Littérature scientifique sur le sujet « Cardinality constrained optimization »
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Articles de revues sur le sujet "Cardinality constrained optimization"
Kanzow, Christian, Andreas B. Raharja et Alexandra Schwartz. « An Augmented Lagrangian Method for Cardinality-Constrained Optimization Problems ». Journal of Optimization Theory and Applications 189, no 3 (29 avril 2021) : 793–813. http://dx.doi.org/10.1007/s10957-021-01854-7.
Texte intégralBertsimas, Dimitris, et Romy Shioda. « Algorithm for cardinality-constrained quadratic optimization ». Computational Optimization and Applications 43, no 1 (15 novembre 2007) : 1–22. http://dx.doi.org/10.1007/s10589-007-9126-9.
Texte intégralKanzow, Christian, Andreas B. Raharja et Alexandra Schwartz. « Sequential optimality conditions for cardinality-constrained optimization problems with applications ». Computational Optimization and Applications 80, no 1 (22 juillet 2021) : 185–211. http://dx.doi.org/10.1007/s10589-021-00298-z.
Texte intégralStephan, Rüdiger. « Cardinality constrained combinatorial optimization : Complexity and polyhedra ». Discrete Optimization 7, no 3 (août 2010) : 99–113. http://dx.doi.org/10.1016/j.disopt.2010.03.002.
Texte intégralCai, L. « Parameterized Complexity of Cardinality Constrained Optimization Problems ». Computer Journal 51, no 1 (6 mars 2007) : 102–21. http://dx.doi.org/10.1093/comjnl/bxm086.
Texte intégralBacanin, Nebojsa, et Milan Tuba. « Firefly Algorithm for Cardinality Constrained Mean-Variance Portfolio Optimization Problem with Entropy Diversity Constraint ». Scientific World Journal 2014 (2014) : 1–16. http://dx.doi.org/10.1155/2014/721521.
Texte intégralXu, Fengmin, Yuhong Dai, Zhihu Zhao et Zongben Xu. « Efficient projected gradient methods for cardinality constrained optimization ». Science China Mathematics 62, no 2 (25 avril 2018) : 245–68. http://dx.doi.org/10.1007/s11425-016-9124-0.
Texte intégralSadjadi, Seyed Jafar, Mohsen Gharakhani et Ehram Safari. « Robust optimization framework for cardinality constrained portfolio problem ». Applied Soft Computing 12, no 1 (janvier 2012) : 91–99. http://dx.doi.org/10.1016/j.asoc.2011.09.006.
Texte intégralShaw, Dong X., Shucheng Liu et Leonid Kopman. « Lagrangian relaxation procedure for cardinality-constrained portfolio optimization ». Optimization Methods and Software 23, no 3 (juin 2008) : 411–20. http://dx.doi.org/10.1080/10556780701722542.
Texte intégralFebrianti, Werry, Kuntjoro Adji Sidarto et Novriana Sumarti. « Solving Constrained Mean-Variance Portfolio Optimization Problems Using Spiral Optimization Algorithm ». International Journal of Financial Studies 11, no 1 (20 décembre 2022) : 1. http://dx.doi.org/10.3390/ijfs11010001.
Texte intégralThèses sur le sujet "Cardinality constrained optimization"
Aslan, Murat. « The Cardinality Constrained Multiple Knapsack Problem ». Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12610131/index.pdf.
Texte intégralLi, Yibo. « Solving cardinality constrained portfolio optimisation problem using genetic algorithms and ant colony optimisation ». Thesis, Brunel University, 2015. http://bura.brunel.ac.uk/handle/2438/10867.
Texte intégralBucher, Max [Verfasser], Alexandra [Akademischer Betreuer] Schwartz et Christian [Akademischer Betreuer] Kanzow. « Optimality Conditions and Numerical Methods for a Continuous Reformulation of Cardinality Constrained Optimization Problems / Max Bucher ; Alexandra Schwartz, Christian Kanzow ». Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2018. http://d-nb.info/1167926323/34.
Texte intégralKreber, Dennis [Verfasser], Sven de [Akademischer Betreuer] Vries, Jan Pablo [Akademischer Betreuer] Burgard, Sven de [Gutachter] Vries, Jan Pablo [Gutachter] Burgard et Christoph [Gutachter] Buchheim. « Cardinality-Constrained Discrete Optimization for Regression / Dennis Kreber ; Gutachter : Sven de Vries, Jan Pablo Burgard, Christoph Buchheim ; Sven de Vries, Jan Pablo Burgard ». Trier : Universität Trier, 2019. http://d-nb.info/1197809198/34.
