Relevant bibliographies by topics / Optimisation nonconvexe

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Optimisation nonconvexe'

Author: Grafiati
Published: 7 July 2024
Last updated: 31 July 2025

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Journal articles on the topic "Optimisation nonconvexe"

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Smith, E. "Global Optimisation of Nonconvex MINLPs." Computers & Chemical Engineering 21, no. 1-2 (1997): S791—S796. http://dx.doi.org/10.1016/s0098-1354(97)00146-4.

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Smith, Edward M. B., and Constantinos C. Pantelides. "Global optimisation of nonconvex MINLPs." Computers & Chemical Engineering 21 (May 1997): S791—S796. http://dx.doi.org/10.1016/s0098-1354(97)87599-0.

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Martínez-legaz, J. E., and A. Seeger. "A formula on the approximate subdifferential of the difference of convex functions." Bulletin of the Australian Mathematical Society 45, no. 1 (1992): 37–41. http://dx.doi.org/10.1017/s0004972700036984.

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We give a formula on the ε−subdifferential of the difference of two convex functions. As a by-product of this formula, one recovers a recent result of Hiriart-Urruty, namely, a necessary and sufficient condition for global optimality in nonconvex optimisation.
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Scott, Carlton H., and Thomas R. Jefferson. "Duality for linear multiplicative programs." ANZIAM Journal 46, no. 3 (2005): 393–97. http://dx.doi.org/10.1017/s1446181100008336.

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AbstractLinear multiplicative programs are an important class of nonconvex optimisation problems that are currently the subject of considerable research as regards the development of computational algorithms. In this paper, we show that mathematical programs of this nature are, in fact, a special case of more general signomial programming, which in turn implies that research on this latter problem may be valuable in analysing and solving linear multiplicative programs. In particular, we use signomial programming duality theory to establish a dual program for a nonconvex linear multiplicative p
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SULTANOVA, NARGIZ. "AGGREGATE SUBGRADIENT SMOOTHING METHODS FOR LARGE SCALE NONSMOOTH NONCONVEX OPTIMISATION AND APPLICATIONS." Bulletin of the Australian Mathematical Society 91, no. 3 (2015): 523–24. http://dx.doi.org/10.1017/s0004972715000143.

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ALI, ELAF J. "CANONICAL DUAL FINITE ELEMENT METHOD FOR SOLVING NONCONVEX MECHANICS AND TOPOLOGY OPTIMISATION PROBLEMS." Bulletin of the Australian Mathematical Society 101, no. 1 (2019): 172–73. http://dx.doi.org/10.1017/s0004972719001205.

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Thi, Hoai An Le, Hoai Minh Le, and Tao Pham Dinh. "Fuzzy clustering based on nonconvex optimisation approaches using difference of convex (DC) functions algorithms." Advances in Data Analysis and Classification 1, no. 2 (2007): 85–104. http://dx.doi.org/10.1007/s11634-007-0011-2.

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Ma, Kai, Congshan Wang, Jie Yang, Chenliang Yuan, and Xinping Guan. "A pricing strategy for demand-side regulation with direct load control: a nonconvex optimisation approach." International Journal of System Control and Information Processing 2, no. 1 (2017): 74. http://dx.doi.org/10.1504/ijscip.2017.084264.

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Ma, Kai, Congshan Wang, Jie Yang, Chenliang Yuan, and Xinping Guan. "A pricing strategy for demand-side regulation with direct load control: a nonconvex optimisation approach." International Journal of System Control and Information Processing 2, no. 1 (2017): 74. http://dx.doi.org/10.1504/ijscip.2017.10005200.

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Smith, E. M. B., and C. C. Pantelides. "A symbolic reformulation/spatial branch-and-bound algorithm for the global optimisation of nonconvex MINLPs." Computers & Chemical Engineering 23, no. 4-5 (1999): 457–78. http://dx.doi.org/10.1016/s0098-1354(98)00286-5.

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Dissertations / Theses on the topic "Optimisation nonconvexe"

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Jerad, Sadok. "Approches du second ordre de d'ordre élevées pour l'optimisation nonconvex avec variantes sans évaluation de la fonction objective." Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSEP024.

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Même si l'optimisation non linéaire semble (a priori) être un domaine mature, de nouveaux schémas de minimisation sont proposés ou redécouverts pour les problèmes modernes à grande échelle. A titre d'exemple et en rétrospective de la dernière décennie, nous avons vu une vague de méthodes du premier ordre avec différentes analyses, malgré le fait que les limitations théoriques bien connues de ces méthodes ont été discutées en profondeur auparavant. Cette thèse explore deux lignes principales de recherche dans le domaine de l'optimisation non-convexe avec un accent particulier sur les méthodes d
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Giagkiozis, Ioannis. "Nonconvex many-objective optimisation." Thesis, University of Sheffield, 2012. http://etheses.whiterose.ac.uk/3683/.

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As many-objective optimisation problems become more prevalent, evolutionary algorithms that are based on Pareto dominance relations are slowly becoming less popular due to severe limitations that such an approach has for this class of problems. At the same time decomposition-based methods, which have been employed traditionally in mathematical programming, are consistently increasing in popularity. These developments have been led by recent research studies that show that decomposition-based algorithms have very good convergence properties compared to Pareto-based algorithms. Decomposition-bas
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Wahid, Faisal. "Optimisation de la rivière : enchères à court terme de l'hydroélectricité sous incertitude." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLX030.

