Rozprawy doktorskie na temat „Algorithme de bandit”
Utwórz poprawne odniesienie w stylach APA, MLA, Chicago, Harvard i wielu innych
Sprawdź 50 najlepszych rozpraw doktorskich naukowych na temat „Algorithme de bandit”.
Przycisk „Dodaj do bibliografii” jest dostępny obok każdej pracy w bibliografii. Użyj go – a my automatycznie utworzymy odniesienie bibliograficzne do wybranej pracy w stylu cytowania, którego potrzebujesz: APA, MLA, Harvard, Chicago, Vancouver itp.
Możesz również pobrać pełny tekst publikacji naukowej w formacie „.pdf” i przeczytać adnotację do pracy online, jeśli odpowiednie parametry są dostępne w metadanych.
Przeglądaj rozprawy doktorskie z różnych dziedzin i twórz odpowiednie bibliografie.
Saadane, Sofiane. "Algorithmes stochastiques pour l'apprentissage, l'optimisation et l'approximation du régime stationnaire". Thesis, Toulouse 3, 2016. http://www.theses.fr/2016TOU30203/document.
Pełny tekst źródłaIn this thesis, we are studying severa! stochastic algorithms with different purposes and this is why we will start this manuscript by giving historicals results to define the framework of our work. Then, we will study a bandit algorithm due to the work of Narendra and Shapiro whose objectif was to determine among a choice of severa! sources which one is the most profitable without spending too much times on the wrong orres. Our goal is to understand the weakness of this algorithm in order to propose an optimal procedure for a quantity measuring the performance of a bandit algorithm, the regret. In our results, we will propose an algorithm called NS over-penalized which allows to obtain a minimax regret bound. A second work will be to understand the convergence in law of this process. The particularity of the algorith is that it converges in law toward a non-diffusive process which makes the study more intricate than the standard case. We will use coupling techniques to study this process and propose rates of convergence. The second work of this thesis falls in the scope of optimization of a function using a stochastic algorithm. We will study a stochastic version of the so-called heavy bali method with friction. The particularity of the algorithm is that its dynamics is based on the ali past of the trajectory. The procedure relies on a memory term which dictates the behavior of the procedure by the form it takes. In our framework, two types of memory will investigated : polynomial and exponential. We will start with general convergence results in the non-convex case. In the case of strongly convex functions, we will provide upper-bounds for the rate of convergence. Finally, a convergence in law result is given in the case of exponential memory. The third part is about the McKean-Vlasov equations which were first introduced by Anatoly Vlasov and first studied by Henry McKean in order to mode! the distribution function of plasma. Our objective is to propose a stochastic algorithm to approach the invariant distribution of the McKean Vlasov equation. Methods in the case of diffusion processes (and sorne more general pro cesses) are known but the particularity of McKean Vlasov process is that it is strongly non-linear. Thus, we will have to develop an alternative approach. We will introduce the notion of asymptotic pseudotrajectory in odrer to get an efficient procedure
Faury, Louis. "Variance-sensitive confidence intervals for parametric and offline bandits". Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT046.
Pełny tekst źródłaIn this dissertation we present recent contributions to the problem of optimization under bandit feedback through the design of variance-sensitive confidence intervals. We tackle two distincts topics: (1) the regret minimization task in Generalized Linear Bandits (GLBs), a broad class of non-linear parametric bandits and (2) the problem of off-line policy optimization under bandit feedback. For (1) we study the effects of non-linearity in GLBs and challenge the current understanding that a high level of non-linearity is detrimental to the exploration-exploitation trade-off. We introduce improved algorithms as well as a novel analysis that prove that if correctly handled, the regret minimization task in GLBs is not necessarily harder than for their linear counterparts. It can even be easier for some important members of the GLB family such as the Logistic Bandit. Our approach leverages a new confidence set which captures the non-linearity of the reward signal through its variance, along with a local treatment of the non-linearity through a so-called self-concordance analysis. For (2) we leverage results from the distributionally robust optimization framework to construct asymptotic variance-sensitive confidence intervals for the counterfactual evaluation of policies. This allows to ensure conservatism (sought out by risk-averse agents) while searching off-line for promising policies. Our confidence intervals lead to new counterfactual objectives which, contrary to their predecessors, are more suited for practical deployment thanks to their convex and composite natures
Sani, Amir. "Apprentissage automatique pour la prise de décisions". Thesis, Lille 1, 2015. http://www.theses.fr/2015LIL10038/document.
Pełny tekst źródłaStrategic decision-making over valuable resources should consider risk-averse objectives. Many practical areas of application consider risk as central to decision-making. However, machine learning does not. As a result, research should provide insights and algorithms that endow machine learning with the ability to consider decision-theoretic risk. In particular, in estimating decision-theoretic risk on short dependent sequences generated from the most general possible class of processes for statistical inference and through decision-theoretic risk objectives in sequential decision-making. This thesis studies these two problems to provide principled algorithmic methods for considering decision-theoretic risk in machine learning. An algorithm with state-of-the-art performance is introduced for accurate estimation of risk statistics on the most general class of stationary--ergodic processes and risk-averse objectives are introduced in sequential decision-making (online learning) in both the stochastic multi-arm bandit setting and the adversarial full-information setting
Clement, Benjamin. "Adaptive Personalization of Pedagogical Sequences using Machine Learning". Thesis, Bordeaux, 2018. http://www.theses.fr/2018BORD0373/document.
Pełny tekst źródłaCan computers teach people? To answer this question, Intelligent Tutoring Systems are a rapidly expanding field of research among the Information and Communication Technologies for the Education community. This subject brings together different issues and researchers from various fields, such as psychology, didactics, neurosciences and, particularly, machine learning. Digital technologies are becoming more and more a part of everyday life with the development of tablets and smartphones. It seems natural to consider using these technologies for educational purposes. This raises several questions, such as how to make user interfaces accessible to everyone, how to make educational content motivating and how to customize it to individual learners. In this PhD, we developed methods, grouped in the aptly-named HMABITS framework, to adapt pedagogical activity sequences based on learners' performances and preferences to maximize their learning speed and motivation. These methods use computational models of intrinsic motivation and curiosity-driven learning to identify the activities providing the highest learning progress and use Multi-Armed Bandit algorithms to manage the exploration/exploitation trade-off inside the activity space. Activities of optimal interest are thus privileged with the target to keep the learner in a state of Flow or in his or her Zone of Proximal Development. Moreover, some of our methods allow the student to make choices about contextual features or pedagogical content, which is a vector of self-determination and motivation. To evaluate the effectiveness and relevance of our algorithms, we carried out several types of experiments. We first evaluated these methods with numerical simulations before applying them to real teaching conditions. To do this, we developed multiple models of learners, since a single model never exactly replicates the behavior of a real learner. The simulation results show the HMABITS framework achieves comparable, and in some cases better, learning results than an optimal solution or an expert sequence. We then developed our own pedagogical scenario and serious game to test our algorithms in classrooms with real students. We developed a game on the theme of number decomposition, through the manipulation of money, for children aged 6 to 8. We then worked with the educational institutions and several schools in the Bordeaux school district. Overall, about 1000 students participated in trial lessons using the tablet application. The results of the real-world studies show that the HMABITS framework allows the students to do more diverse and difficult activities, to achieve better learning and to be more motivated than with an Expert Sequence. The results show that this effect is even greater when the students have the possibility to make choices
Maillard, Odalric-Ambrym. "APPRENTISSAGE SÉQUENTIEL : Bandits, Statistique et Renforcement". Phd thesis, Université des Sciences et Technologie de Lille - Lille I, 2011. http://tel.archives-ouvertes.fr/tel-00845410.
