Letteratura scientifica selezionata sul tema "Optimisation Sans Contrainte"
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Articoli di riviste sul tema "Optimisation Sans Contrainte":
Djeddi, Fadila, Mouloud Mohellebi e Ahmed Ouibrahim. "Optimisation des propriétés mécaniques de matériaux polymériques par les mélanges et effet bénéfique du vieillissement thermique". Matériaux & Techniques 107, n. 6 (2019): 604. http://dx.doi.org/10.1051/mattech/2020001.
BONNEAU, M., J. Y. DOURMAD, B. LEBRET, M. C. MEUNIER-SALAÜN, S. ESPAGNOL, Y. SALAÜN, P. LETERME e H. VAN DER WERF. "Evaluation globale des systèmes de production porcine et leur optimisation au niveau de l’exploitation". INRAE Productions Animales 21, n. 4 (27 settembre 2008): 367–86. http://dx.doi.org/10.20870/productions-animales.2008.21.4.3413.
Babeau, André, e Françoise Charpin. "Détermination du financement optimal d'un logement par la théorie du cycle de vie". Revue de l'OFCE 47, n. 5 (1 novembre 1993): 67–90. http://dx.doi.org/10.3917/reof.p1993.47n1.0067.
SAILLEY, Manon, Cécile CORDIER, Jean-Yves COURTONNE, Boris DUFLOT, François CADUDAL, Christophe PERROT, Aude BRION e René BAUMONT. "Quantifier et segmenter les flux de matières premières utilisées en France par l’alimentation animale". INRAE Productions Animales 34, n. 4 (3 marzo 2022): 273–92. http://dx.doi.org/10.20870/productions-animales.2021.34.4.5396.
MALGOYRE, A., H. SANCHEZ e N. KOULMANN. "Ethique du sport et éthique de la guerre : optimisation des performances physiques du combattant". Revue Médecine et Armées, Volume 43, Numéro 3 (1 giugno 2015): 257–64. http://dx.doi.org/10.17184/eac.6885.
Adda, Asma, Wahib Mohamed Naceur e Mohamed Abbas. "Modélisation et optimisation de la consommation d’énergie d’une station de dessalement par procédé d’osmose inverse en Algérie". Journal of Renewable Energies 19, n. 2 (9 gennaio 2024). http://dx.doi.org/10.54966/jreen.v19i2.557.
Tesi sul tema "Optimisation Sans Contrainte":
Kchouk, Bilel. "Méthodes de Chebyshev d'ordres supérieurs pour l'optimisation non linéaire, sans contrainte et différentiable". Thèse, Université de Sherbrooke, 2012. http://hdl.handle.net/11143/6682.
Kortas, Manel. "Optimisation de la liaison montante pour un réseau de capteurs sans fil avec la contrainte d'énergie". Thesis, Limoges, 2020. http://aurore.unilim.fr/theses/nxfile/default/45adbe14-0f06-4b36-9fb7-80441c295210/blobholder:0/2020LIMO0025.pdf.
In this dissertation, we are interested in the data gathering with energy constraint for Wireless Sensor Networks (WSNs). Yet, there exist several challenges that may disturb a convenient functioning of this kind of networks. Indeed, WSNs' applications have to deal with limited energy, memory and processing capabilities of sensor nodes. Furthermore, as the size of these networks is growing continually, the amount of data for processing and transmitting becomes enormous. In many practical cases, the wireless sensors are distributed across a physical field to monitor physical phenomena with high space-time correlation. Hence, the main focus of this thesis is to reduce the amount of processed and transmitted data in the data gathering scenario. In the first part of this thesis, we consider the Compressive Sensing (CS), which is a promising technique to exploit this correlation in order to limit the number of transmission and therefore increase the lifetime of the network. Typically, we are interested in the mesh network topology, where the sink node is not in the range of sensors and routing schemes must be applied. We propose a joint Space-Time Compressive Sensing (STCS) by exploiting jointly the inter-sensors and intra-sensor data dependency. Moreover, since the routing and the number of retransmission affect significantly the total energy consumption, we introduce the routing in our cost function in order to optimize the selection of the transmitting sensors. Simulation results show that this method outperforms the existing ones and confirm the validity of our approach. In the second part of this thesis, we attempt to address nearly the same twofold energy saving scheme that is investigated in the first part with the use of the Matrix Completion (MC) methodology. Precisely, we assume that a restricted number of sensor nodes are selected to be active and represent the whole network, while the rest of nodes remain idle and do not participate at all in the data sensing and transmission. Furthermore, the set of active nodes' readings is efficiently reduced, in each time slot, according to a cluster scheduling with the Optimized Cluster-based MC data gathering approach (OCBMC). Relying on the existing MC techniques, the sink node is unable to recover the entire data matrix due to the existence of the completely empty rows that correspond to the inactive nodes. Although applying a high data compression ratio extremely reduces the overall network energy consumption, the