Relevant bibliographies by topics / Cardinality constrained optimization

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

Academic literature on the topic 'Cardinality constrained optimization'

Author: Grafiati
Published: 10 March 2023

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Journal articles on the topic "Cardinality constrained optimization"

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Kanzow, Christian, Andreas B. Raharja, and Alexandra Schwartz. "An Augmented Lagrangian Method for Cardinality-Constrained Optimization Problems." Journal of Optimization Theory and Applications 189, no. 3 (2021): 793–813. http://dx.doi.org/10.1007/s10957-021-01854-7.

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AbstractA reformulation of cardinality-constrained optimization problems into continuous nonlinear optimization problems with an orthogonality-type constraint has gained some popularity during the last few years. Due to the special structure of the constraints, the reformulation violates many standard assumptions and therefore is often solved using specialized algorithms. In contrast to this, we investigate the viability of using a standard safeguarded multiplier penalty method without any problem-tailored modifications to solve the reformulated problem. We prove global convergence towards an (essentially strongly) stationary point under a suitable problem-tailored quasinormality constraint qualification. Numerical experiments illustrating the performance of the method in comparison to regularization-based approaches are provided.
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Bertsimas, Dimitris, and Romy Shioda. "Algorithm for cardinality-constrained quadratic optimization." Computational Optimization and Applications 43, no. 1 (2007): 1–22. http://dx.doi.org/10.1007/s10589-007-9126-9.

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Kanzow, Christian, Andreas B. Raharja, and Alexandra Schwartz. "Sequential optimality conditions for cardinality-constrained optimization problems with applications." Computational Optimization and Applications 80, no. 1 (2021): 185–211. http://dx.doi.org/10.1007/s10589-021-00298-z.

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AbstractRecently, a new approach to tackle cardinality-constrained optimization problems based on a continuous reformulation of the problem was proposed. Following this approach, we derive a problem-tailored sequential optimality condition, which is satisfied at every local minimizer without requiring any constraint qualification. We relate this condition to an existing M-type stationary concept by introducing a weak sequential constraint qualification based on a cone-continuity property. Finally, we present two algorithmic applications: We improve existing results for a known regularization method by proving that it generates limit points satisfying the aforementioned optimality conditions even if the subproblems are only solved inexactly. And we show that, under a suitable Kurdyka–Łojasiewicz-type assumption, any limit point of a standard (safeguarded) multiplier penalty method applied directly to the reformulated problem also satisfies the optimality condition. These results are stronger than corresponding ones known for the related class of mathematical programs with complementarity constraints.
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Stephan, Rüdiger. "Cardinality constrained combinatorial optimization: Complexity and polyhedra." Discrete Optimization 7, no. 3 (2010): 99–113. http://dx.doi.org/10.1016/j.disopt.2010.03.002.

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Cai, L. "Parameterized Complexity of Cardinality Constrained Optimization Problems." Computer Journal 51, no. 1 (2007): 102–21. http://dx.doi.org/10.1093/comjnl/bxm086.

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Bacanin, Nebojsa, and 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.

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Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results.
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Xu, Fengmin, Yuhong Dai, Zhihu Zhao, and Zongben Xu. "Efficient projected gradient methods for cardinality constrained optimization." Science China Mathematics 62, no. 2 (2018): 245–68. http://dx.doi.org/10.1007/s11425-016-9124-0.

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Sadjadi, Seyed Jafar, Mohsen Gharakhani, and Ehram Safari. "Robust optimization framework for cardinality constrained portfolio problem." Applied Soft Computing 12, no. 1 (2012): 91–99. http://dx.doi.org/10.1016/j.asoc.2011.09.006.

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Shaw, Dong X., Shucheng Liu, and Leonid Kopman. "Lagrangian relaxation procedure for cardinality-constrained portfolio optimization." Optimization Methods and Software 23, no. 3 (2008): 411–20. http://dx.doi.org/10.1080/10556780701722542.