Pełny tekst źródłaDorard, L. R. M. "Bandit algorithms for searching large spaces". Thesis, University College London (University of London), 2012. http://discovery.ucl.ac.uk/1348319/.
Pełny tekst źródłaJedor, Matthieu. "Bandit algorithms for recommender system optimization". Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM027.
Pełny tekst źródłaIn this PhD thesis, we study the optimization of recommender systems with the objective of providing more refined suggestions of items for a user to benefit.The task is modeled using the multi-armed bandit framework.In a first part, we look upon two problems that commonly occured in recommendation systems: the large number of items to handle and the management of sponsored contents.In a second part, we investigate the empirical performance of bandit algorithms and especially how to tune conventional algorithm to improve results in stationary and non-stationary environments that arise in practice.This leads us to analyze both theoretically and empirically the greedy algorithm that, in some cases, outperforms the state-of-the-art
Besson, Lilian. "Multi-Players Bandit Algorithms for Internet of Things Networks". Thesis, CentraleSupélec, 2019. http://www.theses.fr/2019CSUP0005.
Pełny tekst źródłaIn this PhD thesis, we study wireless networks and reconfigurable end-devices that can access Cognitive Radio networks, in unlicensed bands and without central control. We focus on Internet of Things networks (IoT), with the objective of extending the devices’ battery life, by equipping them with low-cost but efficient machine learning algorithms, in order to let them automatically improve the efficiency of their wireless communications. We propose different models of IoT networks, and we show empirically on both numerical simulations and real-world validation the possible gain of our methods, that use Reinforcement Learning. The different network access problems are modeled as Multi-Armed Bandits (MAB), but we found that analyzing the realistic models was intractable, because proving the convergence of many IoT devices playing a collaborative game, without communication nor coordination is hard, when they all follow random activation patterns. The rest of this manuscript thus studies two restricted models, first multi-players bandits in stationary problems, then non-stationary single-player bandits. We also detail another contribution, SMPyBandits, our open-source Python library for numerical MAB simulations, that covers all the studied models and more
Deffayet, Romain. "Bandit Algorithms for Adaptive Modulation and Coding in Wireless Networks". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281884.
Pełny tekst źródłaEfterfrågan på mobilnät av hög kvalitet har ökat mycket de senaste åren och kommer att fortsätta öka under en nära framtid. Detta är resultatet av en ökad mängd trafik på grund av nya användningsfall (HD-videor, live streaming, onlinespel, ...) men kommer också från en diversifiering av trafiken, i synnerhet på grund av kortare och mer frekventa sändningar vilka kan vara på grund av IOT-enheter eller andra telemetri-applikationer. Mobilnätet blir allt komplexare och behovet av bättre hantering av nätverkets egenskaper är högre än någonsin. Den kombinerade effekten av dessa två paradigmer skapar en avvägning: medan man vill utforma algoritmer som uppnår mycket hög prestanda vid beslutsfattning, skulle man också vilja att algoritmerna kan göra det i alla konfigurationer som kan uppstå i detta komplexa nätverk. Istället föreslår denna avhandling att begränsa omfattningen av beslutsalgoritmerna genom att introducera online-inlärning. Avhandlingen fokuserar på första MCS-valet i Adaptiv Modulering och Kodning, där man måste välja en initial överföringshastighet som garanterar snabb kommunikation och minsta möjliga transmissionsfel. Vi formulerar problemet som ett Reinforcement Learning problem och föreslår relevanta begränsningar för matematikt enklare ramverk som Multi-Armed Bandits och Contextual Bandits. Åtta banditalgoritmer testas och granskas med hänsyn till praktisk tillämpning. Avhandlingen visar att en Reinforcement Learning agent kan förbättra användningen av länkkapaciteten mellan sändare och mottagare. Först presenterar vi en Multi-Armed Bandit agent på cell-nivå, som lär sig den optimala initiala MCSen i en given cell och sedan en kontextuell utvidgning av dennaa agent med användarspecifika funktioner. Den föreslagna metoden uppnår en åttaprocentig (8%) ökning av medianhastigheten och en sextiofemprocentig (65%) minskning av median ångern vid skurvis trafik det första 0.5s av tranmissionen, jämfört med ett fast referensvärde.
Degenne, Rémy. "Impact of structure on the design and analysis of bandit algorithms". Thesis, Université de Paris (2019-....), 2019. http://www.theses.fr/2019UNIP7179.
Pełny tekst źródłaIn this Thesis, we study sequential learning problems called stochastic multi-armed bandits. First a new bandit algorithm is presented. The analysis of that algorithm uses confidence intervals on the mean of the arms reward distributions, as most bandit proofs do. In a parametric setting, we derive concentration inequalities which quantify the deviation between the mean parameter of a distribution and its empirical estimation in order to obtain confidence intervals. These inequalities are presented as bounds on the Kullback-Leibler divergence. Three extensions of the stochastic multi-armed bandit problem are then studied. First we study the so-called combinatorial semi-bandit problem, in which an algorithm chooses a set of arms and the reward of each of these arms is observed. The minimal attainable regret then depends on the correlation between the arm distributions. We consider then a setting in which the observation mechanism changes. One source of difficulty of the bandit problem is the scarcity of information: only the arm pulled is observed. We show how to use efficiently eventual supplementary free information (which do not influence the regret). Finally a new family of algorithms is introduced to obtain both regret minimization and est arm identification regret guarantees. Each algorithm of the family realizes a trade-off between regret and time needed to identify the best arm. In a second part we study the so-called pure exploration problem, in which an algorithm is not evaluated on its regret but on the probability that it returns a wrong answer to a question on the arm distributions. We determine the complexity of such problems and design with performance close to that complexity
Nicol, Olivier. "Data-driven evaluation of contextual bandit algorithms and applications to dynamic recommendation". Thesis, Lille 1, 2014. http://www.theses.fr/2014LIL10211/document.
