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Готові списки джерел за темами / Online convex optimisation

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Добірка наукової літератури з теми "Online convex optimisation"

Автор: Grafiati

Опубліковано: 16 листопада 2024

Оновлено: 31 липня 2025

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Статті в журналах з теми "Online convex optimisation"

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Fan, Wenhui, Hongwen He, and Bing Lu. "Online Active Set-Based Longitudinal and Lateral Model Predictive Tracking Control of Electric Autonomous Driving." Applied Sciences 11, no. 19 (2021): 9259. http://dx.doi.org/10.3390/app11199259.

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Autonomous driving is a breakthrough technology in the automobile and transportation fields. The characteristics of planned trajectories and tracking accuracy affect the development of autonomous driving technology. To improve the measurement accuracy of the vehicle state and realise the online application of predictive control algorithm, an online active set-based longitudinal and lateral model predictive tracking control method of autonomous driving is proposed for electric vehicles. Integrated with the vehicle inertial measurement unit (IMU) and global positioning system (GPS) information,

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Goudarzi, Pejman, Mehdi Hosseinpour, Roham Goudarzi, and Jaime Lloret. "Holistic Utility Satisfaction in Cloud Data CentreNetwork Using Reinforcement Learning." Future Internet 14, no. 12 (2022): 368. http://dx.doi.org/10.3390/fi14120368.

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Cloud computing leads to efficient resource allocation for network users. In order to achieve efficient allocation, many research activities have been conducted so far. Some researchers focus on classical optimisation theory techniques (such as multi-objective optimisation, evolutionary optimisation, game theory, etc.) to satisfy network providers and network users’ service-level agreement (SLA) requirements. Normally, in a cloud data centre network (CDCN), it is difficult to jointly satisfy both the cloud provider and cloud customer’ utilities, and this leads to complex combinatorial problems

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Bakhsh, Pir, Muhammad Ismail, Muhammad Asif Khan, Muhammad Ali, and Raheel Ahmed Memon. "Optimisation of Sentiment Analysis for E-Commerce." VFAST Transactions on Software Engineering 12, no. 3 (2024): 243–62. http://dx.doi.org/10.21015/vtse.v12i3.1907.

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Sentiment analysis is widely used today to make data-driven decisions in different industries, starting from marketing and including brand management, reputation monitoring, and customer satisfaction analysis. Its growing importance is closely linked with so-called ‘word-of-mouth’ communication, from reading online reviews to writing comments on social networks. Effective separation of sentiments ensures that companies' responses are timely and critical patterns are seen in big data sets. Statistical measures, information gain, correlation-based approaches, etc, have been employed for the feat

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4

Yu, Jichi, Jueyou Li, and Guo Chen. "Online bandit convex optimisation with stochastic constraints via two-point feedback." International Journal of Systems Science, June 15, 2023, 1–17. http://dx.doi.org/10.1080/00207721.2023.2209566.

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5

Bao, C. Y., X. Zhou, P. Wang, R. Z. He, and G. J. Tang. "A deep reinforcement learning-based approach to onboard trajectory generation for hypersonic vehicles." Aeronautical Journal, February 8, 2023, 1–21. http://dx.doi.org/10.1017/aer.2023.4.

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Abstract An onboard three-dimensional (3D) trajectory generation approach based on the reinforcement learning (RL) algorithm and deep neural network (DNN) is proposed for hypersonic vehicles in glide phase. Multiple trajectory samples are generated offline through the convex optimisation method. The deep learning (DL) is employed to pre-train the DNN for initialising the actor network and accelerating the RL process. Based on the offline deep policy deterministic actor-critic algorithm, a flight target-oriented reward function with path constraints is designed. The actor network is optimised b

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Gasparin, Andrea, Federico Julian Camerota Verdù, Daniele Catanzaro, and Lorenzo Castelli. "An evolution strategy approach for the Balanced Minimum Evolution Problem." Bioinformatics, October 27, 2023. http://dx.doi.org/10.1093/bioinformatics/btad660.