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Le problème de l'hydro-offre consiste à calculer des d'offre optimales afin de maximiser le bénéfice attendu d'un producteur hydroélectrique participant à un marché de l'électricité. Il combine le processus décisionnel du négociant et de l'hydro-répartiteur en un seul problème d'optimisation stochastique. C'est un problème de prise de décision séquentielle, et peut être formulé comme un programme stochastique à plusieurs étages.Ces modèles peuvent être difficiles à résoudre lorsque la fonction de valeur n'est pas concave. Dans cette thèse, nous étudions quelques-unes des limites du problème hy
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Kleniati, Polyxeni M. "Decomposition schemes for polynomial optimisation, semidefinite programming and applications to nonconvex portfolio decisions." Thesis, Imperial College London, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.509792.

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Wood, Derren Wesley. "Dual sequential approximation methods in structural optimisation." Thesis, Stellenbosch : Stellenbosch University, 2012. http://hdl.handle.net/10019.1/20033.

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Thesis (PhD)--Stellenbosch University, 2012<br>ENGLISH ABSTRACT: This dissertation addresses a number of topics that arise from the use of a dual method of sequential approximate optimisation (SAO) to solve structural optimisation problems. Said approach is widely used because it allows relatively large problems to be solved efficiently by minimising the number of expensive structural analyses required. Some extensions to traditional implementations are suggested that can serve to increase the efficacy of such algorithms. The work presented herein is concerned primarily with three topics
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Sutton, Matthew William. "Variable selection and dimension reduction for structured large datasets." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/129460/1/Matthew_Sutton_Thesis.pdf.

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Recent advances in biomedical technology have allowed us to collect massive quantities of data in the hopes of gaining a better understanding of biological phenomena. This research develops new methods to tackle the challenging problem of determining which parts of these data sets provide useful information. The new methods have been used as a tool to help determine the efficacy of a new HIV vaccine.
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Repetti, Audrey. "Algorithmes d'optimisation en grande dimension : applications à la résolution de problèmes inverses." Thesis, Paris Est, 2015. http://www.theses.fr/2015PESC1032/document.

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Une approche efficace pour la résolution de problèmes inverses consiste à définir le signal (ou l'image) recherché(e) par minimisation d'un critère pénalisé. Ce dernier s'écrit souvent sous la forme d'une somme de fonctions composées avec des opérateurs linéaires. En pratique, ces fonctions peuvent n'être ni convexes ni différentiables. De plus, les problèmes auxquels on doit faire face sont souvent de grande dimension. L'objectif de cette thèse est de concevoir de nouvelles méthodes pour résoudre de tels problèmes de minimisation, tout en accordant une attention particulière aux coûts de calc
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Garrigos, Guillaume. "Descent dynamical systems and algorithms for tame optimization, and multi-objective problems." Thesis, Montpellier, 2015. http://www.theses.fr/2015MONTS191/document.

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Dans une première partie, nous nous intéressons aux systèmes dynamiques gradients gouvernés par des fonctions non lisses, mais aussi non convexes, satisfaisant l'inégalité de Kurdyka-Lojasiewicz. Après avoir obtenu quelques résultats préliminaires pour la dynamique de la plus grande pente continue, nous étudions un algorithme de descente général. Nous prouvons, sous une hypothèse de compacité, que tout suite générée par ce schéma général converge vers un point critique de la fonction. Nous obtenons aussi de nouveaux résultats sur la vitesse de convergence, tant pour les valeurs que pour les it
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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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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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Book chapters on the topic "Optimisation nonconvexe"

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Smith, Edward M. B., and Constantinos C. Pantelides. "Global Optimisation of General Process Models." In Nonconvex Optimization and Its Applications. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-5331-8_12.

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Shao, Weijia, and Sahin Albayrak. "Adaptive Zeroth-Order Optimisation of Nonconvex Composite Objectives." In Machine Learning, Optimization, and Data Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25599-1_42.

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Byrne, R. P., and I. D. L. Bogle. "Solving Nonconvex Process Optimisation Problems Using Interval Subdivision Algorithms." In Nonconvex Optimization and Its Applications. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-5331-8_5.

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Peri, Daniele, Antonio Pinto, and Emilio F. Campana. "Multi-Objective Optimisation of Expensive Objective Functions with Variable Fidelity Models." In Nonconvex Optimization and Its Applications. Springer US, 2006. http://dx.doi.org/10.1007/0-387-30065-1_14.

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Chachuat, B., and M. A. Latifi. "A New Approach in Deterministic Global Optimisation of Problems with Ordinary Differential Equations." In Nonconvex Optimization and Its Applications. Springer US, 2004. http://dx.doi.org/10.1007/978-1-4613-0251-3_5.

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Félix Mora-Camino and Luiz Gustavo Zelaya Cruz. "Advances in Data Processing for Airlines Revenue Management." In Computational Models, Software Engineering, and Advanced Technologies in Air Transportation. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-800-0.ch008.

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In this communication advances in data processing techniques applied to Airlines Revenue Management are displayed. The general introduction presents a brief review of Airlines Revenue Management. The first of the paper introduces the problem of updating the probability distributions of demand for reservations. This updating process, facing the stochastic nature of demand for travel, is a cornerstone for the design of an efficient on-line decision support system to control the reservation process for a flight by an airline. The considered problem is formulated as a dual geometric problem to whi
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Conference papers on the topic "Optimisation nonconvexe"

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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&amp;C) requirements down to hardware level. This is grounded on the parallel design and development of real-time optimisation-based G&amp;C software, allowing to concurrently identify, develop, consolidate, and validate a set of engineering practices and analysis &amp; ve
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