Pełny tekst źródłaThe context of this thesis work is dynamic recommendation. Recommendation is the action, for an intelligent system, to supply a user of an application with personalized content so as to enhance what is refered to as "user experience" e.g. recommending a product on a merchant website or even an article on a blog. Recommendation is considered dynamic when the content to recommend or user tastes evolve rapidly e.g. news recommendation. Many applications that are of interest to us generates a tremendous amount of data through the millions of online users they have. Nevertheless, using this data to evaluate a new recommendation technique or even compare two dynamic recommendation algorithms is far from trivial. This is the problem we consider here. Some approaches have already been proposed. Nonetheless they were not studied very thoroughly both from a theoretical point of view (unquantified bias, loose convergence bounds...) and from an empirical one (experiments on private data only). In this work we start by filling many blanks within the theoretical analysis. Then we comment on the result of an experiment of unprecedented scale in this area: a public challenge we organized. This challenge along with a some complementary experiments revealed a unexpected source of a huge bias: time acceleration. The rest of this work tackles this issue. We show that a bootstrap-based approach allows to significantly reduce this bias and more importantly to control it
Zhong, Hongliang. "Bandit feedback in Classification and Multi-objective Optimization". Thesis, Ecole centrale de Marseille, 2016. http://www.theses.fr/2016ECDM0004/document.
Pełny tekst źródłaBandit problems constitute a sequential dynamic allocation problem. The pulling agent has to explore its environment (i.e. the arms) to gather information on the one hand, and it has to exploit the collected clues to increase its rewards on the other hand. How to adequately balance the exploration phase and the exploitation phase is the crux of bandit problems and most of the efforts devoted by the research community from this fields has focused on finding the right exploitation/exploration tradeoff. In this dissertation, we focus on investigating two specific bandit problems: the contextual bandit problems and the multi-objective bandit problems. This dissertation provides two contributions. The first contribution is about the classification under partial supervision, which we encode as a contextual bandit problem with side informa- tion. This kind of problem is heavily studied by researchers working on social networks and recommendation systems. We provide a series of algorithms to solve the Bandit feedback problem that pertain to the Passive-Aggressive family of algorithms. We take advantage of its grounded foundations and we are able to show that our algorithms are much simpler to implement than state-of-the-art algorithms for bandit with partial feedback, and they yet achieve better perfor- mances of classification. For multi-objective multi-armed bandit problem (MOMAB), we propose an effective and theoretically motivated method to identify the Pareto front of arms. We in particular show that we can find all elements of the Pareto front with a minimal budget
Gajane, Pratik. "Multi-armed bandits with unconventional feedback". Thesis, Lille 3, 2017. http://www.theses.fr/2017LIL30045/document.
Pełny tekst źródłaThe multi-armed bandit (MAB) problem is a mathematical formulation of the exploration-exploitation trade-off inherent to reinforcement learning, in which the learner chooses an action (symbolized by an arm) from a set of available actions in a sequence of trials in order to maximize their reward. In the classical MAB problem, the learner receives absolute bandit feedback i.e. it receives as feedback the reward of the arm it selects. In many practical situations however, different kind of feedback is more readily available. In this thesis, we study two of such kinds of feedbacks, namely, relative feedback and corrupt feedback.The main practical motivation behind relative feedback arises from the task of online ranker evaluation. This task involves choosing the optimal ranker from a finite set of rankers using only pairwise comparisons, while minimizing the comparisons between sub-optimal rankers. This is formalized by the MAB problem with relative feedback, in which the learner selects two arms instead of one and receives the preference feedback. We consider the adversarial formulation of this problem which circumvents the stationarity assumption over the mean rewards for the arms. We provide a lower bound on the performance measure for any algorithm for this problem. We also provide an algorithm called "Relative Exponential-weight algorithm for Exploration and Exploitation" with performance guarantees. We present a thorough empirical study on several information retrieval datasets that confirm the validity of these theoretical results.The motivating theme behind corrupt feedback is that the feedback the learner receives is a corrupted form of the corresponding reward of the selected arm. Practically such a feedback is available in the tasks of online advertising, recommender systems etc. We consider two goals for the MAB problem with corrupt feedback: best arm identification and exploration-exploitation. For both the goals, we provide lower bounds on the performance measures for any algorithm. We also provide various algorithms for these settings. The main contribution of this module is the algorithms "KLUCB-CF" and "Thompson Sampling-CF" which asymptotically attain the best possible performance. We present experimental results to demonstrate the performance of these algorithms. We also show how this problem setting can be used for the practical application of enforcing differential privacy
Soare, Marta. "Sequential resources allocation in linear stochastic bandits". Thesis, Lille 1, 2015. http://www.theses.fr/2015LIL10147/document.
Pełny tekst źródłaThis thesis is dedicated to the study of resource allocation problems in uncertain environments, where an agent can sequentially select which action to take. After each step, the environment returns a noisy observation of the value of the selected action. These observations guide the agent in adapting his resource allocation strategy towards reaching a given objective. In the most typical setting of this kind, the stochastic multi-armed bandit (MAB), it is assumed that each observation is drawn from an unknown probability distribution associated with the selected action and gives no information on the expected value of the other actions. This setting has been widely studied and optimal allocation strategies were proposed to solve various objectives under the MAB assumptions. Here, we consider a variant of the MAB setting where there exists a global linear structure in the environment and by selecting an action, the agent also gathers information on the value of the other actions. Therefore, the agent needs to adapt his resource allocation strategy to exploit the structure in the environment. In particular, we study the design of sequences of actions that the agent should take to reach objectives such as: (i) identifying the best value with a fixed confidence and using a minimum number of pulls, or (ii) minimizing the prediction error on the value of each action. In addition, we investigate how the knowledge gathered by a bandit algorithm in a given environment can be transferred to improve the performance in other similar environments
Brégère, Margaux. "Stochastic bandit algorithms for demand side management Simulating Tariff Impact in Electrical Energy Consumption Profiles with Conditional Variational Autoencoders Online Hierarchical Forecasting for Power Consumption Data Target Tracking for Contextual Bandits : Application to Demand Side Management". Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM022.
Pełny tekst źródłaAs electricity is hard to store, the balance between production and consumption must be strictly maintained. With the integration of intermittent renewable energies into the production mix, the management of the balance becomes complex. At the same time, the deployment of smart meters suggests demand response. More precisely, sending signals - such as changes in the price of electricity - would encourage users to modulate their consumption according to the production of electricity. The algorithms used to choose these signals have to learn consumer reactions and, in the same time, to optimize them (exploration-exploration trade-off). Our approach is based on bandit theory and formalizes this sequential learning problem. We propose a first algorithm to control the electrical demand of a homogeneous population of consumers and offer T⅔ upper bound on its regret. Experiments on a real data set in which price incentives were offered illustrate these theoretical results. As a “full information” dataset is required to test bandit algorithms, a consumption data generator based on variational autoencoders is built. In order to drop the assumption of the population homogeneity, we propose an approach to cluster households according to their consumption profile. These different works are finally combined to propose and test a bandit algorithm for personalized demand side management
Bouttier, Clément. "Optimisation globale sous incertitude : algorithmes stochastiques et bandits continus avec application aux performances avion". Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30176.