Texte intégralDias, Carlos Henrique. « Um novo algoritmo genetico para a otimização de carteiras de investimento com restrições de cardinalidade ». [s.n.], 2008. http://repositorio.unicamp.br/jspui/handle/REPOSIP/307124.
Texte intégralDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica
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Resumo: Este trabalho tem por finalidade a determinação da fronteira eficiente de investimento através da otimização do modelo de média-variância com restrições de cardinalidade e limite inferior de investimento. Por tratar-se de um problema inteiro e não linear, cuja solução exata é de difícil obtenção, optamos por empregar um algoritmo genético, na linha desenvolvida por Chang et al. [3], que até hoje serve como referência para a determinação da fronteira eficiente de Pareto para problemas de otimização de investimentos. Entretanto, verificamos que o algoritmo proposto por Chang et al. apresenta uma distribuição não uniforme na geração de soluções aleatórias. Para contornar esse problema, introduzimos um novo esquema de geração de cromossomos, baseado na discretização do espaço, que permite a geração de soluções que satisfazem diretamente a restrição de montante total aplicado. Com essa nova abordagem, foi possível definir operadores de seleção, crossover e mutação bastante eficientes. Os resultados obtidos mostram que o novo algoritmo é mais robusto que aquele proposto por Chang et al
Abstract: In this work we consider the problem of determining of the efficient frontier of a portfolio using the mean-variance model subject to a cardinality constrain and to lower bounds on the amount invested in the selected assets. As this nonlinear integer programming problem is hard to solve exactly, we use a genetic algorithm, following the lines described by Chang et al. [3], still considered as a reference in the field. However, as the feasible solutions generated by the algorithm of Chang et al. are not uniformly distributed over the solution set, we introduce a new scheme for defining the chromosomes, based on the discretization of the feasible region, so that the amount invested always sum up to one for every solution obtained by the algorithm. This new approach allows us to define very efficient selection, crossover and mutation procedures. The numerical results obtained so far show that the new method is more robust than the one proposed by Chang et al
Mestrado
Otimização
Mestre em Matemática Aplicada
Villela, Pedro Ferraz 1982. « Um algoritmo exato para a otimização de carteiras de investimento com restrições de cardinalidade ». [s.n.], 2008. http://repositorio.unicamp.br/jspui/handle/REPOSIP/307123.
Texte intégralDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica
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Resumo: Neste trabalho, propomos um método exato para a resolução de problemas de programação quadrática que envolvem restrições de cardinalidade. Como aplicação, empregamos o método para a obtenção da fronteira eficiente de um problema (bi-objetivo) de otimização de carteiras de investimento. Nosso algoritmo é baseado no método Branch-and-Bound. A chave de seu sucesso, entretanto, reside no uso do método de Lemke, que é aplicado para a resolução dos subproblemas associados aos nós da árvore gerada pelo Branch-and-Bound. Ao longo do texto, algumas heurísticas também são introduzidas, com o propósito de acelerar a convergência do método. Os resultados computacionais obtidos comprovam que o algoritmo proposto é eficiente.
Abstract: In this work, we propose an exact method for the resolution of quadratic programming problems involving cardinality restrictions. As an application, the algorithm is used to generate the effective Pareto frontier of a (bi-objective) portfolio optimization problem. This algorithm is based on the Branch-and-Bound method. The key to its success, however, resides in the application of Lemke's method to the resolution of the subproblems associated to the nodes of the tree generated by the Branch-and-Bound algorithm. Throughout the text, some heuristics are also introduced as a way to accelerate the performance of the method. The computational results acquired show that the proposed algorithm is efficient.
Mestrado
Otimização
Mestre em Matemática Aplicada
« On cardinality constrained optimization ». Thesis, 2009. http://library.cuhk.edu.hk/record=b6074943.
Texte intégralGao, Jianjun.
Adviser: Duan Li.
Source: Dissertation Abstracts International, Volume: 72-11, Section: B, page: .
Thesis (Ph.D.)--Chinese University of Hong Kong, 2009.
Includes bibliographical references (leaves 134-142).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstract also in Chinese.
« Cardinality constrained portfolio selection using clustering methodology ». 2011. http://library.cuhk.edu.hk/record=b5894828.
Texte intégral"August 2011."
Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.
Includes bibliographical references (p. 90-93).
Abstracts in English and Chinese.
