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Concept

Base de Gröbner

Résumé

En mathématiques, une base de Gröbner (ou base standard, ou base de Buchberger) d'un idéal I de l'anneau de polynômes K[X, …, X] est un ensemble de générateurs de cet idéal, vérifiant certaines propriétés supplémentaires. Cette notion a été introduite dans les années 1960, indépendamment par Heisuke Hironaka et Bruno Buchberger, qui lui a donné le nom de son directeur de thèse Wolfgang Gröbner. Les bases de Gröbner ont le grand avantage de ramener l'étude des idéaux polynomiaux à l'étude des idéaux monomiaux (c'est-à-dire formés de monômes), plus faciles à appréhender. Intérêt Soit K un corps commutatif. Dans le cas des polynômes à une seule variable, l'anneau des polynômes à une variable K[X] est euclidien, et un idéal I de K[X] se représente naturellement par son générateur principal. Plus précisément, l'algorithme d'Euclide permet de déterminer celui-ci à partir d'une famille finie de générateurs, et ainsi de tester l'appartenance d'un polynôme à I, ou encore de calc

Source officielle

https://fr.wikipedia.org/wiki/Base_de_Gr%C3%B6bner

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Concepts associés (39)

La géométrie algébrique est un domaine des mathématiques qui, historiquement, s'est d'abord intéressé à des objets géométriques (courbes, surfaces…) composés des points dont les coordonnées vérifiai

En mathématiques, un système d'équations algébriques est un ensemble d'équations polynomiales f1 = 0..., fh = 0 où les fi sont des polynômes de plusieurs variables (ou indéterminées), x1..., xn, à c

Une variété algébrique est, de manière informelle, l'ensemble des racines communes d'un nombre fini de polynômes en plusieurs indéterminées. C'est l'objet d'étude de la géométrie algébrique. Les sch

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This course complements the Analysis and Linear Algebra courses by providing further mathematical background and practice required for 3rd year physics courses, in particular electrodynamics and quantum mechanics.

L'objectif du cours est d'introduire les notions de base de l'algèbre linéaire et ses applications.

The purpose of the course is to introduce the basic notions of linear algebra and its applications.

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Séances de cours associées (4)

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Fast global spectral methods for three-dimensional partial differential equations

Daniel Kressner, Christoph Max Strössner

Global spectral methods offer the potential to compute solutions of partial differential equations numerically to very high accuracy. In this work, we develop a novel global spectral method for linear partial differential equations on cubes by extending the ideas of Chebop2 (Townsend, A. & Olver, S. (2015) The automatic solution of partial differential equations using a global spectral method. J. Comput. Phys., 299, 106-123) to the three-dimensional setting utilizing expansions in tensorized polynomial bases. Solving the discretized partial differential equation involves a linear system that can be recast as a linear tensor equation. Under suitable additional assumptions, the structure of these equations admits an efficient solution via the blocked recursive solver (Chen, M. & Kressner, D. (2020) Recursive blocked algorithms for linear systems with Kronecker product structure. Numer. Algorithms, 84, 1199-1216). In the general case, when these assumptions are not satisfied, this solver is used as a preconditioner to speed up computations.

OXFORD UNIV PRESS2022

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Super-resolution from unregistered aliased images

Patrick Vandewalle

Aliasing in images is often considered as a nuisance. Artificial low frequency patterns and jagged edges appear when an image is sampled at a too low frequency. However, aliasing also conveys useful information about the high frequency content of the image, which is exploited in super-resolution applications. We use a set of input images of the same scene to extract such high frequency information and create a higher resolution aliasing-free image. Typically, there is a small shift or more complex motion between the different images, such that they contain slightly different information about the scene. Super-resolution image reconstruction can be formulated as a multichannel sampling problem with unknown offsets. This results in a set of equations that are linear in the unknown signal coefficients but nonlinear in the offsets. This thesis concentrates on the computation of these offsets, as they are an essential prerequisite for an accurate high resolution reconstruction. If a part of the image spectra is free of aliasing, the planar shift and rotation parameters can be computed using only this low frequency information. In such a case, the images can be registered pairwise to a reference image. Such a method is not applicable if the images are undersampled by a factor of two or larger. A higher number of images needs to be registered jointly. Two subspace methods are discussed for such highly aliased images. The first approach is based on a Fourier description of the aliased signals as a sum of overlapping parts of the spectrum. It uses a rank condition to find the correct offsets. The second one uses a more general expansion in an arbitrary Hilbert space to compute the signal offsets. The sampled signal is represented as a linear combination of sampled basis functions. The offsets are computed by projecting the signal onto varying subspaces. Under certain conditions, in particular for bandlimited signals, the nonlinear super-resolution equations can be written as a set of polynomial equations. Using Buchberger's algorithm, the solution can then be computed as a Gröbner basis for the corresponding polynomial ideal. After a description of a standard algorithm, adaptations are made for the use with noisy measurements. The techniques presented in this thesis are tested in simulations and practical experiments. The experiments are performed on sets of real images taken with a digital camera. The results show the validity of the algorithms: registration parameters are computed with subpixel precision, and aliasing is accurately removed from the resulting high resolution image. This thesis is produced according to the concepts of reproducible research. All the results and examples used in this thesis are reproducible using the code and data available online.

EPFL2006

Chargement

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In multichannel sampling, several sets of sub-Nyquist sampled signal values are acquired. The offsets between the sets are unknown, and have to be resolved, just like the parameters of the signal itself. This problem is nonlinear in the offsets, but linear in the signal parameters. We show that when the basis functions for the signal space are related to polynomials, we can express the joint offset and signal parameter estimation as a set of polynomial equations. This is the case for example with polynomial signals or Fourier series. The unknown offsets and signal parameters can be computed exactly from such a set of polynomials using Gröbner bases and Buchberger's algorithm. This solution method is developed in detail after a short and tutorial overview of Gröbner basis methods. We then address the case of noisy samples, and consider the computational complexity, exploring simplifications due to the special structure of the problem.

Elsevier2008

Chargement

Super-resolution from unregistered aliased images

Patrick Vandewalle

EPFL2006