Pełny tekst źródłaThis PhD thesis is dedicated to the theoretical and numerical analysis of stochastic algorithms for the stochastic flight planning problem. Optimizing the fuel consumption and flight time is a key factor for airlines to be competitive. These companies thus look for flight optimization tools with higher and higher accuracy requirements. However, nowadays available methodologies for flight planning are based on simplified aircraft performance models. In this PhD, we propose to fulfill the accuracy requirements by adapting our methodology to both the constraints induced by the utilization of an industrial aircraft performance computation code and the consideration of the uncertainty about the real flight conditions, i.e., air traffic and weather conditions. Our proposal is supported by three main contributions. First, we design a numerical framework for benchmarking aircraft trajectory optimization tools. This provides us a unified testing procedure for all aircraft performance models. Second, we propose and study (both theoretically and numerically) two global derivative-free algorithms for stochastic optimization problems. The first approach, the NSA algorithm, is highly generic and does not use any prior knowledge about the aircraft performance model. It is an extension of the simulated annealing algorithm adapted to noisy cost functions. We provide an upper bound on the convergence speed of NSA to globally optimal solutions. The second approach, the SPY algorithm, is a Lipschitz bandit algorithm derived from Piyavskii's algorithm. It is more specific as it requires the knowledge of some Lipschitz regularity property around the optimum, but it is therefore far more efficient. We also provide a theoretical study of this algorithm through an upper bound on its simple regret
Gisselbrecht, Thibault. "Algorithmes de bandits pour la collecte d’informations en temps réel dans les réseaux sociaux". Electronic Thesis or Diss., Paris 6, 2017. http://www.theses.fr/2017PA066655.
Pełny tekst źródłaIn this thesis, we study the problem of real time data capture on social media. Due to the different limitations imposed by those media, but also to the very large amount of information, it is not possible to collect all the data produced by social networks such as Twitter. Therefore, to be able to gather enough relevant information related to a predefined need, it is necessary to focus on a subset of the information sources. In this work, we focus on user-centered data capture and consider each account of a social network as a source that can be listened to at each iteration of a data capture process, in order to collect the corresponding produced contents. This process, whose aim is to maximize the quality of the information gathered, is constrained at each time step by the number of users that can be monitored simultaneously. The problem of selecting a subset of accounts to listen to over time is a sequential decision problem under constraints, which we formalize as a bandit problem with multiple selections. Therefore, we propose several bandit models to identify the most relevant users in real time. First, we study of the case of the so-called stochastic bandit, in which each user corresponds to a stationary distribution. Then, we introduce two contextual banditmodels, one stationary and the other non stationary, in which the utility of each user can be estimated more efficiently by assuming some underlying structure in the reward space. In particular, the first approach introduces the notion of profile, which corresponds to the average behavior of each user. On the other hand, the second approach takes into account the activity of a user at a given instant in order to predict his future behavior. Finally, we are interested in models that are able to take into account complex temporal dependencies between users, with the use of a latent space within which the information transits from one iteration to the other. Moreover, each of the proposed approaches is validated on both artificial and real datasets
Cuvelier, Thibaut. "Polynomial-Time Algorithms for Combinatorial Semibandits : Computationally Tractable Reinforcement Learning in Complex Environments". Electronic Thesis or Diss., université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG020.
Pełny tekst źródłaSequential decision making is a core component of many real-world applications, from computer-network operations to online ads. The major tool for this use is reinforcement learning: an agent takes a sequence of decisions in order to achieve its goal, with typically noisy measurements of the evolution of the environment. For instance, a self-driving car can be controlled by such an agent; the environment is the city in which the car manœuvers. Bandit problems are a class of reinforcement learning for which very strong theoretical properties can be shown. The focus of bandit algorithms is on the exploration-exploitation dilemma: in order to have good performance, the agent must have a deep knowledge of its environment (exploration); however, it should also play actions that bring it closer to its goal (exploitation).In this dissertation, we focus on combinatorial bandits, which are bandits whose decisions are highly structured (a "combinatorial" structure). These include cases where the learning agent determines a path to follow (on a road, in a computer network, etc.) or ads to display on a Website. Such situations share their computational complexity: while it is often easy to determine the optimum decision when the parameters are known (the time to cross a road, the monetary gain of displaying an ad at a given place), the bandit variant (when the parameters must be determined through interactions with the environment) is more complex.We propose two new algorithms to tackle these problems by mathematical-optimisation techniques. Based on weak hypotheses, they have a polynomial time complexity, and yet perform well compared to state-of-the-art algorithms for the same problems. They also enjoy excellent statistical properties, meaning that they find a balance between exploration and exploitation that is close to the theoretical optimum. Previous work on combinatorial bandits had to make a choice between computational burden and statistical performance; our algorithms show that there is no need for such a quandary
Martins, Renato Juliano. "Controle coerente das bandas de emissão do ZnO através de algoritmo genético". Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/76/76131/tde-12032012-081648/.
Pełny tekst źródłaIn this work, we investigate the coherent control of the emission bands, excited via multiphoton absorption, in a zinc oxide crystal (ZnO) by pulse shaping ultrashort laser pulses (790 nm, 30 fs, 5 nJ and 80 MHz). ZnO has been preposed as a potential material for photonic devices due to its strong exciton binding energy(60 meV). Initially, we have implemented the pulse shaper experimental setup, as well as the fluorescence measurements of ZnO. The coherent control was carried out through genetic algorithm (GA) based software, also developed in the course of this work. Using the genetic algorithm, we have observed a significant increase in the ZnO emission when appropriated spectral phase masks are applied to the laser pulse. Autocorrelation measurements were used to infer the pulse duration, which get longer after optimization of the emission band via GA. Additionally, we have found that the phase masks that optimize the process are complex oscillatory functions. Through the Principal Component Analysis, we analyzed the data provided by the GA and observed that it can be used to filter the data, smoothing the curves and highlighting the most important aspects of phase masks obtained by the coherent control. Finally we investigate how important the smoothed masks are for the physical understanding of the process.
Meurisse, Yann. "Contributions à l'étude de performances statistiques de réseaux d'antennes". Paris 11, 2002. http://www.theses.fr/2002PA112209.
Pełny tekst źródłaIn antenna array processing, statistical performance of the estimation methods of direction of arrival (DOA) of sources are generally evaluated by assuming on one band that the antenna arrays are linear with a regular uniform repartition of the sensors and, on the other hand, that sources and noise signals are gaussian, temporally uncorrelated and independent among each other. The observed signals are on the other hand supposed to be narrowband or wideband, leading to specific methods of different complexity. The general objective of our work is to evaluate the statistical performance of second order DOA estimation methods when the preceding convention al hypotheses are not verified. In this general framework, our study has dealt with the following points: the study of array geometries, regular, non-uniform, optimal according to certain criteria and allowing the improvement of the performance of conventional methods of DOA estimation, the study of the performance of second order methods of DOA estimation used in unconventional situations, in particular if the source signals and/or the noise signal are stochastic processes eventually temporally correlated and eventually non-gaussian, the study of the robustness of narrowband DOA algorithms with respect to the signal bandwidth and the symmetry of their spectra, finally, the study of a method called "covariance matching estimation" (COMET) applied to the DOA estimation problem as well as the sinusoidal frequencies estimation problem which provides, under certain conditions, asymptotically minimum variance estimations
Gabillon, Victor. "Algorithmes budgétisés d'itérations sur les politiques obtenues par classification". Thesis, Lille 1, 2014. http://www.theses.fr/2014LIL10032/document.
