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Статті в журналах з теми "Factorisation creuse de matrices"
Melkemi, Lamine, Abdallah Benaissa, and Rachid Benacer. "Factorisation QR des matrices de Tchebychev–Vandermonde confluentes." Comptes Rendus de l'Académie des Sciences - Series I - Mathematics 330, no. 2 (January 2000): 147–52. http://dx.doi.org/10.1016/s0764-4442(00)00144-0.
Повний текст джерелаJacobson, Bernard, and Robert J. Wisner. "Matrix number theory. I. Factorisation of $2\times 2$ unimodular matrices." Publicationes Mathematicae Debrecen 13, no. 1-4 (July 1, 2022): 67–72. http://dx.doi.org/10.5486/pmd.1966.13.1-4.08.
Повний текст джерелаLiew, How Hui, Wei Shean Ng, and Huey Voon Chen. "Investigating the feature extraction capabilities of non-negative matrix factorisation algorithms for black-and-white images." ITM Web of Conferences 67 (2024): 01031. http://dx.doi.org/10.1051/itmconf/20246701031.
Повний текст джерелаBakonyi, Mihály. "On Gaussian elimination and determinant formulas for matrices with chordal inverses." Bulletin of the Australian Mathematical Society 46, no. 3 (December 1992): 435–40. http://dx.doi.org/10.1017/s0004972700012090.
Повний текст джерелаCarbonell-Caballero, José, Antonio López-Quílez, David Conesa, and Joaquín Dopazo. "Deciphering Genomic Heterogeneity and the Internal Composition of Tumour Activities through a Hierarchical Factorisation Model." Mathematics 9, no. 21 (November 8, 2021): 2833. http://dx.doi.org/10.3390/math9212833.
Повний текст джерелаSpencer, N. M., and V. V. Anh. "Spectral factorisation and prediction of multivariate processes with time-dependent rational spectral density matrices." Journal of the Australian Mathematical Society. Series B. Applied Mathematics 33, no. 2 (October 1991): 192–210. http://dx.doi.org/10.1017/s0334270000006998.
Повний текст джерелаHamel, Angèle M., and Ronald C. King. "Half-turn symmetric alternating sign matrices and Tokuyama type factorisation for orthogonal group characters." Journal of Combinatorial Theory, Series A 131 (April 2015): 1–31. http://dx.doi.org/10.1016/j.jcta.2014.11.005.
Повний текст джерелаPrößdorf, S., and F. O. Speck. "A factorisation procedure for two by two matrix functions on the circle with two rationally independent entries." Proceedings of the Royal Society of Edinburgh: Section A Mathematics 115, no. 1-2 (1990): 119–38. http://dx.doi.org/10.1017/s0308210500024616.
Повний текст джерелаLeipnik, R. B. "Reduction of second order linear dynamical systems, with large dissipation, by state variable transformations." Journal of the Australian Mathematical Society. Series B. Applied Mathematics 32, no. 2 (October 1990): 207–22. http://dx.doi.org/10.1017/s0334270000008432.
Повний текст джерелаAbou-Zleikha, Mohamed, and Noor Shaker. "PaTux: An Authoring Tool for Level Design through Pattern Customisation Using Non-Negative Matrix Factorization." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 10, no. 1 (June 29, 2021): 103–4. http://dx.doi.org/10.1609/aiide.v10i1.12726.
Повний текст джерелаДисертації з теми "Factorisation creuse de matrices"
Ramet, Pierre. "Optimisation de la communication et de la distribution des données pour des solveurs parallèles directs en algèbre linéaire dense et creuse." Bordeaux 1, 2000. http://www.theses.fr/2000BOR10506.
Повний текст джерелаGrigori, Laura. "Prédiction de structure et algorithmique parallèle pour la factorisation LU des matrices creuses." Nancy 1, 2001. http://www.theses.fr/2001NAN10264.
