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Luis Quesada 0001

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

SoCS Conference 2013 Conference Paper

Parallelising the k-Medoids Clustering Problem Using Space-Partitioning

Alejandro Arbelaez
Luis Quesada 0001

The k-medoids problem is a combinatorial optimisation problem with multiples applications in Resource Allocation, Mobile Computing, Sensor Networks and Telecommunications. Real instances of this problem involve hundreds of thousands of points and thousands of medoids. Despite the proliferation of parallel architectures, this problem has been mostly tackled using sequential approaches. In this paper, we study the impact of space-partitioning techniques on the performance of parallel local search algorithms to tackle the k-medoids clustering problem, and compare these results with the ones obtained using sampling. Our experiments suggest that approaches relying on partitioning scale more while preserving the quality of the solution.

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ECAI Conference 2010 Conference Paper

Improving the Global Constraint SoftPrec

David Lesaint
Deepak Mehta 0001
Barry O'Sullivan
Luis Quesada 0001
Nic Wilson

A soft global constraint SOFTPREC has been proposed recently for solving optimisation problems involving precedence relations. In this paper we present new pruning rules for this global constraint. We introduce a pruning rule that improves propagation from the objective variable to the decision variables, which is believed to be harder to achieve. We further introduce a pruning rule based on linear programming, and thereby make SOFTPREC a hybrid of constraint programming and linear programming. We present results demonstrating the efficiency of the pruning rules.

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ECAI Conference 2008 Conference Paper

A BDD Approach to the Feature Subscription Problem

Tarik Hadzic
David Lesaint
Deepak Mehta 0001
Barry O'Sullivan
Luis Quesada 0001
Nic Wilson

Modern feature-rich telecommunications services offer significant opportunities to human users. To make these services more usable, facilitating personalisation is very important since it enhances the users' experience considerably. However, regardless how service providers organise their catalogues of features, they cannot achieve complete configurability due to the existence of feature interactions. Distributed Feature Composition (DFC) provides a comprehensive methodology, underpinned by a formal architecture model to address this issue. In this paper we present an approach based on using Binary Decision Diagrams (BDD) to find optimal reconfigurations of features when a user's preferences violate the technical constraints defined by a set of DFC rules. In particular, we propose hybridizing constraint programming and standard BDD compilation techniques in order to scale the construction of a BDD for larger size catalogues. Our approach outperforms the standard BDD techniques by reducing the memory requirements by as much as five orders-of-magnitude and compiles the catalogues for which the standard techniques ran out of memory.

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