Pełny tekst źródłaThis dissertation is motivated by the study of a class of reinforcement learning (RL) algorithms, called classification-based policy iteration (CBPI). Contrary to the standard RL methods, CBPI do not use an explicit representation for value function. Instead, they use rollouts and estimate the action-value function of the current policy at a collection of states. Using a training set built from these rollout estimates, the greedy policy is learned as the output of a classifier. Thus, the policy generated at each iteration of the algorithm, is no longer defined by a (approximated) value function, but instead by a classifier. In this thesis, we propose new algorithms that improve the performance of the existing CBPI methods, especially when they have a fixed budget of interaction with the environment. Our improvements are based on the following two shortcomings of the existing CBPI algorithms: 1) The rollouts that are used to estimate the action-value functions should be truncated and their number is limited, and thus, we have to deal with bias-variance tradeoff in estimating the rollouts, and 2) The rollouts are allocated uniformly over the states in the rollout set and the available actions, while a smarter allocation strategy could guarantee a more accurate training set for the classifier. We propose CBPI algorithms that address these issues, respectively, by: 1) the use of a value function approximation to improve the accuracy (balancing the bias and variance) of the rollout estimates, and 2) adaptively sampling the rollouts over the state-action pairs
Guingne, Franck. "Contribution à l’algorithmique des automates pondérés à une, deux ou plusieurs bandes". Rouen, 2005. http://www.theses.fr/2005ROUES009.
Pełny tekst źródłaThis PhD thesis deals with the automata theory. A general study of similarity relations and the notion a cover automata are presented. We define merging relations and the notion of cover transducers. A reduction algorithm is exposed and tested. Weighted multi-tape automata are a generalisation of automata and transducers. We define the join and auto-intersection operations and we present the corresponding algorithms. As a consequence of the Post's Correspondence Problem there does not exist a general algorithm of auto-intersection. We define a class of weighted multi-tape automata in which it is possible to compute the auto-intersection. After a State of the Art of finite state machine compilers, we present XEROX's compilers, WFSC and XFST, through their specificities. We present the '?' symbol and extended-alphabet machines, then the notion of virtual networks in XFST. Several characteristics of the generic and modular design of WFSC are exposed. We study the worst-case complexity for the printing of all paths of an acyclic automaton. We describe the structure that maximizes the complexity, then we compute this complexity for a given number of states
Iacob, Alexandra. "Scalable Model-Free Algorithms for Influencer Marketing". Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG012.
Pełny tekst źródłaMotivated by scenarios of information diffusion and advertising in social media, we study an emph{influence maximization} (IM) problem in which little is assumed to be known about the diffusion network or about the model that determines how information may propagate. In such a highly uncertain environment, one can focus on emph{multi-round diffusion campaigns}, with the objective to maximize the number of distinct users that are influenced or activated, starting from a known base of few influential nodes.During a campaign, spread seeds are selected sequentially at consecutive rounds, and feedback is collected in the form of the activated nodes at each round.A round's impact (reward) is then quantified as the number of emph{newly activated nodes}.Overall, one must maximize the campaign's total spread, as the sum of rounds' rewards.We consider two sub-classes of IM, emph{cimp} (CIMP) and emph{ecimp} (ECIMP), where (i) the reward of a given round of an ongoing campaign consists of only the extit{new activations} (not observed at previous rounds within that campaign), (ii) the round's context and the historical data from previous rounds can be exploited to learn the best policy, and (iii) ECIMP is CIMP repeated multiple times, offering the possibility of learning from previous campaigns as well.This problem is directly motivated by the real-world scenarios of information diffusion in emph{influencer marketing}, where (i) only a target user's emph{first} / unique activation is of interest (and this activation will emph{persist} as an acquired, latent one throughout the campaign), and (ii) valuable side-information is available to the learning agent.In this setting, an explore-exploit approach could be used to learn the key underlying diffusion parameters, while running the campaigns.For CIMP, we describe and compare two methods of emph{contextual multi-armed bandits}, with emph{upper-confidence bounds} on the remaining potential of influencers, one using a generalized linear model and the Good-Turing estimator for remaining potential (glmucb), and another one that directly adapts the LinUCB algorithm to our setting (linucb).For ECIMP, we propose the algorithmlgtlsvi, which implements the extit{optimism in the face of uncertainty} principle for episodic reinforcement learning with linear approximation. The learning agent estimates for each seed node its remaining potential with a Good-Turing estimator, modified by an estimated Q-function.We show that they outperform baseline methods using state-of-the-art ideas, on synthetic and real-world data, while at the same time exhibiting different and complementary behavior, depending on the scenarios in which they are deployed
Bubeck, Sébastien. "JEUX DE BANDITS ET FONDATIONS DU CLUSTERING". Phd thesis, Université des Sciences et Technologie de Lille - Lille I, 2010. http://tel.archives-ouvertes.fr/tel-00845565.
Pełny tekst źródłaPerrault, Pierre. "Apprentissage efficient dans les problèmes de semi-bandits stochastiques combinatoires". Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM023.
Pełny tekst źródłaCombinatorial stochastic semi-bandits appear naturally in many contexts where the exploration/exploitation dilemma arises, such as web content optimization (recommendation/online advertising) or shortest path routing methods. This problem is formulated as follows: an agent sequentially optimizes an unknown and noisy objective function, defined on a power set mathcal{P}([n]). For each set A tried out, the agent suffers a loss equal to the expected deviation from the optimal solution while obtaining observations to reduce its uncertainty on the coordinates from A. Our objective is to study the efficiency of policies for this problem, focusing in particular on the following two aspects: statistical efficiency, where the criterion considered is the regret suffered by the policy (the cumulative loss) that measures learning performance; and computational efficiency. It is sometimes difficult to combine these two aspects in a single policy. In this thesis, we propose different directions for improving statistical efficiency, while trying to maintain the computational efficiency of policies.In particular, we have improved optimistic methods by developing approximation algorithms and refining the confidence regions used. We also explored an alternative to the optimistic methods, namely randomized methods, and found them to be a serious candidate for combining the two types of efficiency
Hadiji, Hédi. "On some adaptivity questions in stochastic multi-armed bandits". Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM021.
Pełny tekst źródłaThe main topics adressed in this thesis lie in the general domain of sequential learning, and in particular stochastic multi-armed bandits. The thesis is divided into four chapters and an introduction. In the first part of the main body of the thesis, we design a new algorithm achieving, simultaneously, distribution-dependent and distribution-free optimal guarantees. The next two chapters are devoted to adaptivity questions. First, in the context of continuum-armed bandits, we present a new algorithm which, for the first time, does not require the knowledge of the regularity of the bandit problem it is facing. Then, we study the issue of adapting to the unknown support of the payoffs in bounded K-armed bandits. We provide a procedure that (almost) obtains the same guarantees as if it was given the support in advance. In the final chapter, we study a slightly different bandit setting, designed to enforce diversity-preserving conditions on the strategies. We show that the optimal regert in this setting at a speed that is quite different from the traditional bandit setting. In particular, we observe that bounded regret is possible under some specific hypotheses
Silvestre, Fialho Álvaro Roberto. "Adaptive operator selection for optimization". Paris 11, 2010. http://www.theses.fr/2010PA112292.
