tuple sizes, for example
showing that a triple sieve can solve the shortest vector problem (SVP) in
dimension in time , using a technique similar to
locality-sensitive hashing for finding nearest neighbors.
In this work, we generalize the spherical locality-sensitive filters of
Becker-Ducas-Gama-Laarhoven [SODA 2016] to obtain space-time tradeoffs for near
neighbor searching on dense data sets, and we apply these techniques to tuple
lattice sieving to obtain even better time complexities. For instance, our
triple sieve heuristically solves SVP in time . For
practical sieves based on Micciancio-Voulgaris' GaussSieve [SODA 2010], this
shows that a triple sieve uses less space and less time than the current best
near-linear space double sieve.Comment: 12 pages + references, 2 figures. Subsumed/merged into Cryptology
ePrint Archive 2017/228, available at https://ia.cr/2017/122
Shared Memory Parallel Subgraph Enumeration
The subgraph enumeration problem asks us to find all subgraphs of a target
graph that are isomorphic to a given pattern graph. Determining whether even
one such isomorphic subgraph exists is NP-complete---and therefore finding all
such subgraphs (if they exist) is a time-consuming task. Subgraph enumeration
has applications in many fields, including biochemistry and social networks,
and interestingly the fastest algorithms for solving the problem for
biochemical inputs are sequential. Since they depend on depth-first tree
traversal, an efficient parallelization is far from trivial. Nevertheless,
since important applications produce data sets with increasing difficulty,
parallelism seems beneficial.
We thus present here a shared-memory parallelization of the state-of-the-art
subgraph enumeration algorithms RI and RI-DS (a variant of RI for dense graphs)
by Bonnici et al. [BMC Bioinformatics, 2013]. Our strategy uses work stealing
and our implementation demonstrates a significant speedup on real-world
biochemical data---despite a highly irregular data access pattern. We also
improve RI-DS by pruning the search space better; this further improves the
empirical running times compared to the already highly tuned RI-DS.Comment: 18 pages, 12 figures, To appear at the 7th IEEE Workshop on Parallel
/ Distributed Computing and Optimization (PDCO 2017
Evaluating Downside Risks in Reliable Networks
Reliable networks are those in which network elements have a positive probability of failing. Conventional performance measures for such networks concern themselves either with expected network performance or with the performance of the network when it is performing well. In reliable networks modeling critical functions, decision makers are often more concerned with network performance when the network is not performing well. In this paper, we study the single-source single-destination maximum flow problem through reliable networks and propose two risk measures to evaluate such downside performance. We propose an algorithm called COMPUTE-RISK to compute downside risk measures, and report our computational experience with the proposed algorithm.
Efficient Generation of Stable Planar Cages for Chemistry
In this paper we describe an algorithm which generates all colored planar
maps with a good minimum sparsity from simple motifs and rules to connect them.
An implementation of this algorithm is available and is used by chemists who
want to quickly generate all sound molecules they can obtain by mixing some
basic components.Comment: 17 pages, 7 figures. Accepted at the 14th International Symposium on
Experimental Algorithms (SEA 2015
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