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Febrianti, Werry, Kuntjoro Adji Sidarto, and Novriana Sumarti. "Solving Constrained Mean-Variance Portfolio Optimization Problems Using Spiral Optimization Algorithm." International Journal of Financial Studies 11, no. 1 (2022): 1. http://dx.doi.org/10.3390/ijfs11010001.

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Portfolio optimization is an activity for balancing return and risk. In this paper, we used mean-variance (M-V) portfolio models with buy-in threshold and cardinality constraints. This model can be formulated as a mixed integer nonlinear programming (MINLP) problem. To solve this constrained mean-variance portfolio optimization problem, we propose the use of a modified spiral optimization algorithm (SOA). Then, we use Bartholomew-Biggs and Kane’s data to validate our proposed algorithm. The results show that our proposed algorithm can be an efficient tool for solving this portfolio optimization problem.
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Dissertations / Theses on the topic "Cardinality constrained optimization"

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Aslan, Murat. "The Cardinality Constrained Multiple Knapsack Problem." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12610131/index.pdf.

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The classical multiple knapsack problem selects a set of items and assigns each to one of the knapsacks so as to maximize the total profit. The knapsacks have limited capacities. The cardinality constrained multiple knapsack problem assumes limits on the number of items that are to be put in each knapsack, as well. Despite many efforts on the classical multiple knapsack problem, the research on the cardinality constrained multiple knapsack problem is scarce. In this study we consider the cardinality constrained multiple knapsack problem. We propose heuristic and optimization procedures that rely on the optimal solutions of the linear programming relaxation problem. Our computational results on the large-sized problem instances have shown the satisfactory performances of our algorithms.
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Li, 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.

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In this thesis we consider solution approaches for the index tacking problem, in which we aim to reproduces the performance of a market index without purchasing all of the stocks that constitute the index. We solve the problem using three different solution approaches: Mixed Integer Programming (MIP), Genetic Algorithms (GAs), and Ant-colony Optimization (ACO) Algorithm by limiting the number of stocks that can be held. Each index is also assigned with different cardinalities to examine the change to the solution values. All of the solution approaches are tested by considering eight market indices. The smallest data set only consists of 31 stocks whereas the largest data set includes over 2000 stocks. The computational results from the MIP are used as the benchmark to measure the performance of the other solution approaches. The Computational results are presented for different solution approaches and conclusions are given. Finally, we implement post analysis and investigate the best tracking portfolios achieved from the three solution approaches. We summarise the findings of the investigation, and in turn, we further improve some of the algorithms. As the formulations of these problems are mixed-integer linear programs, we use the solver ‘Cplex’ to solve the problems. All of the programming is coded in AMPL.
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Bucher, Max [Verfasser], Alexandra [Akademischer Betreuer] Schwartz, and 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.

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Kreber, Dennis [Verfasser], Sven de [Akademischer Betreuer] Vries, Jan Pablo [Akademischer Betreuer] Burgard, Sven de [Gutachter] Vries, Jan Pablo [Gutachter] Burgard, and 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.

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Dias, 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.

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Orientador: Francisco de Assis Magalhães Gomes Neto<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica<br>Made available in DSpace on 2018-08-10T22:50:20Z (GMT). No. of bitstreams: 1 Dias_CarlosHenrique_M.pdf: 2721795 bytes, checksum: 57d6019ecabf33034889a64675ccf707 (MD5) Previous issue date: 2008<br>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<br>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<br>Mestrado<br>Otimização<br>Mestre em Matemática Aplicada
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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.

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Orientador: Francisco de Assis Magalhães Gomes Neto<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica<br>Made available in DSpace on 2018-08-12T16:09:04Z (GMT). No. of bitstreams: 1 Villela_PedroFerraz_M.pdf: 727069 bytes, checksum: d87d64ae49bfc1a53017a463cf10b453 (MD5) Previous issue date: 2008<br>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.<br>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.<br>Mestrado<br>Otimização<br>Mestre em Matemática Aplicada
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"On cardinality constrained optimization." Thesis, 2009. http://library.cuhk.edu.hk/record=b6074943.

