Texte remanié de: Th. doct.--Sc. de l'éduc.--Aix-Marseille 1, 1996. Titre de soutenance : La régulation du projet professionnel dans la formation en alternance des cadres : une approche herméneutique. / Bibliogr. p. 127-133.

Techn. Hochsch., Diss., 2002--Aachen.

Techn. Hochsch., Diss., 2004--Aachen.

The thesis is devoted to the study of in-chain impurities in spin 1/2 antiferromagnetic Heisenberg chains (S=1/2 aHC's)---a model which accompanies the research on magnetism since the early days of quantum theory and which is one of the few integrable spin systems. With respect to impurities it is special insofar as an impurity perturbs the system strongly due to its topology: there is no way around the defect. To what extend the one-dimensional picture stays a good basis for the description of real materials even if the chains are disturbed by in-chain impurities is an interesting question which is addressed in this work. For this purpose, Cu Nuclear Magnetic Resonance (NMR) measurements on the cuprate spin chain compounds SrCuO2 and Sr2CuO3 intentionally doped with nickel (Ni), zinc (Zn) and palladium (Pd) are presented. These materials are well known to be among the best realizations of the S=1/2 aHC model and their large exchange coupling constants allow the investigation of the low-energy dynamics within experimentally easily feasible temperatures. NMR provides the unique ability to study the static and dynamic magnetic properties of the spin chains locally which is important since randomly placed impurities break the translational invariance. Because copper is the magnetically active ion in those materials and the copper nuclear spin is most directly coupled to its electron spin, the NMR measurements have been performed on the copper site. The measurements show in all cases that there are changes in the results of these measurements as compared to the pure compounds which indicate the opening of gaps in the excitation spectra of the spin chains and the emergence of oscillations of the local susceptibility close to the impurities. These experimental observations are compared to theoretical predictions to clarify if and to what extend the already proposed model for these doped systems---the finite spin chain---is suitable to predict the behavior of real materials. Thereby, each impurity shows peculiarities. While Zn and Pd are know to be spin 0 impurities, it is not clear if Ni carries spin 1. To shed some light on this issue is another scope of this work. For Zn impurities, there are indications that they avoid to occupy copper sites, other than in the layered cuprate compounds. Also this matter is considered.

Quantification of stochastic or quantum systems by a joint probability density or wave function is a notoriously difficult computational problem, since the solution depends on all possible states (or realizations) of the system. Due to this combinatorial flavor, even a system containing as few as ten particles may yield as many as $10^{10}$ discretized states. None of even modern supercomputers are capable to cope with this curse of dimensionality straightforwardly, when the amount of quantum particles, for example, grows up to more or less interesting order of hundreds. A traditional approach for a long time was to avoid models formulated in terms of probabilistic functions, and simulate particular system realizations in a randomized process. Since different times in different communities, data-sparse methods came into play. Generally, they aim to define all data points indirectly, by a map from a low amount of representers, and recast all operations (e.g. linear system solution) from the initial data to the effective parameters. The most advanced techniques can be applied (at least, tried) to any given array, and do not rely explicitly on its origin. The current work contributes further progress to this area in the particular direction: tensor product methods for separation of variables. The separation of variables has a long history, and is based on the following elementary concept: a function of many variables may be expanded as a product of univariate functions. On the discrete level, a function is encoded by an array of its values, or a tensor. Therefore, instead of a huge initial array, the separation of variables allows to work with univariate factors with much less efforts. The dissertation contains a short overview of existing tensor representations: canonical PARAFAC, Hierarchical Tucker, Tensor Train (TT) formats, as well as the artificial tensorisation, resulting in the Quantized Tensor Train (QTT) approximation method. The contribution of the dissertation consists in both theoretical constructions and practical numerical algorithms for high-dimensional models, illustrated on the examples of the Fokker-Planck and the chemical master equations. Both arise from stochastic dynamical processes in multiconfigurational systems, and govern the evolution of the probability function in time. A special focus is put on time propagation schemes and their properties related to tensor product methods. We show that these applications yield large-scale systems of linear equations, and prove analytical separable representations of the involved functions and operators. We propose a new combined tensor format (QTT-Tucker), which descends from the TT format (hence TT algorithms may be generalized smoothly), but provides complexity reduction by an order of magnitude. We develop a robust iterative solution algorithm, constituting most advantageous properties of the classical iterative methods from numerical analysis and alternating density matrix renormalization group (DMRG) techniques from quantum physics. Numerical experiments confirm that the new method is preferable to DMRG algorithms. It is as fast as the simplest alternating schemes, but as reliable and accurate as the Krylov methods in linear algebra.