Chapter 1 --- Introduction --- p.1
Chapter 2 --- Portfolio Selection Using Clustering Methodology --- p.7
Chapter 2.1 --- Heuristic algorithm --- p.8
Chapter 2.1.1 --- Step 1: Security transformation by factor model --- p.8
Chapter 2.1.2 --- Step 2: Clustering algorithm --- p.10
Chapter 2.1.3 --- Step 3: Representative selection by t he Sliarpe ratio --- p.16
Chapter 2.2 --- Numerical results --- p.17
Chapter 3 --- Modified Portfolio Selection Using Clustering Methodology --- p.22
Chapter 3.1 --- Analysis of artificial factors --- p.23
Chapter 3.2 --- Problem reformulation --- p.27
Chapter 3.3 --- Numerical results --- p.29
Chapter 4 --- Minimum Variance Point --- p.70
Chapter 4.1 --- Iterative elimination scheme I --- p.72
Chapter 4.2 --- Iterative elimination scheme II --- p.74
Chapter 4.3 --- Orthogonal matrix mapping --- p.76
Chapter 4.4 --- Condition to solve diagonal dominant problem --- p.77
Chapter 4.5 --- L1 formulation --- p.82
Chapter 4.6 --- Numerical results --- p.85
Chapter 5 --- Summary and Future work --- p.88
« Cardinality constrained discrete-time linear-quadratic control ». 2005. http://library.cuhk.edu.hk/record=b5892706.
Texte intégralThesis (M.Phil.)--Chinese University of Hong Kong, 2005.
Includes bibliographical references (leaves 75-76).
Abstracts in English and Chinese.
Chapter 1 --- Introduction --- p.1
Chapter 2 --- Solution Framework Using Dynamic Programming --- p.7
Chapter 2.1 --- Difficulty of using dynamic programming --- p.8
Chapter 2.2 --- Scalar-state problems --- p.12
Chapter 2.3 --- Time-invariant system --- p.17
Chapter 2.4 --- Illustrative example of a scalar-state problem --- p.21
Chapter 3 --- Cardinality Constrained Quadratic Optimization --- p.26
Chapter 3.1 --- Reformulation --- p.27
Chapter 3.2 --- NP hardness --- p.31
Chapter 3.3 --- Solving CCQP with an efficient branch and bound method --- p.34
Chapter 3.3.1 --- Efficient branch and bound algorithm --- p.34
Chapter 3.3.2 --- Geometrical interpretation of the proposed ranking order --- p.48
Chapter 3.3.3 --- Additional algorithmic ideas for enhancing computational efficiency --- p.56
Chapter 3.4 --- Numerical example and computational results --- p.60
Chapter 4 --- Summary and Future Work --- p.73
McCarthy, Philip James. « Cardinality Constrained Robust Optimization Applied to a Class of Interval Observers ». Thesis, 2013. http://hdl.handle.net/10012/7907.
Texte intégralChapitres de livres sur le sujet "Cardinality constrained optimization"
de Farias, Ismael R., et George L. Nemhauser. « A Polyhedral Study of the Cardinality Constrained Knapsack Problem ». Dans Integer Programming and Combinatorial Optimization, 291–303. Berlin, Heidelberg : Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-47867-1_21.
Texte intégralKhokhar, Mulazim-Ali, Kris Boudt et Chunlin Wan. « Cardinality-Constrained Higher-Order Moment Portfolios Using Particle Swarm Optimization ». Dans Applying Particle Swarm Optimization, 169–87. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70281-6_10.
Texte intégralStreichert, Felix, Holger Ulmer et Andreas Zell. « Evolutionary Algorithms and the Cardinality Constrained Portfolio Optimization Problem ». Dans Operations Research Proceedings, 253–60. Berlin, Heidelberg : Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17022-5_33.
Texte intégralCui, Min, Donglei Du, Ling Gai et Ruiqi Yang. « A Linear-Time Streaming Algorithm for Cardinality-Constrained Maximizing Monotone Non-submodular Set Functions ». Dans Combinatorial Optimization and Applications, 96–110. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92681-6_9.
Texte intégralAhmad, Faisal, Faraz Hasan, Mohammad Shahid, Jahangir Chauhan et Mohammad Imran. « Cardinality Constrained Portfolio Selection Strategy Based on Hybrid Metaheuristic Optimization Algorithm ». Dans Proceedings of International Conference on Data Science and Applications, 853–62. Singapore : Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6631-6_59.