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Abstract Motivation The Balanced Minimum Evolution (BME) is a powerful distance based phylogenetic estimation model introduced by Desper and Gascuel and nowadays implemented in popular tools for phylogenetic analyses. It was proven to be computationally less demanding than more sophisticated estimation methods, e.g. maximum likelihood or Bayesian inference while preserving the statistical consistency and the ability to run with almost any kind of data for which a dissimilarity measure is available. BME can be stated in terms of a nonlinear non-convex combinatorial optimisation problem, usually

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Pokhriyal, Himani, and Goonjan Jain. "Supposititious Sarcasm Detection and Sentiment Analysis Coping Hindi Language in Social Networks Harnessing Zipf- Mandelbrot Probabilistic Optimisation and Perplexity Entropy Learning." ACM Transactions on Asian and Low-Resource Language Information Processing, January 16, 2025. https://doi.org/10.1145/3712061.

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Sarcasm is used to convey the contempt via poking and ridiculing the other person. It is frequently used to make fun of other people by saying unpleasant things. The presence of informal language patterns, modified vocabulary, and speak-text utilized in online communities makes sarcasm detection a challenging task. Due to the occupancy of these terms in the conversation it is challenging to understand and identify sarcasm. Further, sarcasm detection in Hindi, a native Indian language is itself a challenging task to perform. This study proposes a novel method for sarcasm detection in Hindi comm

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Дисертації з теми "Online convex optimisation"

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Deswarte, Raphaël. "Régression linéaire et apprentissage : contributions aux méthodes de régularisation et d’agrégation." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLX047/document.

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Анотація:

Cette thèse aborde le sujet de la régression linéaire dans différents cadres, liés notamment à l’apprentissage. Les deux premiers chapitres présentent le contexte des travaux, leurs apports et les outils mathématiques utilisés. Le troisième chapitre est consacré à la construction d’une fonction de régularisation optimale, permettant par exemple d’améliorer sur le plan théorique la régularisation de l’estimateur LASSO. Le quatrième chapitre présente, dans le domaine de l’optimisation convexe séquentielle, des accélérations d’un algorithme récent et prometteur, MetaGrad, et une conversion d’un c

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Fernandez, Camila. "Contributions and applications to survival analysis." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS230.

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L'analyse de survie a suscité l'intérêt de diverses disciplines, allant de la médecine et de la maintenance prédictive à diverses applications industrielles. Sa popularité croissante peut être attribuée aux avancées significatives en matière de puissance de calcul et à la disponibilité accrue des données. Des approches variées ont été développées pour répondre au défi des données censurées, allant des outils statistiques classiques aux techniques contemporaines d'apprentissage automatique. Cependant, il reste encore une marge considérable pour l'amélioration. Cette thèse vise à introduire des

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3

Karimi, Belhal. "Non-Convex Optimization for Latent Data Models : Algorithms, Analysis and Applications." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLX040/document.

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De nombreux problèmes en Apprentissage Statistique consistent à minimiser une fonction non convexe et non lisse définie sur un espace euclidien. Par exemple, les problèmes de maximisation de la vraisemblance et la minimisation du risque empirique en font partie.Les algorithmes d'optimisation utilisés pour résoudre ce genre de problèmes ont été largement étudié pour des fonctions convexes et grandement utilisés en pratique.Cependant, l'accrudescence du nombre d'observation dans l'évaluation de ce risque empirique ajoutée à l'utilisation de fonctions de perte de plus en plus sophistiquées représ

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Akhavanfoomani, Aria. "Derivative-free stochastic optimization, online learning and fairness." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAG001.

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Анотація:

Dans cette thèse, nous étudions d'abord le problème de l'optimisation d'ordre zéro dans le cadre actif pour des fonctions lisses et trois classes différentes de fonctions : i) les fonctions qui satisfont la condition de Polyak-Łojasiewicz, ii) les fonctions fortement convexes, et iii) la classe plus large des fonctions non convexes fortement lisses.De plus, nous proposons un nouvel algorithme basé sur la randomisation de type l1, et nous étudions ses propriétés pour les fonctions convexes Lipschitz dans un cadre d'optimisation en ligne. Notre analyse est due à la dérivation d'une nouvelle inég

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Reiffers-Masson, Alexandre. "Compétition sur la visibilité et la popularité dans les réseaux sociaux en ligne." Thesis, Avignon, 2016. http://www.theses.fr/2016AVIG0210/document.

