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Your search keyword '"Prissette, Cyril"' showing total 6 results
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6 results on '"Prissette, Cyril"'

1. An Algorithm to List All the Fixed-Point Free Involutions on a Finite Set

Author

Prissette, Cyril

Subjects
Computer Science - Data Structures and Algorithms
Abstract

An involution on a finite set is a bijection such as I(I(e))=e for all the element of the set. A fixed-point free involution on a finite set is an involution such as I(e)=e for none element of the set. In this article, the fixed-point free involutions are represented as partitions of the set and some properties linked to this representation are exhibited. Then an optimal algorithm to list all the fixed-point free involutions is presented. Its soundness relies on the representation of the fixed-point free involutions as partitions. Finally, an implementation of the algorithm is proposed, with an effective data representation.

Published
2010

4. A parallel strategy for an evolutionary stochastic algorithm: application to the CP decomposition of nonnegative N -th order tensors

Author

Laura, Souquières, Prissette, Cyril, Maire, Sylvain, Thirion-Moreau, Nadège, Thirion-Moreau, Nadège, Laboratoire d'Informatique et Systèmes (LIS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Université de Toulon - École d’ingénieurs SeaTech (UTLN SeaTech), and Université de Toulon (UTLN)

Subjects
Index Terms-Tensor decomposition, parallel strategy, [INFO.INFO-DC] Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], memetic algorithms, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], stochastic optimization, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; In this article, we address the problem of the Canonical Polyadic decomposition (a.k.a. CP, Candecomp or Parafac decomposition) of N-th order tensors that can be very large. In our case, this decomposition is performed under nonnegativity constraints. While this problem is often tackled thanks to deterministic approaches, we focus here, on a stochastic approach based on a memetic algorithm. It relies on the evolution of a population and a local search stage. The main drawback of such algorithms can be their relative slowness. It is the reason why we suggest and implement a parallel strategy to increase the acceptance rate of the original algorithm and thus to accelerate its convergence speed. Numerical simulations are performed in order to illustrate the effectiveness of our approach on simulated 3D fluorescence spectroscopy tensors.

Published
2020

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