4,076 research outputs found
Postponing Branching Decisions
Solution techniques for Constraint Satisfaction and Optimisation Problems
often make use of backtrack search methods, exploiting variable and value
ordering heuristics. In this paper, we propose and analyse a very simple method
to apply in case the value ordering heuristic produces ties: postponing the
branching decision. To this end, we group together values in a tie, branch on
this sub-domain, and defer the decision among them to lower levels of the
search tree. We show theoretically and experimentally that this simple
modification can dramatically improve the efficiency of the search strategy.
Although in practise similar methods may have been applied already, to our
knowledge, no empirical or theoretical study has been proposed in the
literature to identify when and to what extent this strategy should be used.Comment: 11 pages, 3 figure
Decomposition Based Search - A theoretical and experimental evaluation
In this paper we present and evaluate a search strategy called Decomposition
Based Search (DBS) which is based on two steps: subproblem generation and
subproblem solution. The generation of subproblems is done through value
ranking and domain splitting. Subdomains are explored so as to generate,
according to the heuristic chosen, promising subproblems first.
We show that two well known search strategies, Limited Discrepancy Search
(LDS) and Iterative Broadening (IB), can be seen as special cases of DBS. First
we present a tuning of DBS that visits the same search nodes as IB, but avoids
restarts. Then we compare both theoretically and computationally DBS and LDS
using the same heuristic. We prove that DBS has a higher probability of being
successful than LDS on a comparable number of nodes, under realistic
assumptions. Experiments on a constraint satisfaction problem and an
optimization problem show that DBS is indeed very effective if compared to LDS.Comment: 16 pages, 8 figures. LIA Technical Report LIA00203, University of
Bologna, 200
The motion of a deformable drop in a second-order fluid
The cross-stream migration of a deformable drop in a unidirectional shear flow of a second-order fluid is considered. Expressions for the particle velocity due to the separate effects of deformation and viscoelastic rheology are obtained. The direction and magnitude of migration are calculated for the particular cases of Poiseuille flow and simple shear flow and compared with experimental data
Statistical Mechanics of High-Dimensional Inference
To model modern large-scale datasets, we need efficient algorithms to infer a
set of unknown model parameters from noisy measurements. What are
fundamental limits on the accuracy of parameter inference, given finite
signal-to-noise ratios, limited measurements, prior information, and
computational tractability requirements? How can we combine prior information
with measurements to achieve these limits? Classical statistics gives incisive
answers to these questions as the measurement density . However, these classical results are not
relevant to modern high-dimensional inference problems, which instead occur at
finite . We formulate and analyze high-dimensional inference as a
problem in the statistical physics of quenched disorder. Our analysis uncovers
fundamental limits on the accuracy of inference in high dimensions, and reveals
that widely cherished inference algorithms like maximum likelihood (ML) and
maximum-a posteriori (MAP) inference cannot achieve these limits. We further
find optimal, computationally tractable algorithms that can achieve these
limits. Intriguingly, in high dimensions, these optimal algorithms become
computationally simpler than MAP and ML, while still outperforming them. For
example, such optimal algorithms can lead to as much as a 20% reduction in the
amount of data to achieve the same performance relative to MAP. Moreover, our
analysis reveals simple relations between optimal high dimensional inference
and low dimensional scalar Bayesian inference, insights into the nature of
generalization and predictive power in high dimensions, information theoretic
limits on compressed sensing, phase transitions in quadratic inference, and
connections to central mathematical objects in convex optimization theory and
random matrix theory.Comment: See http://ganguli-gang.stanford.edu/pdf/HighDimInf.Supp.pdf for
supplementary materia
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