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Although cardinality constraints naturally arise in many applications, e.g., in portfolio selection problems of choosing small number of assets from a large pool of stocks or dynamic portfolio selection problems with limited trading dates within a given time horizon and in subset selection of the regression analysis, the state-of-the-art in cardinality constrained optimization has been stagnant up to this stage, largely due to the inherent combinatorial nature of such hard problems. We focus in this research on developing efficient and implementable solution algorithms for cardinality constrained optimization by investigating prominent structures and hidden properties of such problems. More specifically, we develop solution algorithms for four specific cardinality constrained optimization problems, including (i) the cardinality constrained linear-quadratic control problem, (ii) the optimal control problem of linear switched system with limited number of switching, (iii) the time cardinality constrained dynamic mean- variance portfolio selection problem, and (iv) cardinality constrained quadratic optimization problem. Taking advantages of a linear-quadratic structure of cardinality constrained optimization problems, we strive for analytical solutions when possible. More specifically, we derive an analytical solution for problem (iii) and obtain for both problems (i) and (ii) semi-analytical expressions of the solution governed by a family of Ricatti-like equations, which still suffer an exponentially growing complexity. To achieve high-performance of the solution algorithm, we devise algorithms of a branch and bound (BnB) type with various tight and computationally-cheap lower bounds achieved by identifying suitable SDP formulations and by exploiting geometric properties of the problem. We demonstrate efficiency of our proposed solution schemes evidenced from numerical experiments and present a firm step-forward in tackling this long-standing challenge of cardinality constrained optimization.<br>Gao, Jianjun.<br>Adviser: Duan Li.<br>Source: Dissertation Abstracts International, Volume: 72-11, Section: B, page: .<br>Thesis (Ph.D.)--Chinese University of Hong Kong, 2009.<br>Includes bibliographical references (leaves 134-142).<br>Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.<br>Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.<br>Abstract also in Chinese.
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"Cardinality constrained portfolio selection using clustering methodology." 2011. http://library.cuhk.edu.hk/record=b5894828.

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Jiang, Kening.<br>"August 2011."<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.<br>Includes bibliographical references (p. 90-93).<br>Abstracts in English and Chinese.<br>Chapter 1 --- Introduction --- p.1<br>Chapter 2 --- Portfolio Selection Using Clustering Methodology --- p.7<br>Chapter 2.1 --- Heuristic algorithm --- p.8<br>Chapter 2.1.1 --- Step 1: Security transformation by factor model --- p.8<br>Chapter 2.1.2 --- Step 2: Clustering algorithm --- p.10<br>Chapter 2.1.3 --- Step 3: Representative selection by t he Sliarpe ratio --- p.16<br>Chapter 2.2 --- Numerical results --- p.17<br>Chapter 3 --- Modified Portfolio Selection Using Clustering Methodology --- p.22<br>Chapter 3.1 --- Analysis of artificial factors --- p.23<br>Chapter 3.2 --- Problem reformulation --- p.27<br>Chapter 3.3 --- Numerical results --- p.29<br>Chapter 4 --- Minimum Variance Point --- p.70<br>Chapter 4.1 --- Iterative elimination scheme I --- p.72<br>Chapter 4.2 --- Iterative elimination scheme II --- p.74<br>Chapter 4.3 --- Orthogonal matrix mapping --- p.76<br>Chapter 4.4 --- Condition to solve diagonal dominant problem --- p.77<br>Chapter 4.5 --- L1 formulation --- p.82<br>Chapter 4.6 --- Numerical results --- p.85<br>Chapter 5 --- Summary and Future work --- p.88
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"Cardinality constrained discrete-time linear-quadratic control." 2005. http://library.cuhk.edu.hk/record=b5892706.