Повний текст джерелаThis dissertation treats of parallel numerical computing considering the Gaussian elimination, as it is used to solve large sparse nonsymmetric linear systems. Usually, computations on sparse matrices have an initial phase that predicts the nonzero structure of the output, which helps with memory allocations, set up data structures and schedule parallel tasks prior to the numerical computation itself. To this end, we study the structure prediction for the sparse LU factorization with partial pivoting. We are mainly interested to identify upper bounds as tight as possible to these structures. This structure prediction is then used in a phase called symbolic factorization, followed by a phase that performs the numerical computation of the factors, called numerical factorization. For very large matrices, a significant part of the overall memory space is needed by structures used during the symbolic factorization, and this can prevent a swap-free execution of the LU factorization. We propose and study a parallel algorithm to decrease the memory requirements of the nonsymmetric symbolic factorization. For an efficient parallel execution of the numerical factorization, we consider the analysis and the handling of the data dependencies graphs resulting from the processing of sparse matrices. This analysis enables us to develop scalable algorithms, which manage memory and computing resources in an effective way
Puglisi, Chiara. "Factorisation QR de grandes matrices creuses basée sur une méthode multifrontale dans un environnement multiprocesseur." Toulouse, INPT, 1993. http://www.theses.fr/1993INPT091H.
Повний текст джерелаGuermouche, Abdou. "Étude et optimisation du comportement mémoire dans les méthodes parallèles de factorisation de matrices creuses." Lyon, École normale supérieure (sciences), 2004. http://www.theses.fr/2004ENSL0284.
Повний текст джерелаDirect methods for solving sparse linear systems are known for their large memory requirements that can represent the limiting factor to solve large systems. The work done during this thesis concerns the study and the optimization of the memory behaviour of a sparse direct method, the multifrontal method, for both the sequential and the parallel cases. Thus, optimal memory minimization algorithms have been proposed for the sequential case. Concerning the parallel case, we have introduced new scheduling strategies aiming at improving the memory behaviour of the method. After that, we extended these approaches to have a good performance while keeping a good memory behaviour. In addition, in the case where the data to be treated cannot fit into memory, out-of-core factorization schemes have to be designed. To be efficient, such approaches require to overlap I/O operations with computations and to reuse the data sets already in memory to reduce the amount of I/O operations. Therefore, another part of the work presented in this thesis concerns the design and the study of implicit out-of-core techniques well-adapted to the memory access pattern of the multifrontal method. These techniques are based on a modification of the standard paging policies of the operating system using a low-level tool (MMUM&MMUSSEL)
Amestoy, Patrick. "Factorisation de grandes matrices creuses non symétriques basée sur une méthode multifrontale dans un environnement multiprocesseur." Toulouse, INPT, 1990. http://www.theses.fr/1990INPT050H.
Повний текст джерелаZheng, Léon. "Frugalité en données et efficacité computationnelle dans l'apprentissage profond." Electronic Thesis or Diss., Lyon, École normale supérieure, 2024. http://www.theses.fr/2024ENSL0009.
Повний текст джерелаThis thesis focuses on two challenges of frugality and efficiency in modern deep learning: data frugality and computational resource efficiency. First, we study self-supervised learning, a promising approach in computer vision that does not require data annotations for learning representations. In particular, we propose a unification of several self-supervised objective functions under a framework based on rotation-invariant kernels, which opens up prospects to reduce the computational cost of these objective functions. Second, given that matrix multiplication is the predominant operation in deep neural networks, we focus on the construction of fast algorithms that allow matrix-vector multiplication with nearly linear complexity. More specifically, we examine the problem of sparse matrix factorization under the constraint of butterfly sparsity, a structure common to several fast transforms like the discrete Fourier transform. The thesis establishes new theoretical guarantees for butterfly factorization algorithms, and explores the potential of butterfly sparsity to reduce the computational costs of neural networks during their training or inference phase. In particular, we explore the efficiency of GPU implementations for butterfly sparse matrix multiplication, with the goal of truly accelerating sparse neural networks
Agullo, Emmanuel. "Méthodes directes hors-mémoire (out-of-core) pour la résolution de systèmes linéaires creux de grande taille." Phd thesis, Ecole normale supérieure de lyon - ENS LYON, 2008. http://tel.archives-ouvertes.fr/tel-00563463.
Повний текст джерелаRouet, François-Henry. "Problèmes de mémoire et de performance de la factorisation multifrontale parallèle et de la résolution triangulaire à seconds membres creux." Phd thesis, Institut National Polytechnique de Toulouse - INPT, 2012. http://tel.archives-ouvertes.fr/tel-00785748.
Повний текст джерелаGouvert, Olivier. "Factorisation bayésienne de matrices pour le filtrage collaboratif." Thesis, Toulouse, INPT, 2019. https://oatao.univ-toulouse.fr/25879/1/Gouvert_Olivier.pdf.
