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Academic literature on the topic 'Approximations par matrices faible rang'
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Journal articles on the topic "Approximations par matrices faible rang"
Regniers, Olivier, Lionel Bombrun, and Christian Germain. "Modélisation de texture basée sur les ondelettes pour la détection de parcelles viticoles à partir d'images Pléiades panchromatiques." Revue Française de Photogrammétrie et de Télédétection, no. 208 (September 8, 2014): 117–22. http://dx.doi.org/10.52638/rfpt.2014.122.
Full textLabai, Nadia, and Johann Makowsky. "Tropical Graph Parameters." Discrete Mathematics & Theoretical Computer Science DMTCS Proceedings vol. AT,..., Proceedings (January 1, 2014). http://dx.doi.org/10.46298/dmtcs.2406.
Full textDissertations / Theses on the topic "Approximations par matrices faible rang"
Weisbecker, Clement. "Amélioration des solveurs multifrontaux à l'aide de représentations algébriques rang-faible par blocs." Phd thesis, Institut National Polytechnique de Toulouse - INPT, 2013. http://tel.archives-ouvertes.fr/tel-00934939.
Full textAbbas, Kinan. "Dématriçage et démélange conjoints d'images multispectrales." Electronic Thesis or Diss., Littoral, 2024. http://www.theses.fr/2024DUNK0710.
Full textIn this thesis, we consider images sensed by a miniaturized multispectral (MS) snapshot camera. Contrary to classical RGB cameras, MS imaging allows to observe a scene on tens of different wavelengths, allowing a much more precise analysis of the observed content. While most MS cameras require a scan to generate an image, snapshot MS cameras can instantaneouslyprovide images, or even videos. When the camera is miniaturized, instead of a 3D data cube, it gets a 2D image, each pixel being associated with a filtered version of the theoretical spectrum it should acquire. Post-processing, called “demosaicing”, is then necessary to reconstruct a data cube. Furthermore, in each pixel of the image, the observed spectrum can be considered as a mixture of spectra of pure materials present in the pixel. Estimating these spectra named endmembers as well as their spatial distribution (named abundances) is called “unmixing”. While a classical pipeline to process MS snapshot images is to first demosaice and then unmix the data, the work introduced in this thesis explores alternative strategies in which demosaicing and unmixing are jointly performed. Extending classical assumptions met in sparse component analysis and in remote sensing MS unmixing, we propose two different frameworks to restore and unmixing the acquired scene, based on low-rank matrix completion and deconvolution, respectively, the latter being specifically designed for Fabry-Perot filters used in the considered camera. The four proposed methods exhibit a far better unmixing enhancement than the variants they extend when the latter are applied to demosaiced data. Still, they allow a similar demosaicing performance as state-of-the-art methods. The last part of this thesis introduces a deconvolution approach to restore the spectra of such cameras. Our contribution lies in the weights of the penalization term which are automatically set using the entropy of the Fabry-Perot harmonics. The proposed method exhibits a better spectrum restoration than the strategy proposed by the camera manufacturer and than the classical deconvolution technique it extends
Mary, Théo. "Solveurs multifrontaux exploitant des blocs de rang faible : complexité, performance et parallélisme." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30305/document.
Full textWe investigate the use of low-rank approximations to reduce the cost of sparse direct multifrontal solvers. Among the different matrix representations that have been proposed to exploit the low-rank property within multifrontal solvers, we focus on the Block Low-Rank (BLR) format whose simplicity and flexibility make it easy to use in a general purpose, algebraic multifrontal solver. We present different variants of the BLR factorization, depending on how the low-rank updates are performed and on the constraints to handle numerical pivoting. We first investigate the theoretical complexity of the BLR format which, unlike other formats such as hierarchical ones, was previously unknown. We prove that the theoretical complexity of the BLR multifrontal factorization is asymptotically lower than that of the full-rank solver. We then show how the BLR variants can further reduce that complexity. We provide an experimental study with numerical results to support our complexity bounds. After proving that BLR multifrontal solvers can achieve a low complexity, we turn to the problem of translating that low complexity in actual performance gains on modern architectures. We first present a multithreaded BLR factorization, and analyze its performance in shared-memory multicore environments on a large set of real-life problems. We put forward several algorithmic properties of the BLR variants necessary to efficiently exploit multicore systems by improving the arithmetic intensity and the scalability of the BLR factorization. We then move on to the distributed-memory BLR factorization, for which additional challenges are identified and addressed. The algorithms presented throughout this thesis have been implemented within the MUMPS solver. We illustrate the use of our approach in three industrial applications coming from geosciences and structural mechanics. We also compare our solver with the STRUMPACK package, based on Hierarchically Semi-Separable approximations. We conclude this thesis by reporting results on a very large problem (130 millions of unknowns) which illustrates future challenges posed by BLR multifrontal solvers at scale
Badreddine, Siwar. "Symétries et structures de rang faible des matrices et tenseurs pour des problèmes en chimie quantique." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS029.