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Gao Jianjun.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.<br>Includes bibliographical references (leaves 75-76).<br>Abstracts in English and Chinese.<br>Chapter 1 --- Introduction --- p.1<br>Chapter 2 --- Solution Framework Using Dynamic Programming --- p.7<br>Chapter 2.1 --- Difficulty of using dynamic programming --- p.8<br>Chapter 2.2 --- Scalar-state problems --- p.12<br>Chapter 2.3 --- Time-invariant system --- p.17<br>Chapter 2.4 --- Illustrative example of a scalar-state problem --- p.21<br>Chapter 3 --- Cardinality Constrained Quadratic Optimization --- p.26<br>Chapter 3.1 --- Reformulation --- p.27<br>Chapter 3.2 --- NP hardness --- p.31<br>Chapter 3.3 --- Solving CCQP with an efficient branch and bound method --- p.34<br>Chapter 3.3.1 --- Efficient branch and bound algorithm --- p.34<br>Chapter 3.3.2 --- Geometrical interpretation of the proposed ranking order --- p.48<br>Chapter 3.3.3 --- Additional algorithmic ideas for enhancing computational efficiency --- p.56<br>Chapter 3.4 --- Numerical example and computational results --- p.60<br>Chapter 4 --- Summary and Future Work --- p.73
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McCarthy, Philip James. "Cardinality Constrained Robust Optimization Applied to a Class of Interval Observers." Thesis, 2013. http://hdl.handle.net/10012/7907.

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Observers are used in the monitoring and control of dynamical systems to deduce the values of unmeasured states. Designing an observer requires having an accurate model of the plant — if the model parameters are characterized imprecisely, the observer may not provide reliable estimates. An interval observer, which comprises an upper and lower observer, bounds the plant's states from above and below, given the range of values of the imprecisely characterized parameters, i.e., it defines an interval in which the plant's states must lie at any given instant. We propose a linear programming-based method of interval observer design for two cases: 1) only the initial conditions of the plant are uncertain; 2) the dynamical parameters are also uncertain. In the former, we optimize the transient performance of the interval observers, in the sense that the volume enclosed by the interval is minimized. In the latter, we optimize the steady state performance of the interval observers, in the sense that the norm of the width of the interval is minimized at steady state. Interval observers are typically designed to characterize the widest interval that bounds the states. This thesis proposes an interval observer design method that utilizes additional, but still-incomplete information, that enables the designer to identify tighter bounds on the uncertain parameters under certain operating conditions. The number of bounds that can be refined defines a class of systems. The definition of this class is independent of the specific parameters whose bounds are refined. Applying robust optimization techniques, under a cardinality constrained model of uncertainty, we design a single observer for an entire class of systems. These observers guarantee a minimum level of performance with respect to the aforementioned metrics, as we optimize the worst-case performance over a given class of systems. The robust formulation allows the designer to tune the level of uncertainty in the model. If many of the uncertain parameter bounds can be refined, the nominal performance of the observer can be improved, however, if few or none of the parameter bounds can be refined, the nominal performance of the observer can be designed to be more conservative.
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Book chapters on the topic "Cardinality constrained optimization"

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de Farias, Ismael R., and George L. Nemhauser. "A Polyhedral Study of the Cardinality Constrained Knapsack Problem." In Integer Programming and Combinatorial Optimization. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-47867-1_21.

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Khokhar, Mulazim-Ali, Kris Boudt, and Chunlin Wan. "Cardinality-Constrained Higher-Order Moment Portfolios Using Particle Swarm Optimization." In Applying Particle Swarm Optimization. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70281-6_10.

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Streichert, Felix, Holger Ulmer, and Andreas Zell. "Evolutionary Algorithms and the Cardinality Constrained Portfolio Optimization Problem." In Operations Research Proceedings. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17022-5_33.

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Cui, Min, Donglei Du, Ling Gai, and Ruiqi Yang. "A Linear-Time Streaming Algorithm for Cardinality-Constrained Maximizing Monotone Non-submodular Set Functions." In Combinatorial Optimization and Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92681-6_9.