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Cette thèse utilise la théorie des jeux pour comprendre le comportement des usagers dans les réseaux sociaux. Trois problématiques y sont abordées: "Comment maximiser la popularité des contenus postés dans les réseaux sociaux?";" Comment modéliser la répartition des messages par sujets?";"Comment minimiser la propagation d’une rumeur et maximiser la diversité des contenus postés?". Après un état de l’art concernant ces questions développé dans le chapitre 1, ce travail traite, dans le chapitre 2, de la manière d’aborder l’environnement compétitif pour accroître la visibilité. Dans le chapitre

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Ho, Vinh Thanh. "Techniques avancées d'apprentissage automatique basées sur la programmation DC et DCA." Electronic Thesis or Diss., Université de Lorraine, 2017. http://www.theses.fr/2017LORR0289.

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Dans cette thèse, nous développons certaines techniques avancées d'apprentissage automatique dans le cadre de l'apprentissage en ligne et de l'apprentissage par renforcement (« reinforcement learning » en anglais -- RL). L'épine dorsale de nos approches est la programmation DC (Difference of Convex functions) et DCA (DC Algorithm), et leur version en ligne, qui sont reconnues comme de outils puissants d'optimisation non convexe, non différentiable. Cette thèse se compose de deux parties : la première partie étudie certaines techniques d'apprentissage automatique en mode en ligne et la deuxième

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Ho, Vinh Thanh. "Techniques avancées d'apprentissage automatique basées sur la programmation DC et DCA." Thesis, Université de Lorraine, 2017. http://www.theses.fr/2017LORR0289/document.

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Анотація:

Dans cette thèse, nous développons certaines techniques avancées d'apprentissage automatique dans le cadre de l'apprentissage en ligne et de l'apprentissage par renforcement (« reinforcement learning » en anglais -- RL). L'épine dorsale de nos approches est la programmation DC (Difference of Convex functions) et DCA (DC Algorithm), et leur version en ligne, qui sont reconnues comme de outils puissants d'optimisation non convexe, non différentiable. Cette thèse se compose de deux parties : la première partie étudie certaines techniques d'apprentissage automatique en mode en ligne et la deuxième

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El, Gueddari Loubna. "Proximal structured sparsity regularization for online reconstruction in high-resolution accelerated Magnetic Resonance imaging." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS573.

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L'imagerie par résonance magnétique (IRM) est la technique d'imagerie médicale de référence pour sonder in vivo et non invasivement les tissus mous du corps humain, en particulier le cerveau.L'amélioration de la résolution de l'IRM en un temps d'acquisition standard (400µm isotrope en 15 minutes) permettrait aux médecins d'améliorer considérablement leur diagnostic et le suivi des patients. Cependant, le temps d'acquisition en IRM reste long. Pour réduire ce temps, la récente théorie de l'échantillonnage comprimée (EC) a révolutionné la façon d'acquérir des données dans plusieurs domaines dont

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Частини книг з теми "Online convex optimisation"

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Anagnostopoulos Christoforos, Tasoulis Dimitris, Hand David J., and Adams Niall M. "Online optimization for variable selection in data streams." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2008. https://doi.org/10.3233/978-1-58603-891-5-132.