The thesis is devoted to the study of in-chain impurities in spin 1/2 antiferromagnetic Heisenberg chains (S=1/2 aHC's)---a model which accompanies the research on magnetism since the early days of quantum theory and which is one of the few integrable spin systems. With respect to impurities it is special insofar as an impurity perturbs the system strongly due to its topology: there is no way around the defect. To what extend the one-dimensional picture stays a good basis for the description of real materials even if the chains are disturbed by in-chain impurities is an interesting question which is addressed in this work. For this purpose, Cu Nuclear Magnetic Resonance (NMR) measurements on the cuprate spin chain compounds SrCuO2 and Sr2CuO3 intentionally doped with nickel (Ni), zinc (Zn) and palladium (Pd) are presented. These materials are well known to be among the best realizations of the S=1/2 aHC model and their large exchange coupling constants allow the investigation of the low-energy dynamics within experimentally easily feasible temperatures. NMR provides the unique ability to study the static and dynamic magnetic properties of the spin chains locally which is important since randomly placed impurities break the translational invariance. Because copper is the magnetically active ion in those materials and the copper nuclear spin is most directly coupled to its electron spin, the NMR measurements have been performed on the copper site. The measurements show in all cases that there are changes in the results of these measurements as compared to the pure compounds which indicate the opening of gaps in the excitation spectra of the spin chains and the emergence of oscillations of the local susceptibility close to the impurities. These experimental observations are compared to theoretical predictions to clarify if and to what extend the already proposed model for these doped systems---the finite spin chain---is suitable to predict the behavior of real materials. Thereby, each impurity shows peculiarities. While Zn and Pd are know to be spin 0 impurities, it is not clear if Ni carries spin 1. To shed some light on this issue is another scope of this work. For Zn impurities, there are indications that they avoid to occupy copper sites, other than in the layered cuprate compounds. Also this matter is considered.

Quantification of stochastic or quantum systems by a joint probability density or wave function is a notoriously difficult computational problem, since the solution depends on all possible states (or realizations) of the system. Due to this combinatorial flavor, even a system containing as few as ten particles may yield as many as $10^{10}$ discretized states. None of even modern supercomputers are capable to cope with this curse of dimensionality straightforwardly, when the amount of quantum particles, for example, grows up to more or less interesting order of hundreds. A traditional approach for a long time was to avoid models formulated in terms of probabilistic functions, and simulate particular system realizations in a randomized process. Since different times in different communities, data-sparse methods came into play. Generally, they aim to define all data points indirectly, by a map from a low amount of representers, and recast all operations (e.g. linear system solution) from the initial data to the effective parameters. The most advanced techniques can be applied (at least, tried) to any given array, and do not rely explicitly on its origin. The current work contributes further progress to this area in the particular direction: tensor product methods for separation of variables. The separation of variables has a long history, and is based on the following elementary concept: a function of many variables may be expanded as a product of univariate functions. On the discrete level, a function is encoded by an array of its values, or a tensor. Therefore, instead of a huge initial array, the separation of variables allows to work with univariate factors with much less efforts. The dissertation contains a short overview of existing tensor representations: canonical PARAFAC, Hierarchical Tucker, Tensor Train (TT) formats, as well as the artificial tensorisation, resulting in the Quantized Tensor Train (QTT) approximation method. The contribution of the dissertation consists in both theoretical constructions and practical numerical algorithms for high-dimensional models, illustrated on the examples of the Fokker-Planck and the chemical master equations. Both arise from stochastic dynamical processes in multiconfigurational systems, and govern the evolution of the probability function in time. A special focus is put on time propagation schemes and their properties related to tensor product methods. We show that these applications yield large-scale systems of linear equations, and prove analytical separable representations of the involved functions and operators. We propose a new combined tensor format (QTT-Tucker), which descends from the TT format (hence TT algorithms may be generalized smoothly), but provides complexity reduction by an order of magnitude. We develop a robust iterative solution algorithm, constituting most advantageous properties of the classical iterative methods from numerical analysis and alternating density matrix renormalization group (DMRG) techniques from quantum physics. Numerical experiments confirm that the new method is preferable to DMRG algorithms. It is as fast as the simplest alternating schemes, but as reliable and accurate as the Krylov methods in linear algebra.