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Ahmad, Faisal, Faraz Hasan, Mohammad Shahid, Jahangir Chauhan, and Mohammad Imran. "Cardinality Constrained Portfolio Selection Strategy Based on Hybrid Metaheuristic Optimization Algorithm." In Proceedings of International Conference on Data Science and Applications. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6631-6_59.

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Pleshakov, Michael, Sergei Sidorov, and Kirill Spiridonov. "Convergence Analysis of Penalty Decomposition Algorithm for Cardinality Constrained Convex Optimization in Hilbert Spaces." In Mathematical Optimization Theory and Operations Research. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49988-4_10.

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Mayambala, Fred, Elina Rönnberg, and Torbjörn Larsson. "Tight Upper Bounds on the Cardinality Constrained Mean-Variance Portfolio Optimization Problem Using Truncated Eigendecomposition." In Operations Research Proceedings. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28697-6_54.

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Ruiz-Torrubiano, Rubén, Sergio García-Moratilla, and Alberto Suárez. "Optimization Problems with Cardinality Constraints." In Computational Intelligence in Optimization. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12775-5_5.

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Régin, Jean-Charles. "Combination of Among and Cardinality Constraints." In Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11493853_22.

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Kocjan, Waldemar, and Per Kreuger. "Filtering Methods for Symmetric Cardinality Constraint." In Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24664-0_14.

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Conference papers on the topic "Cardinality constrained optimization"

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Friedrich, Tobias, Timo Kötzing, Aishwarya Radhakrishnan, et al. "Crossover for cardinality constrained optimization." In GECCO '22: Genetic and Evolutionary Computation Conference. ACM, 2022. http://dx.doi.org/10.1145/3512290.3528713.

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Cheng, Runze, and Jianjun Gao. "On cardinality constrained mean-CVaR portfolio optimization." In 2015 27th Chinese Control and Decision Conference (CCDC). IEEE, 2015. http://dx.doi.org/10.1109/ccdc.2015.7162076.

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Gomez, Miguel A., Carmen X. Flores, and Maria A. Osorio. "Hybrid search for cardinality constrained portfolio optimization." In the 8th annual conference. ACM Press, 2006. http://dx.doi.org/10.1145/1143997.1144302.

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Parizy, Matthieu, Przemyslaw Sadowski, and Nozomu Togawa. "Cardinality Constrained Portfolio Optimization on an Ising Machine." In 2022 IEEE 35th International System-on-Chip Conference (SOCC). IEEE, 2022. http://dx.doi.org/10.1109/socc56010.2022.9908082.

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Zhang, Jize, Tim Leung, and Aleksandr Aravkin. "A Relaxed Optimization Approach for Cardinality-Constrained Portfolios." In 2019 18th European Control Conference (ECC). IEEE, 2019. http://dx.doi.org/10.23919/ecc.2019.8796164.

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Cui, Tianxiang, Shi Cheng, and Ruibin Bai. "A combinatorial algorithm for the cardinality constrained portfolio optimization problem." In 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014. http://dx.doi.org/10.1109/cec.2014.6900357.

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McCarthy, Philip James, Christopher Nielsen, and Stephen L. Smith. "Cardinality constrained robust optimization applied to a class of interval observers." In 2014 American Control Conference - ACC 2014. IEEE, 2014. http://dx.doi.org/10.1109/acc.2014.6859149.

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Chen, Angela H. L., Yun-Chia Liang, and Chia-Chien Liu. "An artificial bee colony algorithm for the cardinality-constrained portfolio optimization problems." In 2012 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2012. http://dx.doi.org/10.1109/cec.2012.6252920.

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Suthiwong, Dit, and Maleerat Sodanil. "Cardinality-constrained Portfolio optimization using an improved quick Artificial Bee Colony Algorithm." In 2016 International Computer Science and Engineering Conference (ICSEC). IEEE, 2016. http://dx.doi.org/10.1109/icsec.2016.7859943.

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Min, Jiang, Zhiqing Meng, Gengui Zhou, and Rui Shen. "A Smoothing Penalty Function Algorithm for Two-Cardinality Sparse Constrained Optimization Problems." In 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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