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Variable selection for regression is a classical statistical problem, motivated by concerns that too many covariates invite overfitting. Existing approaches notably include a class of convex optimisation techniques, such as the Lasso algorithm. Such techniques are invariably reliant on assumptions that are unrealistic in streaming contexts, namely that the data is available off-line and the correlation structure is static. In this paper, we relax both these constraints, proposing for the first time an online implementation of the Lasso algorithm with exponential forgetting. We also optimise th

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Тези доповідей конференцій з теми "Online convex optimisation"

1

Lourenço, Pedro, Hugo Costa, João Branco, et al. "Verification & validation of optimisation-based control systems: methods and outcomes of VV4RTOS." In ESA 12th International Conference on Guidance Navigation and Control and 9th International Conference on Astrodynamics Tools and Techniques. ESA, 2023. http://dx.doi.org/10.5270/esa-gnc-icatt-2023-155.

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VV4RTOS is an activity supported by the European Space Agency aimed at the development and validation of a framework for the verification and validation of spacecraft guidance, navigation, and control (GNC) systems based on embedded optimisation, tailored to handle different layers of abstraction, from guidance and control (G&C) requirements down to hardware level. This is grounded on the parallel design and development of real-time optimisation-based G&C software, allowing to concurrently identify, develop, consolidate, and validate a set of engineering practices and analysis & ve

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Filipski, Tatiana. "The valorization of students museum education within the school – museum – family – community interconnectivity during the pandemic crisis." In Condiții pedagogice de optimizare a învățării în post criză pandemică prin prisma dezvoltării gândirii științifice. "Ion Creanga" State Pedagogical University, 2021. http://dx.doi.org/10.46728/c.18-06-2021.p262-267.

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The article reflects on the pandemic crisis impact on museal education of students and touches the problem of collaboration of educational institution with museum, family and community in this period, which is difficult for the entire society. In this context were developed and signed multiple collaboration contracts in the view of museum education process optimisation and educational institution-museum-family-community interoperability, building an online oriented museum education methodology, in which were actively and sistematically involved pupils, students, proffesors, school managers, pr

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3

Baert, Lieven, Ingrid Lepot, Caroline Sainvitu, Emmanuel Chérière, Arnaud Nouvellon, and Vincent Leonardon. "Aerodynamic Optimisation of the Low Pressure Turbine Module: Exploiting Surrogate Models in a High-Dimensional Design Space." In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-91570.

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Abstract Further improvement of state-of-the-art Low Pressure (LP) turbines has become progressively more challenging. LP design is more than ever confronted to the need to further integrate complex models and to shift from single component design to the design of the complete LPT module at once. This leads to high-dimensional design spaces and automatically challenges its applicability within an industrial context, where CPU resources are limited and the cycle time crucial. The aerodynamic design of a multistage LP turbine is discussed for a design space defined by 350 parameters. Using an on

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4

Tyrrell, Grainne, Donna Curley, Leonard O' Sullivan, and Eoin White. "Comparing Perceptions of Human Factors - Priorities of Cardiologists and Biomedical Engineers in the Design of Cardiovascular Devices." In 15th International Conference on Applied Human Factors and Ergonomics (AHFE 2024). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1005076.

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This study aimed to understand perceptions of Human Factors (HF) within the Product Development Process (PDP) of catheter-based cardiovascular therapies. Attitudes of biomedical engineers were compared to those of the clinicians who use these devices. The main objectives were to:1.Determine how Engineers and Cardiologists perceive HF impact on user experience.2.Gain an understanding of how various design factors affect the user experience. 3.Identify Engineers’ familiarity with HF resources and understand what HF data they seek during the PDP.By identifying and later filling data gaps and barr

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Звіти організацій з теми "Online convex optimisation"

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Kaufmann, Joachim, Peter Kaufmann, and Simone Maria Grabner. Assessment of completed BRIDGE Discovery projects Synthesis at programme level. BMK - Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology, 2023. http://dx.doi.org/10.22163/fteval.2023.640.

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The aim of this short evaluation was to systematically collect information from the completed projects of the BRIDGE Discovery programme as of June 2023. This will be used for strategic optimisation and decision making for the funding period 2025-2028. BRIDGE Discovery is an open-topic funding programme at the interface between basic and applied research, which is jointly funded and implemented by the Swiss National Science Foundation (SNSF) and Innosuisse - Swiss Agency for Innovation Promotion. The study used a mixed-methods approach to gather information about the programme context and the

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