Texte intégralPleshakov, Michael, Sergei Sidorov et Kirill Spiridonov. « Convergence Analysis of Penalty Decomposition Algorithm for Cardinality Constrained Convex Optimization in Hilbert Spaces ». Dans Mathematical Optimization Theory and Operations Research, 141–53. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49988-4_10.
Texte intégralMayambala, Fred, Elina Rönnberg et Torbjörn Larsson. « Tight Upper Bounds on the Cardinality Constrained Mean-Variance Portfolio Optimization Problem Using Truncated Eigendecomposition ». Dans Operations Research Proceedings, 385–92. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28697-6_54.
Texte intégralRuiz-Torrubiano, Rubén, Sergio García-Moratilla et Alberto Suárez. « Optimization Problems with Cardinality Constraints ». Dans Computational Intelligence in Optimization, 105–30. Berlin, Heidelberg : Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12775-5_5.
Texte intégralRégin, Jean-Charles. « Combination of Among and Cardinality Constraints ». Dans Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, 288–303. Berlin, Heidelberg : Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11493853_22.
Texte intégralKocjan, Waldemar, et Per Kreuger. « Filtering Methods for Symmetric Cardinality Constraint ». Dans Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, 200–208. Berlin, Heidelberg : Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24664-0_14.
Texte intégralActes de conférences sur le sujet "Cardinality constrained optimization"
Friedrich, Tobias, Timo Kötzing, Aishwarya Radhakrishnan, Leon Schiller, Martin Schirneck, Georg Tennigkeit et Simon Wietheger. « Crossover for cardinality constrained optimization ». Dans GECCO '22 : Genetic and Evolutionary Computation Conference. New York, NY, USA : ACM, 2022. http://dx.doi.org/10.1145/3512290.3528713.
Texte intégralCheng, Runze, et Jianjun Gao. « On cardinality constrained mean-CVaR portfolio optimization ». Dans 2015 27th Chinese Control and Decision Conference (CCDC). IEEE, 2015. http://dx.doi.org/10.1109/ccdc.2015.7162076.
Texte intégralGomez, Miguel A., Carmen X. Flores et Maria A. Osorio. « Hybrid search for cardinality constrained portfolio optimization ». Dans the 8th annual conference. New York, New York, USA : ACM Press, 2006. http://dx.doi.org/10.1145/1143997.1144302.
Texte intégralParizy, Matthieu, Przemyslaw Sadowski et Nozomu Togawa. « Cardinality Constrained Portfolio Optimization on an Ising Machine ». Dans 2022 IEEE 35th International System-on-Chip Conference (SOCC). IEEE, 2022. http://dx.doi.org/10.1109/socc56010.2022.9908082.
Texte intégralZhang, Jize, Tim Leung et Aleksandr Aravkin. « A Relaxed Optimization Approach for Cardinality-Constrained Portfolios ». Dans 2019 18th European Control Conference (ECC). IEEE, 2019. http://dx.doi.org/10.23919/ecc.2019.8796164.
Texte intégralCui, Tianxiang, Shi Cheng et Ruibin Bai. « A combinatorial algorithm for the cardinality constrained portfolio optimization problem ». Dans 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014. http://dx.doi.org/10.1109/cec.2014.6900357.
Texte intégralMcCarthy, Philip James, Christopher Nielsen et Stephen L. Smith. « Cardinality constrained robust optimization applied to a class of interval observers ». Dans 2014 American Control Conference - ACC 2014. IEEE, 2014. http://dx.doi.org/10.1109/acc.2014.6859149.
Texte intégralChen, Angela H. L., Yun-Chia Liang et Chia-Chien Liu. « An artificial bee colony algorithm for the cardinality-constrained portfolio optimization problems ». Dans 2012 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2012. http://dx.doi.org/10.1109/cec.2012.6252920.
Texte intégralSuthiwong, Dit, et Maleerat Sodanil. « Cardinality-constrained Portfolio optimization using an improved quick Artificial Bee Colony Algorithm ». Dans 2016 International Computer Science and Engineering Conference (ICSEC). IEEE, 2016. http://dx.doi.org/10.1109/icsec.2016.7859943.
Texte intégralMin, Jiang, Zhiqing Meng, Gengui Zhou et Rui Shen. « A Smoothing Penalty Function Algorithm for Two-Cardinality Sparse Constrained Optimization Problems ». Dans 2018 14th International Conference on Computational Intelligence and Security (CIS). IEEE, 2018. http://dx.doi.org/10.1109/cis2018.2018.00018.
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