Pełny tekst źródłaEvolutionary Algorithms have demonstrated their ability to address a wide range of optimization problems; but their performance relies on tuning a few parameters, depending on the problem at hand. During this thesis work, we have focused on the development of tools to automatically set some of these parameters, using machine learning techniques. More specifically, we have worked on the following sub-problem: given a number of available variation operators, it consists in selecting which is the best operator to be applied at each moment of the search, based on how the operators have performed up to the given time instant of the current search/optimization process. This approach is applied online, i. E. , while solving the problem, using only the history of the current run to evaluate and decide between the operators; this paradigm is commonly referred to as Adaptive Operator Selection (AOS). To do AOS, we need two components: a Credit Assignment scheme, that defines how to reward the operators based on their impact in the search process, and an Operator Selection mechanism that, based on the rewards received, decides which is the best operator to be applied next. The contributions of this thesis, in summary, lie in the proposaI and analysis of schemes to solve the AOS problem based on the Multi-Armed Bandit (MAB) paradigm; we have proposed different techniques, in order to enable a MAB algorithm to efficiently cope with the dynamics of evolution and with the very different characteristics of the problems to be tackled. The latest one, referred to as AUC-MAB, is able to efficiently control the application of operators, while being robust with respect to its own parameters
Benhamiche, Amal. "Designing optical multi-band networks : polyhedral analysis and algorithms". Thesis, Paris 9, 2013. http://www.theses.fr/2013PA090075/document.
Pełny tekst źródłaIn this thesis we consider two capacitated network design (CND) problems, using OFDM multi-band technology. The first problem is related to single-layer network design with specific requirements. We give an ILP formulation for this problem and study the polyhedra associated with its arc-set restriction. We describe two families of facet defining inequalities. We devise a Branch-and-Cut algorithm for the problem. Next, we investigate the multilayer version of CND using OFDM technology. We propose several ILP formulations and study the polyhedron associated with the first (cut) formulation. We identify several classes of facets and discuss the related separation problem. We devise a Branch-and-Cut algorithm embedding valid inequalities of both single-layer and multilayer problems. The second formulation is compact, and holds a polynomial number of constraints and variables. Two further path formulations are given which yield two efficient Branch-and-Price algorithms for the problem
Abeille, Marc. "Exploration-exploitation with Thompson sampling in linear systems". Thesis, Lille 1, 2017. http://www.theses.fr/2017LIL10182/document.
Pełny tekst źródłaThis dissertation is dedicated to the study of the Thompson Sampling (TS) algorithms designed to address the exploration-exploitation dilemma that is inherent in sequential decision-making under uncertainty. As opposed to algorithms derived from the optimism-in-the-face-of-uncertainty (OFU) principle, where the exploration is performed by selecting the most favorable model within the set of plausible one, TS algorithms rely on randomization to enhance the exploration, and thus are much more computationally efficient. We focus on linearly parametrized problems that allow for continuous state-action spaces, namely the Linear Bandit (LB) problems and the Linear Quadratic (LQ) control problems. We derive two novel analyses for the regret of TS algorithms in those settings. While the obtained regret bound for LB is similar to previous results, the proof sheds new light on the functioning of TS, and allows us to extend the analysis to LQ problems. As a result, we prove the first regret bound for TS in LQ, and show that the frequentist regret is of order O(sqrt{T}) which matches the existing guarantee for the regret of OFU algorithms in LQ. Finally, we propose an application of exploration-exploitation techniques to the practical problem of portfolio construction, and discuss the need for active exploration in this setting
Habermann, Mateus. "Band selection in hyperspectral images using artificial neural networks". Thesis, Compiègne, 2018. http://www.theses.fr/2018COMP2434/document.
Pełny tekst źródłaHyperspectral images (HSIs) are capable of providing a detailed spectral information about scenes or objects under analysis. It is possible thanks to both numerous and contiguous bands contained in such images. Given that di_erent materials have distinct spectral signatures, objects that have similar colors and shape can be distinguished in the spectral domain that goes beyond the visual range. However, in a pattern recognition system, the huge amount of data contained in HSIs may pose problems in terms of data storage and transmission. Also, the high dimensionality of hyperspectral images can cause the overfitting of the classifer in case of insufficient training data. One way to solve such problems is to perform band selection(BS) in HSIs, because it decreases the size of the dataset while keeping both useful and original information. In this thesis, we propose three different band selection frameworks. The first one is a supervised one, and it is designed to use only 20% of the available training data. For each class in the dataset, a binary one-versus-all classification using a single-layer neural network is performed, and the bands linked to the largest and smallest coefficients of the resulting hyperplane are selected. During this process, the most correlated bands with the bands already selected are automatically discarded, following a procedure also proposed in this thesis. Consequently, the proposed method may be seen as a classoriented band selection approach, allowing a BS criterion that meets the needs of each class. The second method we propose is an unsupervised version of the first framework. Instead of using the class information, the K-Means algorithm is used to perform successive binary clustering of the dataset. For each pair of clusters, a single-layer neural network is used to find the separating hyperplane, then the selection of bands is done as previously described. For the third proposed BS framework, we take advantage of the unsupervised nature of autoencoders. During the training phase, the input vector is subjected to masking Noise - some positions of this vector are randomly flipped to zero and the reconstruction error is calculated based on the uncorrupted input vector. The bigger the error, the more important the masked features are. Thus, at the end, it is possible to have a ranking of the spectral bands of the dataset
Cayuela, Rafols Marc. "Algorithmic Study on Prediction with Expert Advice : Study of 3 novel paradigms with Grouped Experts". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254344.
Pełny tekst źródłaHuvudarbetet för den här avhandlingen har varit en grundlig studie av den nya Prediction with Partially Monitored Grouped Expert Advice and Side Information paradigmet. Detta är nyligen föreslagit i denna avhandling, och det utökar det brett studerade Prediction with Expert Advice paradigmet. Förlängningen baseras på två antaganden och en begränsning som ändrar det ursprungliga problemet. Det första antagandet, Grouped, förutsätter att experterna är inbyggda i grupper. Det andra antagandet, Side Information, introducerar ytterligare information som kan användas för att i tid relatera förutsägelser med grupper. Slutligen innebär begränsningen, Partially Monitored, att gruppens förutsägelser endast är kända för en grupp i taget. Studien av detta paradigm innefattar utformningen av en komplett förutsägelsesalgoritm, beviset på en teoretisk bindning till det sämre fallet kumulativa ånger för en sådan algoritm och en experimentell utvärdering av algoritmen (bevisar förekomsten av fall där detta paradigm överträffar Prediction with Expert Advice). Eftersom algoritmens utveckling är konstruktiv tillåter den dessutom att enkelt bygga två ytterligare prediksionsalgoritmer för Prediction with Grouped Expert Advice och Prediction with Grouped Expert Advice and Side Information paradigmer. Därför presenterar denna avhandling tre nya prediktionsalgoritmer med motsvarande ångergränser och en jämförande experimentell utvärdering inklusive det ursprungliga Prediction with Expert Advice paradigmet.