Повний текст джерелаIn recent years, a lot of research has been devoted to recommender systems. The goal of these systems is to recommend to each user some products that he/she may like, in order to facilitate his/her exploration of large catalogs of items. Collaborative filtering (CF) allows to make such recommendations based on the past interactions of the users only. These data are stored in a matrix, where each entry corresponds to the feedback of a user on an item. In particular, this matrix is of very high dimensions and extremly sparse, since the users have interacted with a few items from the catalog. Implicit feedbacks are the easiest data to collect. They are usually available in the form of counts, corresponding to the number of times a user interacted with an item. Non-negative matrix factorization (NMF) techniques consist in approximating the feedback matrix by the product of two non-negative matrices. Thus, each user and item is represented by a latent factor of small dimension corresponding to its preferences and attributes respectively. In recent years, a lot of research has been devoted to recommender systems. The goal of these systems is to recommend to each user some products that he/she may like, in order to facilitate his/her exploration of large catalogs of items. Collaborative filtering (CF) allows to make such recommendations based on the past interactions of the users only. These data are stored in a matrix, where each entry corresponds to the feedback of a user on an item. In particular, this matrix is of very high dimensions and extremly sparse, since the users have interacted with a few items from the catalog. Implicit feedbacks are the easiest data to collect. They are usually available in the form of counts, corresponding to the number of times a user interacted with an item. Non-negative matrix factorization (NMF) techniques consist in approximating the feedback matrix by the product of two non-negative matrices. Thus, each user and item is represented by a latent factor of small dimension corresponding to its preferences and attributes respectively. The goal of this thesis is to develop Bayesian NMF methods which can directly model the overdispersed count data arising in CF. To do so, we first study Poisson factorization (PF) and present its limits for the processing of over-dispersed data. To alleviate this problem, we propose two extensions of PF : negative binomial factorization (NBF) and discrete compound Poisson factorisation (dcPF). In particular, dcPF leads to an interpretation of the variables especially suited to music recommendation. Then, we choose to work on quantified implicit data. This pre- processing simplifies the data which are therefore ordinal. Thus, we propose a Bayesian NMF model for this kind of data, coined OrdNMF. We show that this model is also an extension of PF applied to pre-processed data. Finally, in the last chapter of this thesis, we focus on the wellknown cold-start problem which affects CF techniques. We propose a matrix co-factorization model which allow us to solve this issue
Aleksandrova, Marharyta. "Factorisation de matrices et analyse de contraste pour la recommandation." Thesis, Université de Lorraine, 2017. http://www.theses.fr/2017LORR0080/document.
Повний текст джерелаIn many application areas, data elements can be high-dimensional. This raises the problem of dimensionality reduction. The dimensionality reduction techniques can be classified based on their aim: dimensionality reduction for optimal data representation and dimensionality reduction for classification, as well as based on the adopted strategy: feature selection and feature extraction. The set of features resulting from feature extraction methods is usually uninterpretable. Thereby, the first scientific problematic of the thesis is how to extract interpretable latent features? The dimensionality reduction for classification aims to enhance the classification power of the selected subset of features. We see the development of the task of classification as the task of trigger factors identification that is identification of those factors that can influence the transfer of data elements from one class to another. The second scientific problematic of this thesis is how to automatically identify these trigger factors? We aim at solving both scientific problematics within the recommender systems application domain. We propose to interpret latent features for the matrix factorization-based recommender systems as real users. We design an algorithm for automatic identification of trigger factors based on the concepts of contrast analysis. Through experimental results, we show that the defined patterns indeed can be considered as trigger factors
Частини книг з теми "Factorisation creuse de matrices"
Seo, S. G., M. J. Downie, G. E. Hearn, and C. Phillips. "A Systolic Algorithm for the Factorisation of Matrices Arising in the Field of Hydrodynamics." In Vector and Parallel Processing – VECPAR’98, 355–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/10703040_27.
Повний текст джерелаCONRADSEN, Knut, Henning SKRIVER, Morton J. CANTY, and Allan A. NIELSEN. "Détection de séries de changements dans des séries d’images SAR polarimétriques." In Détection de changements et analyse des séries temporelles d’images 1, 41–81. ISTE Group, 2022. http://dx.doi.org/10.51926/iste.9056.ch2.
Повний текст джерела