2,969 research outputs found
Improving Table Compression with Combinatorial Optimization
We study the problem of compressing massive tables within the
partition-training paradigm introduced by Buchsbaum et al. [SODA'00], in which
a table is partitioned by an off-line training procedure into disjoint
intervals of columns, each of which is compressed separately by a standard,
on-line compressor like gzip. We provide a new theory that unifies previous
experimental observations on partitioning and heuristic observations on column
permutation, all of which are used to improve compression rates. Based on the
theory, we devise the first on-line training algorithms for table compression,
which can be applied to individual files, not just continuously operating
sources; and also a new, off-line training algorithm, based on a link to the
asymmetric traveling salesman problem, which improves on prior work by
rearranging columns prior to partitioning. We demonstrate these results
experimentally. On various test files, the on-line algorithms provide 35-55%
improvement over gzip with negligible slowdown; the off-line reordering
provides up to 20% further improvement over partitioning alone. We also show
that a variation of the table compression problem is MAX-SNP hard.Comment: 22 pages, 2 figures, 5 tables, 23 references. Extended abstract
appears in Proc. 13th ACM-SIAM SODA, pp. 213-222, 200
Meta-heuristics for stable scheduling on a single machine.
This paper presents a model for single-machine scheduling with stability objective and a common deadline. Job durations are uncertain, and our goal is to ensure that there is little deviation between planned and actual job starting times. We propose two meta-heuristics for solving an approximate formulation of the model that assumes that exactly one job is disrupted during schedule execution, and we also present a meta-heuristic for the global problem with independent job durationsMeta-heuristics; Robustness; Single-machine scheduling; Uncertainty;
Graph Partitioning-Based Coordination Methods for Large-Scale Multidisciplinary Design Optimization Problems
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97127/1/AIAA2012-5522.pd
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