Let X. Y be compact Hausdorff spaces, and E, F be Banach spaces. A linear map T¡GC (X¡AE)¡÷C (Y¡AF) is separating if ¡üTf(y)¡ü¡üTg(y)¡ü¡×0 whenever ¡üf(x)¡ü¡üg(x)¡ü¡×0, for every x belonging to X, y belonging to Y. Gau, Jeang and Wong prove that a biseparating linear bijection T is a weighted composition oprator Tf¡×hf¡³£p where h is a function from Y into the set of inveritable linear operators from E onto F and £p is a homeomorphism from Y onto X. In this thesis, we extend this result to the case that continuous functions are defined to a locally compact Hausdorff space, which is either £m-compact or first countable. Moreover, we give a short proof of a recent result of Mrcun. Finally, we give an alternative approach to an Araujo's result concerning biseparating maps of smooth functions appeared in Adv. Math.

Cette thèse se consacre aux garanties de reconstruction et de l’analyse de sensibilité de régularisation variationnelle pour des problèmes inverses linéaires bruités. Il s’agit d’un problème d’optimisation convexe combinant un terme d’attache aux données et un terme de régularisation promouvant des solutions vivant dans un espace dit de faible complexité. Notre approche, basée sur la notion de fonctions partiellement lisses, permet l’étude d’une grande variété de régularisations comme par exemple la parcimonie de type analyse ou structurée, l’anti-Parcimonie et la structure de faible rang. Nous analysons tout d’abord la robustesse au bruit, à la fois en termes de distance entre les solutions et l’objet original, ainsi que la stabilité de l’espace modèle promu.Ensuite, nous étudions la stabilité de ces problèmes d’optimisation à des perturbations des observations. A partir d’observations aléatoires, nous construisons un estimateur non biaisé du risque afin d’obtenir un schéma de sélection de paramètre. / This thesis is concerned with recovery guarantees and sensitivity analysis of variational regularization for noisy linear inverse problems. This is cast as aconvex optimization problem by combining a data fidelity and a regularizing functional promoting solutions conforming to some notion of low complexity related to their non-Smoothness points. Our approach, based on partial smoothness, handles a variety of regularizers including analysis/structured sparsity, antisparsity and low-Rank structure. We first give an analysis of thenoise robustness guarantees, both in terms of the distance of the recovered solutions to the original object, as well as the stability of the promoted modelspace. We then turn to sensivity analysis of these optimization problems to observation perturbations. With random observations, we build un biased estimator of the risk which provides a parameter selection scheme.