Pham, Trung-Kien. "Étude et conception de réseaux transmetteurs reconfigurables en bande Ka". Thesis, Rennes 1, 2017. http://www.theses.fr/2017REN1S065.
Pełny tekst źródłaTransmitarray is an attractive solution for front-end devices in the next generation of communications (5G). The spatial-fed architecture of transmitarray antennas can compete with phase-arrays due to the absence of feeding network and with reflectarrays since they do not suffer from feed blockage. Thanks to their operation in transmission mode, transmitarrays can be easily mounted on platforms for outdoor environment applications. With mature printed-circuit board technology, there are unstoppable experiments in various frequency bands from cm-wave to mm-wave and up to terahertz in upcoming years for potential applications. Many advanced properties are exploited in transmitarrays in recent years to meet high demands of communications facilities, for example, circular-polarization, dual-/multi-polarization or frequencies through many techniques. Some experiments are consid-ered to validate eligibility of this antenna type in commercial services or military missions, namely electronically steering beam, broad bandwidth, etc. In terms of cost reduction and rigidity, non-dielectric prototypes are also proposed. The Ka-band Satcom applications are the main objective of this thesis through trans-mitarray solution. This band provides high data rate for both down-link and up-link in replacement of the current Ku-band systems with miniaturized module in next dec-ades. Hence, it is worth to pay attention to communications for moving platforms, for example, high-speed trains, planes, etc
Delestre, Cyrile. "Géolocalisation d'émetteurs en une étape : Algorithmes et performances". Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLN006/document.
Pełny tekst źródłaThe context of the thesis is the transmitters geolocalization (position estimation in the space) of radiocommunication from several widely spaced multi-sensor stations. The conventional geolocalization methods as the tirnagulation are in 2 staps (the first step estimates intermediate parameters and the second step "merges" these mesures from the stations in order to obtain the transmitters positions). The 1 stap methods use the observertions from all the stations to directly and optimally estimate the sources positions. Directly handling the signals on the global array (composed of all the local stations) leads to a broadband effect on the signals between the stations.The thesis proposes to study the residual broadband effect on the global array of the 1 step methods. Then we propose improvements on some 1 step geolocalization methods, especially based on the random matrix theory in large dimension and on the introduction of a new method named LOST-FIND. Finally, a new approach differently tackling the braodband problem has been introduced and leads to TARGET algorithm
Steinberg, David. "Otimização da largura de banda de ganho de um amplificador Raman na banda "O" baseada em algoritmo genético". Universidade Presbiteriana Mackenzie, 2011. http://tede.mackenzie.br/jspui/handle/tede/1401.
Pełny tekst źródłaIn this work, the O-band discrete Raman amplifier gain bandwidth optimization using genetic algorithm of a commercial simulator is carried out. The main objective of the study was to optimize the wide Raman amplifier gain bandwidth obtaining values of gain variation less than 0.5 dB. Using a fiber DCF (Dispersion Compensating Fiber) as Raman amplifier medium, the pump number was varied and the gain variation for each pump configuration has been verified. For a fixed 70 nm (1275-1345 nm) bandwidth consisting of 62 signal frequencies points, the results were generated for one, two, three, four, five, six, seven and eight backward pumps, which with the last one it was obtained the best result of 0.35 dB gain variation. In addition to this analysis, it was also found the Raman amplifier noise figure for this band and made a brief comparison with the conventional profile bands.
Neste trabalho a otimização da largura de banda de ganho de um amplificador Raman discreto na banda "O" utilizando algoritmo genético de um simulador comercial foi realizada. O objetivo principal do trabalho foi otimizar a maior largura de banda de ganho do amplificador Raman obtendo valores de variação de ganho menores que 0.5 dB. Utilizando uma fibra DCF (Dispersion Compensating Fiber) como meio de ganho do amplificador Raman, o número de bombeio foi variado e a variação de ganho para cada configuração de bombeio foi verificada. Para uma largura de banda fixa em 70 nm (1275-1345 nm) compreendendo 62 pontos de freqüências de sinal, foram gerados resultados para um, dois, três, quatro, cinco, seis, sete e oito bombeios contrapropagantes sendo que com esta última configuração foi obtido o melhor resultado de variação de ganho de 0.35 dB. Além desta análise, também foi verificado o perfil da figura de ruído do amplificador Raman nesta banda e feita uma breve comparação com o perfil em bandas convencionais.
Gómez-Villouta, Giglia. "Méthodes heuristiques pour le problème de placement sur bande en deux dimensions". Angers, 2010. http://www.theses.fr/2010ANGE0022.
Pełny tekst źródłaPacking problems are usually NP-hard, or NP-complete according to the objective. One has to locate a set of objects into one or more “container(s)”, with fix dimensions or of infinite height, while respecting constraints related to some characteristics (weight, quantity, rotation, stability, guillotine cuts. . . ). Themain interest of these problems are the numerous practical applications from various domains. The most effective solution strategies for these problems are usually approximate methods, local search in particular. In this thesis, we are interested in a particular two-dimensional packing problem (without rotation nor guillotine cuts) known as “strip packing” (SPP). The objective of this problem, after locating rectangular objects without overlap, is to minimize the height of the resulting packing. We developed two “meta-heuristic” approaches for the SPP, both including innovative components based on problem knowledge. The first one is a genetic algorithm with a new (highly “visual”) crossover and a hierarchical fitness function. The second one is a tabu search with “direct” representation (i. E. Not using the classical permutations) whose main characteristics are a consistent neighborhood, a “well-informed” diversification (based on the search history), and a fitness function able to evaluate possibly partial solutions. The two proposed approaches, assessed on a well-known and very difficult benchmark, show good performances compared with other strategies
Fialho, Álvaro. "Selection Adaptative d'Operateurs pour l'Optimisation". Phd thesis, Université Paris Sud - Paris XI, 2010. http://tel.archives-ouvertes.fr/tel-00578431.
Pełny tekst źródłaBouton, Eric Albert. "Algorithmes d'allocation de ressources pour des systèmes à interférence". Phd thesis, Télécom ParisTech, 2010. http://pastel.archives-ouvertes.fr/pastel-00005795.
Pełny tekst źródłaBouton, Eric. "Algorithmes d'allocation de ressources pour des systèmes à interférence". Paris, Télécom ParisTech, 2010. http://www.theses.fr/2010ENST0002.