network lifetime is not necessarily extended due to the uneven energy depletion of the sensor nodes' batteries. To this end, in the third part of this thesis, we have developed the Energy-Aware Matrix Completion based data gathering approach (EAMC), which designates the active nodes according to their residual energy levels. Furthermore, since we are mainly interested in the high data loss scenarios, the limited amount of delivered data must be sufficient in terms of informative quality it holds in order to reach good and satisfactory recovery accuracy for the entire network data. For that reason, the EAMC selects the nodes that can best represent the network depending on their inter-correlation as well as the network energy efficiency, with the use of a combined energy-aware and correlation-based metric. This introduced active node cost function changes with the type of application one wants to perform, with the intention to reach a longer lifespan for the network
Raynaud, Paul. "L'exploitation de la structure partiellement-séparable dans les méthodes quasi-Newton pour l'optimisation sans contrainte et l'apprentissage profond". Electronic Thesis or Diss., Université Grenoble Alpes, 2024. http://www.theses.fr/2024GRALI021.
This thesis studies and improves the use of the partially-separable structure for unconstrained optimization, particularly for quasi-Newton methods and training neural networks.A partially-separable function is the sum of element functions, each of lower dimension than the total problem.Thus, the Hessian can be aggregated by separately approximating the Hessian of each element function with a dense matrix.These partitioned quasi-Newton methods are applicable to high-dimensional problems and maintain the sparse structure of the Hessian, unlike a limited-memory quasi-Newton method.In practice, these methods require fewer iterations than a limited-memory quasi-Newton method and are parallelizable by distributing computations related to the element functions.However, a comprehensive literature review on the subject has revealed some limitations, particularly when the dimension of the element functions is large.Additionally, the only open-source optimization software exploiting the partially-separable structure is unusable for inexperienced users, leaving only commercial software as an option.In this thesis, two solutions are proposed to address these shortcomings, along with an application of partially-separable optimization concepts to supervised learning of a neural network.The first contribution is a software suite based on an automatic detection of the partially-separable structure of a problem, i.e., retrieves each reduced-dimensional element function.Following this, partitioned data structures necessary for storing derivatives, or their approximations, are allocated and used to define partitioned quasi-Newton optimization methods.The entire suite is integrated into the "JuliaSmoothOptimizers" ecosystem, which gathers numerous tools for smooth optimization, including optimization algorithms that can therefore exploit the detected partial separability.The second contribution replaces the approximation of an element Hessian by a dense matrix with a limited-memory quasi-Newton linear operator.As a result, the memory cost of the total Hessian approximation is no longer quadratically related to the dimension of the element functions.A limited-memory partitioned quasi-Newton method is then applicable when the element functions are large.Each limited-memory partitioned quasi-Newton method has a proof of global convergence.Additionally, numerical results show that these methods outperform partitioned or limited-memory quasi-Newton methods when the elements are large.The final contribution examines the exploitation of the partially-separable structure during supervised training of a neural network.The optimization problem associated with training is generally not partially-separable.Therefore, a partially-separable loss function and a partitioned network architecture are introduced to make the training partially-separable.Numerical results combining these two contributions are competitive with standard architectures and loss functions according to state-of-the-art training methods.Moreover, this combination produces an additional parallelization scheme to existing methods for supervised learning.Indeed, the calculations of each element loss function can be distributed to a worker requiring only a fraction of the neural network to operate.Finally, a limited-memory partitioned quasi-Newton training is proposed.This training is empirically shown to be competitive with state-of-the-art training methods
Zayyoun, Najoua. "Optimisation et modélisation du détachement de couches minces de silicium par contrainte thermique avec ou sans guidage de la fracture : application au photovoltaïque". Thesis, Orléans, 2019. http://www.theses.fr/2019ORLE3036.