Full textThis thesis presents novel numerical algorithms and conducts a comprehensive study of some existing numerical methods to address high-dimensional challenges arising from the resolution of the electronic Schrödinger equation in quantum chemistry. Focusing on two specific problems, our approach involves the identification and exploitation of symmetries and low-rank structures within matrices and tensors, aiming to mitigate the curse of dimensionality. The first problem considered in this thesis is the efficient numerical evaluation of the long-range component of the range-separated Coulomb potential and the long-range two-electron integrals 4th-order tensor which occurs in many quantum chemistry methods. We present two novel approximation methods. This is achieved by relying on tensorized Chebyshev interpolation, Gaussian quadrature rules combined with low-rank approximations as well as Fast Multipole Methods (FMM). This work offers a detailed explanation of these introduced approaches and algorithms, accompanied by a thorough comparison between the newly proposed methods. The second problem of interest is the exploitation of symmetries and low-rank structures to derive efficient tensor train representations of operators involved in the Density Matrix Renormalization Group (DMRG) algorithm. This algorithm, referred to as the Quantum Chemical DMRG (QC-DMRG) when applied in the field of quantum chemistry, is an accurate iterative optimization method employed to numerically solve the time-independent Schrödinger equation. This work aims to understand and interpret the results obtained from the physics and chemistry communities and seeks to offer novel theoretical insights that, to the best of our knowledge, have not received significant attention before. We conduct a comprehensive study and provide demonstrations, when necessary, to explore the existence of a particular block-sparse tensor train representation of the Hamiltonian operator and its associated eigenfunction. This is achieved while maintaining physical conservation laws, manifested as group symmetries in tensors, such as the conservation of the particle number. The third part of this work is dedicated to the realization of a proof-of-concept Quantum Chemical DMRG (QC-DMRG) Julia library, designed for the quantum chemical Hamiltonian operator model. We exploit here the block-sparse tensor train representation of both the operator and the eigenfunction. With these structures, our goal is to speed up the most time-consuming steps in QC-DMRG, including tensor contractions, matrix-vector operations, and matrix compression through truncated Singular Value Decompositions (SVD). Furthermore, we provide empirical results from various molecular simulations, while comparing the performance of our library with the state-of-the-art ITensors library where we show that we attain a similar performance
Weisbecker, Clément. "Improving multifrontal solvers by means of algebraic Block Low-Rank representations." Phd thesis, Toulouse, INPT, 2013. http://oatao.univ-toulouse.fr/10506/1/weisbecker.pdf.
Full textGerest, Matthieu. "Using Block Low-Rank compression in mixed precision for sparse direct linear solvers." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS447.
Full textIn order to solve large sparse linear systems, one may want to use a direct method, numerically robust but rather costly, both in terms of memory consumption and computation time. The multifrontal method belong to this class algorithms, and one of its high-performance parallel implementation is the solver MUMPS. One of the functionalities of MUMPS is the use of Block Low-Rank (BLR) matrix compression, that improves its performance. In this thesis, we present several new techniques aiming at further improving the performance of dense and sparse direct solvers, on top of using a BLR compression. In particular, we propose a new variant of BLR compression in which several floating-point formats are used simultaneously (mixed precision). Our approach is based on an error analysis, and it first allows to reduce the estimated cost of a LU factorization of a dense matrix, without having a significant impact on the error. Second, we adapt these algorithms to the multifrontal method. A first implementation uses our mixed-precision BLR compression as a storage format only, thus allowing to reduce the memory footprint of MUMPS. A second implementation allows to combine these memory gains with time reductions in the triangular solution phase, by switching computations to low precision. However, we notice performance issues related to BLR for this phase, in case the system has many right-hand sides. Therefore, we propose new BLR variants of triangular solution that improve the data locality and reduce data movements, as highlighted by a communication volume analysis. We implement our algorithms within a simplified prototype and within solver MUMPS. In both cases, we obtain time gains