19,597 research outputs found
Bayesian network learning with cutting planes
The problem of learning the structure of Bayesian networks from complete
discrete data with a limit on parent set size is considered. Learning is cast
explicitly as an optimisation problem where the goal is to find a BN structure
which maximises log marginal likelihood (BDe score). Integer programming,
specifically the SCIP framework, is used to solve this optimisation problem.
Acyclicity constraints are added to the integer program (IP) during solving in
the form of cutting planes. Finding good cutting planes is the key to the
success of the approach -the search for such cutting planes is effected using a
sub-IP. Results show that this is a particularly fast method for exact BN
learning
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Machine learning : techniques and foundations
The field of machine learning studies computational methods for acquiring new knowledge, new skills, and new ways to organize existing knowledge. In this paper we present some of the basic techniques and principles that underlie AI research on learning, including methods for learning from examples, learning in problem solving, learning by analogy, grammar acquisition, and machine discovery. In each case, we illustrate the techniques with paradigmatic examples
Exact Solution Methods for the -item Quadratic Knapsack Problem
The purpose of this paper is to solve the 0-1 -item quadratic knapsack
problem , a problem of maximizing a quadratic function subject to two
linear constraints. We propose an exact method based on semidefinite
optimization. The semidefinite relaxation used in our approach includes simple
rank one constraints, which can be handled efficiently by interior point
methods. Furthermore, we strengthen the relaxation by polyhedral constraints
and obtain approximate solutions to this semidefinite problem by applying a
bundle method. We review other exact solution methods and compare all these
approaches by experimenting with instances of various sizes and densities.Comment: 12 page
Seeing Shapes in Clouds: On the Performance-Cost trade-off for Heterogeneous Infrastructure-as-a-Service
In the near future FPGAs will be available by the hour, however this new
Infrastructure as a Service (IaaS) usage mode presents both an opportunity and
a challenge: The opportunity is that programmers can potentially trade
resources for performance on a much larger scale, for much shorter periods of
time than before. The challenge is in finding and traversing the trade-off for
heterogeneous IaaS that guarantees increased resources result in the greatest
possible increased performance. Such a trade-off is Pareto optimal. The Pareto
optimal trade-off for clusters of heterogeneous resources can be found by
solving multiple, multi-objective optimisation problems, resulting in an
optimal allocation of tasks to the available platforms. Solving these
optimisation programs can be done using simple heuristic approaches or formal
Mixed Integer Linear Programming (MILP) techniques. When pricing 128 financial
options using a Monte Carlo algorithm upon a heterogeneous cluster of Multicore
CPU, GPU and FPGA platforms, the MILP approach produces a trade-off that is up
to 110% faster than a heuristic approach, and over 50% cheaper. These results
suggest that high quality performance-resource trade-offs of heterogeneous IaaS
are best realised through a formal optimisation approach.Comment: Presented at Second International Workshop on FPGAs for Software
Programmers (FSP 2015) (arXiv:1508.06320
A Hybrid Multicast-Unicast Infrastructure for Efficient Publish-Subscribe in Enterprise Networks
One of the main challenges in building a large scale publish-subscribe
infrastructure in an enterprise network, is to provide the subscribers with the
required information, while minimizing the consumed host and network resources.
Typically, previous approaches utilize either IP multicast or point-to-point
unicast for efficient dissemination of the information.
In this work, we propose a novel hybrid framework, which is a combination of
both multicast and unicast data dissemination. Our hybrid framework allows us
to take the advantages of both multicast and unicast, while avoiding their
drawbacks. We investigate several algorithms for computing the best mapping of
publishers' transmissions into multicast and unicast transport.
Using extensive simulations, we show that our hybrid framework reduces
consumed host and network resources, outperforming traditional solutions. To
insure the subscribers interests closely resemble those of real-world settings,
our simulations are based on stock market data and on recorded IBM WebShpere
subscriptions
A Constraint-directed Local Search Approach to Nurse Rostering Problems
In this paper, we investigate the hybridization of constraint programming and
local search techniques within a large neighbourhood search scheme for solving
highly constrained nurse rostering problems. As identified by the research, a
crucial part of the large neighbourhood search is the selection of the fragment
(neighbourhood, i.e. the set of variables), to be relaxed and re-optimized
iteratively. The success of the large neighbourhood search depends on the
adequacy of this identified neighbourhood with regard to the problematic part
of the solution assignment and the choice of the neighbourhood size. We
investigate three strategies to choose the fragment of different sizes within
the large neighbourhood search scheme. The first two strategies are tailored
concerning the problem properties. The third strategy is more general, using
the information of the cost from the soft constraint violations and their
propagation as the indicator to choose the variables added into the fragment.
The three strategies are analyzed and compared upon a benchmark nurse rostering
problem. Promising results demonstrate the possibility of future work in the
hybrid approach
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