The reduction of photovoltaic cells cost and the increase of their efficiency is probably one of the best solution to tacle the climate change issues. The mean of this thesis is to study the innovative processes to produce ultra-thin monocrystalline silicon layers without loss of raw material (with thicknesses ranging from several hundred nanometers to several micrometers), by using thermal stress and low energy hydrogen implantation. The use of such kerf-free processes leads to a significant reduction of the silicon consumption, in order to produce of low-cost photovoltaic solar cells.In this work, by using analytical and numerical modeling, we first determined the thermal stresses needed for the detachment of silicon by stress-induced spalling process and predicts the detached thickness of silicon foils. These models depend on thermals and elastics parameters of metal used as well as the applied thermal loading. A good agreement between the theoretical and experimental results was obtained. Furthermore, different optimal parameters leading to the detachment of silicon foils with desired thicknesses using SIS process were investigated such as the thickness of the stressor layer, the nature of stressor layer and the thickness of glue. In a second part, thin silicon layers were transferred as a function of thermal annealing using the stress-induced spalling process guided by hydrogen implantation-induced defects. Then, the use of experimental characterizations and FEM simulations of the thermal stresses induced in implanted silicon we explain the mechanisms involved when combining the two processes. Characterization of silicon foils was performed by various technique in order to validate and optimized the process
Maurandi, Victor. "Algorithmes pour la diagonalisation conjointe de tenseurs sans contrainte unitaire. Application à la séparation MIMO de sources de télécommunications numériques". Thesis, Toulon, 2015. http://www.theses.fr/2015TOUL0009/document.
This thesis develops joint diagonalization of matrices and third-order tensors methods for MIMO source separation in the field of digital telecommunications. After a state of the art, the motivations and the objectives are presented. Then the joint diagonalisation and the blind source separation issues are defined and a link between both fields is established. Thereafter, five Jacobi-like iterative algorithms based on an LU parameterization are developed. For each of them, we propose to derive the diagonalization matrix by optimizing an inverse criterion. Two ways are investigated : minimizing the criterion in a direct way or assuming that the elements from the considered set are almost diagonal. Regarding the parameters derivation, two strategies are implemented : one consists in estimating each parameter independently, the other consists in the independent derivation of couple of well-chosen parameters. Hence, we propose three algorithms for the joint diagonalization of symmetric complex matrices or hermitian ones. The first one relies on searching for the roots of the criterion derivative, the second one relies on a minor eigenvector research and the last one relies on a gradient descent method enhanced by computation of the optimal adaptation step. In the framework of joint diagonalization of symmetric, INDSCAL or non symmetric third-order tensors, we have developed two algorithms. For each of them, the parameters derivation is done by computing the roots of the considered criterion derivative. We also show the link between the joint diagonalization of a third-order tensor set and the canonical polyadic decomposition of a fourth-order tensor. We confront both methods through numerical simulations. The good behavior of the proposed algorithms is illustrated by means of computing simulations. Finally, they are applied to the source separation of digital telecommunication signals
Montalbano, Pierre. "Contraintes linéaires et apprentissage sans conflit pour les modèles graphiques". Electronic Thesis or Diss., Toulouse 3, 2023. http://www.theses.fr/2023TOU30340.
Graphical models define a family of formalisms and algorithms used in particular for logical and probabilistic reasoning, in fields as varied as image analysis or natural language processing. They are capable of being learned from data, giving probabilistic information that can then be combined with logical information. The goal of the thesis is to improve the efficiency of reasoning algorithms on these models crossing probabilities and logic by generalizing a fundamental mechanism of the most efficient purely logical reasoning tools (SAT solvers) to this hybrid case mixing probabilities and logic: conflict-based learning. The work is based on the concept of duality in linear programming and our learning mechanism is conflict-free, producing linear constraints efficiently solved using a knapsack formulation
Medjiah, Samir. "Optimisation des protocoles de routage dans les réseaux multi-sauts sans fil à contraintes". Thesis, Bordeaux 1, 2012. http://www.theses.fr/2012BOR14663/document.