Pełny tekst źródłaInterference is a phenomenon that is present in many communication systems and is often a serious hindrance to their development. An intelligent management of this phenomenon is thus necessary to limit its negative impact. In this thesis, we addressed three issues related to its presence. In the first part, in order to boost transmission rates in impulse radio ultra-wide band systems, we propose to assign multiple time-hopping codes to the same user. This generates a new type of interference associated with the additional codes allocated to the user of interest. Since the benefit of this type of strategy is not straightforward, we study the impact of our proposition on the system's performance. In the second part, we tackle the problem of optimizing the outage probability of multiple-antenna systems in a slow fading Rician channel. We propose to optimize this probability in the context of N transmit antennas and one single receive antenna, when the signal-to-noise ratio is high, and improve it when there are multiple antennas both at the transmitter and at the receiver. In the last part, we study the problem of power allocation in a Gaussian interference channel. Based on a new approximate expression of this channel's capacity and using an OFDM modulation, we propose to develop a new power allocation algorithm that improves the achievable rates region, compared with a uniform allocation and classic dynamic spectrum management techniques
Djouob, Charles. "Contribution à la synthèse des filtres microondes par une méthode de prédiction fondée sur des données expérimentales : application à la technologie microruban suspendu". Limoges, 1990. http://www.theses.fr/1990LIMO4001.
Pełny tekst źródłaZaarour, Farah. "Channel estimation algorithms for OFDM in interference scenarios". Thesis, Lille 1, 2015. http://www.theses.fr/2015LIL10105/document.
Pełny tekst źródłaThe scarcity of the radio spectrum and the increasing demand on bandwidth makes it vital to optimize the spectrum use. While a maximum efficiency should be attained, a minimal interference level should be maintained. OFDM has been selected as the modulation scheme in several wireless standards. Channel estimation is a fundamental task in OFDM and it becomes even more challenging in the presence of interference. In this thesis, our aim is to propose channel estimation algorithms for OFDM systems in the presence of interference, where conventional channel estimators designed for OFDM fail. First, we consider the cognitive radio environment and propose a novel channel estimation framework for fast time-varying channels in OFDM with NBI. This is accomplished through an expectation maximization (EM) based algorithm. This formulation allows us to obtain a closed-form expression for the estimation of the noise power. In this thesis, we are particularly interested in a very recent scheme of superimposed pilots for OFDM (DNSP). DNSP assures interference-free pilots at the expense of data interference. Seen the modernity of DNSP, a suitable receiver has to be designed to cope with its design. We first propose a low-complexity interference canceler (IC) for slow time-varying channels with DNSP. The performance of the proposed IC is guaranteed when the channel estimation error is small. As another contribution, we extend the design of the approximated IC for DNSP so as to take the channel estimation errors into account. Finally, we consider robust channel estimation which can be viewed as one of the perspectives of this thesis
Bouzegzi, Abdelaziz. "Algorithmes de discrimination de signaux pour la radio cognitive". Paris, Télécom ParisTech, 2009. http://www.theses.fr/2009ENST0048.
Pełny tekst źródłaIn the context of cognitive radio it is a crucial task to distinguish blindly various wireless systems (e. G. , Wifi, Wimax, 3GPP/LTE, DVB-T) from each others. We focus on the OFDM based systems which differ from their subcarrier spacing used in OFDM modulation. One can thus carry out recognition algorithms based on the value of the subcarrier spacing. Standard approaches developed in the literature rely on the detection of the cyclic prefix which enables to exhibit the value of the used subcarrier spacing. Nevertheless, these approaches fail when either the cyclic prefix duration is small or the channel impulse response is almost as large as the cyclic prefix. Therefore, this thesis proposes new algorithms to estimate the parameters of OFDM modulated signal (especially the subcarrier spacing) relying on i) the normalized kurtosis, ii) the maximum-likelihood principle, iii) the matched filter, and iv) the second-order cyclostationary property. We have shown the strong robustness of proposed algorithms to short cyclic prefix, multipath channel, time offset, and frequency offset
Allmendinger, Richard. "Tuning evolutionary search for closed-loop optimization". Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/tuning-evolutionary-search-for-closedloop-optimization(d54e63e2-7927-42aa-b974-c41e717298cb).html.
Pełny tekst źródłaParaschakis, Dimitris. "Algorithmic and Ethical Aspects of Recommender Systems in e-Commerce". Licentiate thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-7792.
Pełny tekst źródłaRippl, Michael [Verfasser], Thomas [Akademischer Betreuer] Huckle, Bruno [Gutachter] Lang i Thomas [Gutachter] Huckle. "Parallel Algorithms for the Solution of Banded Symmetric Generalized Eigenvalue Problems / Michael Rippl ; Gutachter: Bruno Lang, Thomas Huckle ; Betreuer: Thomas Huckle". München : Universitätsbibliothek der TU München, 2020. http://d-nb.info/1230985379/34.
Pełny tekst źródłaChaux, Caroline. "Analyse en ondelettes M-bandes en arbre dual : application à la restauration d'images". Phd thesis, Université de Marne la Vallée, 2006. http://tel.archives-ouvertes.fr/tel-00714292.
Pełny tekst źródłaAttalah, Samir. "Amélioration des performances des algorithmes LMS 1-D et 2-D : utilisation des techniques de décomposition en ondelettes, en sous-bandes et filtrage rapide". Bordeaux 1, 1996. http://www.theses.fr/1996BOR10666.
Pełny tekst źródłaElias, Jocelyne. "Allocation dynamique de la bande passante dans les réseaux à qualité de service". Paris 6, 2006. http://www.theses.fr/2006PA066169.
Pełny tekst źródłaJin, Yan. "Hybrid metaheuristic algorithms for sum coloring and bandwidth coloring". Thesis, Angers, 2015. http://www.theses.fr/2015ANGE0062/document.
Pełny tekst źródłaThe minimum sum coloring problem (MSCP) and the bandwidth coloring problem (BCP) are two important generalizations of the classical vertex coloring problem with numerous applications in diverse domains, including VLSI design, scheduling, resource allocation and frequency assignment in mobile networks, etc. Since the MSCP and BCP are NP-hard problems, heuristics and metaheuristics are practical solution methods to obtain high quality solutions in an acceptable computing time. This thesis is dedicated to developing effective hybrid metaheuristic algorithms for the MSCP and BCP. For the MSCP, we present two memetic algorithms which combine population-based evolutionary search and local search. An effective algorithm for maximum independent set is devised for generating initial solutions. For the BCP, we propose a learning-based hybrid search algorithm which follows a cooperative framework between an informed construction procedure and a local search heuristic. The proposed algorithms are evaluated on well-known benchmark instances and show highly competitive performances compared to the current state-of-the-art algorithms from the literature. Furthermore, the key issues of these algorithms are investigated and analyzed
Bouneffouf, Djallel. "DRARS, A Dynamic Risk-Aware Recommender System". Phd thesis, Institut National des Télécommunications, 2013. http://tel.archives-ouvertes.fr/tel-01026136.
Pełny tekst źródłaGomez-Villouta, Giglia. "Méthodes heuristiques pour le problème de placement sur bande en deux dimensions". Phd thesis, Université d'Angers, 2010. http://tel.archives-ouvertes.fr/tel-00575859.
Pełny tekst źródła