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In this paper we study numerical approximations of the evolution problem governed by the nonlocal p-Laplacian operator with a given kernel and homogeneous Neumann boundary conditions. More precisely, we consider discretized versions on inhomogeneous random graph sequences, establish their continuum limits and provide error bounds with nonasymptotic rate of convergence of solutions of the discrete problems to their continuum counterparts as the number of vertices grows. Our bounds reveal the role of the different parameters that come into play, and in particular that of p and of the geometry/regularity of the initial data and the kernel.

We introduce a framework for quasi-Newton forward--backward splitting algorithms (proximal quasi-Newton methods) with a metric induced by diagonal $\pm$ rank-$r$ symmetric positive definite matrices. This special type of metric allows for a highly efficient evaluation of the proximal mapping. The key to this efficiency is a general proximal calculus in the new metric. By using duality, formulas are derived that relate the proximal mapping in a rank-$r$ modified metric to the original metric. We also describe efficient implementations of the proximity calculation for a large class of functions; the implementations exploit the piece-wise linear nature of the dual problem. Then, we apply these results to acceleration of composite convex minimization problems, which leads to elegant quasi-Newton methods for which we prove convergence. The algorithm is tested on several numerical examples and compared to a comprehensive list of alternatives in the literature. Our quasi-Newton splitting algorithm with the prescribed metric compares favorably against state-of-the-art. The algorithm has extensive applications including signal processing, sparse recovery, machine learning and classification to name a few., Comment: arXiv admin note: text overlap with arXiv:1206.1156

International audience; We propose a unifying algorithm for non-smooth non-convex optimization. The algorithm approximates the objective function by a convex model function and finds an approximate (Bregman) proximal point of the convex model. This approximate minimizer of the model function yields a descent direction, along which the next iterate is found. Complemented with an Armijo-like line search strategy, we obtain a flexible algorithm for which we prove (subsequential) convergence to a stationary point under weak assumptions on the growth of the model function error. Special instances of the algorithm with a Euclidean distance function are, for example, Gradient Descent, Forward-Backward Splitting, ProxDescent, without the common requirement of a "Lipschitz continuous gradient". In addition, we consider a broad class of Bregman distance functions (generated by Legendre functions), replacing the Euclidean distance. The algorithm has a wide range of applications including many linear and non-linear inverse problems in signal/image processing and machine learning.

National audience; L' équation d'évolution du p-Laplacien non-local, gou-vernée par un noyau donné, a de très nombreuses applications pour modéliser les phénomènes de diffusion, notam-ment en traitement du signal et des images sur graphes. En pratique, cette équation d'évolution est implémenté sous une forme discrète (en temps et en espace) comme une approximation numérique du problème continu, où le noyau est remplacé par la matrice d'adjacence d'un graphe. La question naturelle est alors d'étudier la structure des solutions du problème discret et d'en établir la limite continue. C'est l'objectif poursuivi dans ce travail. En combinant des outils issus de la théorie des graphes et des équations d'évolution non-linéaires, nous donnons une interprétation rigoureuse à la limite continue du probléme du p-Laplacien discret sur graphes. Plus spécifiquement, nous considérons une suite de graphes déterministes, pondérés dont l'objet limite est appelé graphon. L'équation d'évolution du p-Laplacien est alors discrétisée en temps et en espace sur cette suite de graphes. Ainsi, nous prouvons la convergence des solutions de la suite des problèmes discrétisés vers la solution du problème d'évolution continu gouverné par le graphon lorsque le nombre des noeuds du graphe tend vers l'infini. Ce faisant, nous exhibons le vitesse de convergence correspondante. Mots Clef Diffusion nonlocale; p-Laplacien; limites de graphes; approximation numérique. Abstract The non-local p-Laplacian evolution equation, governed by given kernel, has various applications to model diffusion phenomena, in particular in signal and image processing. In practice, such an evolution equation is implemented in discrete form (in space and time) as a numerical approximation to a continuous problem, where the kernel is replaced by an adjacency matrix of graph. The natural question that arises is to understand the structure of solutions to the discrete problem, and study their continuous limit. This is the goal pursued in this work. Combining tools from graph theory and non-linear evolution equations , we give a rigorous interpretation to the continuous limit of the discrete p-Laplacian on graphs. More specifically , we consider a sequence of deterministic weighted graphs converging to a so-called graphon. The continuous p-Laplacian evolution equation is then discretized on this graph sequence both in space and time. We therefore prove that the solutions of the sequence of discrete problems converge to the solution of the continuous evolution problem governed by the graphon, when the number of graph ver-tices grows to infinity. We exhibit the corresponding convergence rate.

In this paper, we present a convergence rate analysis for the inexact Krasnosel'skii-Mann iteration built from nonexpansive operators. Our results include two main parts: we first establish global pointwise and ergodic iteration-complexity bounds, and then, under a metric subregularity assumption, we establish local linear convergence for the distance of the iterates to the set of fixed points. The obtained iteration-complexity result can be applied to analyze the convergence rate of various monotone operator splitting methods in the literature, including the Forward-Backward, the Generalized Forward-Backward, Douglas-Rachford, alternating direction method of multipliers (ADMM) and Primal-Dual splitting methods. For these methods, we also develop easily verifiable termination criteria for finding an approximate solution, which can be seen as a generalization of the termination criterion for the classical gradient descent method. We finally develop a parallel analysis for the non-stationary Krasnosel'skii-Mann iteration. The usefulness of our results is illustrated by applying them to a large class of structured monotone inclusion and convex optimization problems. Experiments on some large scale inverse problems in signal and image processing problems are shown., Comment: This is an extended version of the work presented in http://arxiv.org/abs/1310.6636, and is accepted by the Mathematical Programming

International audience; This article concerns a novel method for sensorless speed control of Permanent Magnet Synchrounous Motor based on adaptive supertwisting sliding mode algorithm. This algorithm is used for motor position observation and for the derivation of the position to deduce the speed. The stability is deduced based on the Lyapunov theory. The Experimental tests, carried out on a test bench, show the pertinence of the proposed scheme.

A $d$-dimensional nonparametric additive regression model with dependent observations is considered. Using the marginal integration technique and wavelets methodology, we develop a new adaptive estimator for a component of the additive regression function. Its asymptotic properties are investigated via the minimax approach under the $\mathbb{L}_2$ risk over Besov balls. We prove that it attains a sharp rate of convergence which turns to be the one obtained in the $\iid$ case for the standard univariate regression estimation problem., Substantial improvement of the estimator and the main theorem