Great research efforts have been carried out in the field of challenged multihop wireless networks (MWNs). Thanks to the evolution of the Micro-Electro-Mechanical Systems (MEMS) technology and nanotechnologies, multihop wireless networks have been the solution of choice for a plethora of problems. The main advantage of these networks is their low manufacturing cost that permits one-time application lifecycle. However, if nodes are low-costly to produce, they are also less capable in terms of radio range, bandwidth, processing power, memory, energy, etc. Thus, applications need to be carefully designed and especially the routing task because radio communication is the most energy-consuming functionality and energy is the main issue for challenged multihop wireless networks.The aim of this thesis is to analyse the different challenges that govern the design of challenged multihop wireless networks such as applications challenges in terms of quality of service (QoS), fault-tolerance, data delivery model, etc., but also networking challenges in terms of dynamic network topology, topology voids, etc. Our contributions in this thesis focus on the optimization of routing under different application requirements and network constraints. First, we propose an online multipath routing protocol for QoS-based applications using wireless multimedia sensor networks. The proposed protocol relies on the construction of multiple paths while transmitting data packets to their destination, i.e. without prior topology discovery and path establishment. This protocol achieves parallel transmissions and enhances the end-to-end transmission by maximizing path bandwidth and minimizing the delays, and thus meets the requirements of QoS-based applications. Second, we tackle the problem of routing in mobile delay-tolerant networks by studying the intermittent connectivity of nodes and deriving a contact model in order to forecast future nodes' contacts. Based upon this contact model, we propose a routing protocol that makes use of nodes' locations, nodes' trajectories, and inter-node contact prediction in order to perform forwarding decisions. The proposed routing protocol achieves low end-to-end delays while using efficiently constrained nodes' resources in terms of memory (packet queue occupancy) and processing power (forecasting algorithm). Finally, we present a topology control mechanism along a packet forwarding algorithm for event-driven applications using stationary wireless sensor networks. Topology control is achieved by using a distributed duty-cycle scheduling algorithm. Algorithm parameters can be tuned according to the desired node's awake neighbourhood size. The proposed topology control mechanism ensures trade-off between event-reporting delay and energy consumption
Langouët, Hoël. "Optimisation sans dérivées sous contraintes : deux applications industrielles en ingénierie de réservoir et en calibration des moteurs". Phd thesis, Université de Nice Sophia-Antipolis, 2011. http://tel.archives-ouvertes.fr/tel-00671987.
Ngom, Diery. "Optimisation de la durée de vie dans les réseaux de capteurs sans fil sous contraintes de couvertureet de connectivité réseau". Thesis, Mulhouse, 2016. http://www.theses.fr/2016MULH9134/document.
Since the past two decades, a new technology called Wireless Sensor Network (WSN) which result in a fusion of embedded systems and wireless communications has emerged. A WSN is Ad hoc network composed of many sensors nodes communicating via wireless links and which can be deployed randomly or deterministically over a given interest region. Theses sensors can also collect data from the environment, do local processing and transmit the data to a sink node or Base Station (BS) via multipath routing. Thereby, a wide range of potential applications have been envisioned using WSN such as environmental conditions monitoring, wildlife habitat monitoring, industrial diagnostic, agricultural, improve health care, etc. Nevertheless,WSN are not perfect. Indeed, given their small size, their low cost and their deployment generally in hostile or difficult access areas, sensor nodes have some weaknesses such as: a limited energy, so a network lifetime limited, limited bandwidth, limited computations and communications capabilities, etc. To overcome these limitations, several research issues from were created in recent years, and the main issues focus on the optimization of energy consumption in order to improve the network lifetime. Other important researches focus on issues of coverage areas, placement strategies of sensor nodes and network connectivity. However, most solutions proposed in recent years to resolve these issues do not take into account all these issues that we cited above in resolutions models; while in many WSN applications such as monitoring critical region, wildlife habitat monitoring, agricultural application, a full coverage of the monitoring region and network connectivity are mandatory as well an energy-awareness network lifetime. The objective of this thesis is thus to propose new scheduling mechanisms for optimizing the network lifetime in WSN, while ensuring at any time of the network lifetime a full coverage of the monitored region and network connectivity. To achieve our goals, we have study and done proposal in two axes which are placement strategy of sensor nodes and scheduling mechanism in the MAC layer. For these, we have implemented a Distributed Scheduling Medium Access Control algorithm (DSMAC) based on our placement method. Furthermore, DSMAC enables to cover 100% of the monitored region, to ensure optimal network connectivity and also allows sensors node to save up to 30% of their energy compared to other MAC protocols such as TunableMAC
Hernandez, Sébastien. "Evaluation et optimisation du mécanisme de Handhover dans un Réseau Local Sans Fil dédié aux applications à trafic contraint par le temps". Phd thesis, Clermont-Ferrand 2, 2006. https://theses.hal.science/docs/00/70/32/74/PDF/2006CLF21682.pdf.
Libri sul tema "Optimisation Sans Contrainte":
Scales, L. E. Introduction to non-linear optimization. New York: Springer-Verlag, 1985.
Scales, L. E. Introduction to non-linear optimization. London: Macmillan, 1985.
Capitoli di libri sul tema "Optimisation Sans Contrainte":
"3. Optimisation sans contraintes". In Techniques d’optimisation Tome 1, 189–294. EDP Sciences, 2022. http://dx.doi.org/10.1051/978-2-7598-2769-5.c005.