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Artykuły w czasopismach na temat "Méthode stochastique itérative"
Nyobe, Samuel, Fabien Campillo, Serge Moto i Vivien Rossi. "The one step fixed-lag particle smoother as a strategy to improve the prediction step of particle filtering". Revue Africaine de Recherche en Informatique et Mathématiques Appliquées Volume 39 - 2023 (14.12.2023). http://dx.doi.org/10.46298/arima.10784.
Pełny tekst źródłaRozprawy doktorskie na temat "Méthode stochastique itérative"
Gazagnadou, Nidham. "Expected smoothness for stochastic variance-reduced methods and sketch-and-project methods for structured linear systems". Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT035.
Pełny tekst źródłaThe considerable increase in the number of data and features complicates the learning phase requiring the minimization of a loss function. Stochastic gradient descent (SGD) and variance reduction variants (SAGA, SVRG, MISO) are widely used to solve this problem. In practice, these methods are accelerated by computing these stochastic gradients on a "mini-batch": a small group of samples randomly drawn.Indeed, recent technological improvements allowing the parallelization of these calculations have generalized the use of mini-batches.In this thesis, we are interested in the study of variants of stochastic gradient algorithms with reduced variance by trying to find the optimal hyperparameters: step and mini-batch size. Our study allows us to give convergence results interpolating between stochastic methods drawing a single sample per iteration and the so-called "full-batch" gradient descent using all samples at each iteration. Our analysis is based on the expected smoothness constant which allows to capture the regularity of the random function whose gradient is calculated.We study another class of optimization algorithms: the "sketch-and-project" methods. These methods can also be applied as soon as the learning problem boils down to solving a linear system. This is the case of ridge regression. We analyze here variants of this method that use different strategies of momentum and acceleration. These methods also depend on the sketching strategy used to compress the information of the system to be solved at each iteration. Finally, we show that these methods can also be extended to numerical analysis problems. Indeed, the extension of sketch-and-project methods to Alternating-Direction Implicit (ADI) methods allows to apply them to large-scale problems, when the so-called "direct" solvers are too slow
Faye, Jean-Pierre. "Approche stochastique de la propagation des erreurs d'arrondi dans les méthodes itératives : Application a l'algorithme QR de calcul des valeurs propres". Paris 6, 1987. http://www.theses.fr/1987PA066368.
Pełny tekst źródłaThiéry, Christophe. "Itération sur les politiques optimiste et apprentissage du jeu de Tetris". Thesis, Nancy 1, 2010. http://www.theses.fr/2010NAN10128/document.
Pełny tekst źródłaThis thesis studies policy iteration methods with linear approximation of the value function for large state space problems in the reinforcement learning context. We first introduce a unified algorithm that generalizes the main stochastic optimal control methods. We show the convergence of this unified algorithm to the optimal value function in the tabular case, and a performance bound in the approximate case when the value function is estimated. We then extend the literature of second-order linear approximation algorithms by proposing a generalization of Least-Squares Policy Iteration (LSPI) (Lagoudakis and Parr, 2003). Our new algorithm, Least-Squares [lambda] Policy Iteration (LS[lambda]PI), adds to LSPI an idea of [lambda]-Policy Iteration (Bertsekas and Ioffe, 1996): the damped (or optimistic) evaluation of the value function, which allows to reduce the variance of the estimation to improve the sampling efficiency. Thus, LS[lambda]PI offers a bias-variance trade-off that may improve the estimation of the value function and the performance of the policy obtained. In a second part, we study in depth the game of Tetris, a benchmark application that several works from the literature attempt to solve. Tetris is a difficult problem because of its structure and its large state space. We provide the first full review of the literature that includes reinforcement learning works, evolutionary methods that directly explore the policy space and handwritten controllers. We observe that reinforcement learning is less successful on this problem than direct policy search approaches such as the cross-entropy method (Szita et Lorincz, 2006). We finally show how we built a controller that outperforms the previously known best controllers, and shortly discuss how it allowed us to win the Tetris event of the 2008 Reinforcement Learning Competition
Acheli, Dalila. "Application de la méthode des sentinelles à quelques problèmes inverses". Compiègne, 1997. http://www.theses.fr/1997COMP1034.
Pełny tekst źródłaThe aim of our work is the application of the sentinels method to salve two inverse problems. It concerns the parameters estimation of two given models using measures undertaken on the process. The framework being nonlinear, the estimation of the parameters is performed in an iterative manner by the tangent sentinels method. The first problem concerns the environment for which the parameters to be estimated are the coordinates of the pollution source trajectory in a river. The pollution phenomenon is governed by a PDE's system. The second problem studied, deals with the medical framework. The aim is to estimate the kinetic parameters in the enzymatic reaction. The model considered here is a differential equations system. First, we show the existence and the uniqueness of the system solution. Then, we study the stability of the solution using the Lyapounov function. Assuming certain hypotheses, the global identifiability of parameters based on the differential algebra and Taylor development is shown. We also give a detailed study of the observation sensitivity with respect to the model parameters. Ln order to check the efficiency of this method, some tests were clone on noised data. This method becomes deficient as the noise on measures becomes important. Ln this case the inverse problem is ill-posed because a perturbation in the data will implies an important change in the solution. Among the techniques employed to improve the conditionement of the problem, the Gauss-Newton iterative regularization technique remains inefficient. Therefore, we propose a new approach of regularization, called "iterative regularized Tichonov method". Some tests were conducted on different types of experiences in pharmacokinetic. They show that this approach is robust with respect to noise measures and allows good parameters identification
Thiery, Christophe. "Itération sur les Politiques Optimiste et Apprentissage du Jeu de Tetris". Phd thesis, Université Henri Poincaré - Nancy I, 2010. http://tel.archives-ouvertes.fr/tel-00550081.
Pełny tekst źródłaFernandes, Paulo. "Méthodes numériques pour la solution de systèmes Markoviens à grand espace d'états". Phd thesis, 1998. http://tel.archives-ouvertes.fr/tel-00004886.
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