Relevant bibliographies by topics / Quasi concave functions

Academic literature on the topic 'Quasi concave functions'

Author: Grafiati
Published: 10 March 2023

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Journal articles on the topic "Quasi concave functions"

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Kempner, Yulia, and Ilya Muchnik. "Quasi-concave functions on meet-semilattices." Discrete Applied Mathematics 156, no. 4 (February 2008): 492–99. http://dx.doi.org/10.1016/j.dam.2006.12.005.

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Fabella, Raul V. "Quasi-Concave (Composition) Functions with Nonconcave Argument Functions." International Economic Review 33, no. 2 (May 1992): 473. http://dx.doi.org/10.2307/2526905.

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Kempner, Yulia, and Vadim E. Levit. "Duality between quasi-concave functions and monotone linkage functions." Discrete Mathematics 310, no. 22 (November 2010): 3211–18. http://dx.doi.org/10.1016/j.disc.2009.09.001.

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Colesanti, Andrea, Nico Lombardi, and Lukas Parapatits. "Translation invariant valuations on quasi-concave functions." Studia Mathematica 243, no. 1 (2018): 79–99. http://dx.doi.org/10.4064/sm170323-7-7.

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Richard, Scott F., and William R. Zame. "Proper preferences and quasi-concave utility functions." Journal of Mathematical Economics 15, no. 3 (January 1986): 231–47. http://dx.doi.org/10.1016/0304-4068(86)90012-1.

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Kempner, Y., B. Mirkin, and I. Muchnik. "Monotone linkage clustering and quasi-concave set functions." Applied Mathematics Letters 10, no. 4 (July 1997): 19–24. http://dx.doi.org/10.1016/s0893-9659(97)00053-0.

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Beer, Gerald. "Quasi-concave functions and convex convergence to infinity." Bulletin of the Australian Mathematical Society 60, no. 1 (August 1999): 81–94. http://dx.doi.org/10.1017/s0004972700033359.

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By a convex mode of convergence to infinity 〈Ck〉, we mean a sequence of nonempty closed convex subsets of a normed linear space X such that for each k, Ck+1 ⊆ int Ck and and a sequence 〈xn〉 is X is declared convergent to infinity with respect to 〈Ck〉 provided each Ck contains xn eventually. Positive convergence to infinity with respect to a pointed cone with nonempty interior as well as convergence to infinity in a fixed direction fit within this framework. In this paper we study the representation of convex modes of convergence to infinity by quasi-concave functions and associated remetrizations of the space.
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Anello, Giovanni, Maria B. Donato, and Monica Milasi. "Variational methods for equilibrium problems involving quasi-concave utility functions." Optimization and Engineering 13, no. 2 (June 21, 2011): 169–79. http://dx.doi.org/10.1007/s11081-011-9151-5.

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Fowler, John W., Esma S. Gel, Murat M. Köksalan, Pekka Korhonen, Jon L. Marquis, and Jyrki Wallenius. "Interactive evolutionary multi-objective optimization for quasi-concave preference functions." European Journal of Operational Research 206, no. 2 (October 2010): 417–25. http://dx.doi.org/10.1016/j.ejor.2010.02.027.

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Bobkov, S. G., A. Colesanti, and I. Fragalà. "Quermassintegrals of quasi-concave functions and generalized Prékopa–Leindler inequalities." Manuscripta Mathematica 143, no. 1-2 (April 5, 2013): 131–69. http://dx.doi.org/10.1007/s00229-013-0619-9.

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Dissertations / Theses on the topic "Quasi concave functions"

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Lombardi, Nico. "Theory of valuations on the space of quasi-concave functions." Doctoral thesis, 2019. http://hdl.handle.net/2158/1151532.

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We studied the space of quasi-concave functions, that are positive real-valued functions defined on R^n with any super-level set either a convex body or empty. We studied mainly the theory of valuations defined on the space of quasi-concave functions, i.e. real-valued functionals with some additivity condition. We established characterization theorems for continuous, with respect to a suitable topology, invariant, with respect several groups that act on R^n, valuations.
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Book chapters on the topic "Quasi concave functions"

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Kythe, Prem K. "Quasi-Concave Functions." In Elements of Concave Analysis and Applications, 153–78. Boca Raton, Florida : CRC Press, [2018]: Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315202259-6.

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Kythe, Prem K. "Quasi-Convex Functions." In Elements of Concave Analysis and Applications, 179–96. Boca Raton, Florida : CRC Press, [2018]: Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315202259-7.

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Colesanti, Andrea, and Nico Lombardi. "Valuations on the Space of Quasi-Concave Functions." In Lecture Notes in Mathematics, 71–105. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-45282-1_6.

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Oettli, W. "Decomposition Schemes for Finding Saddle Points of Quasi-Convex-Concave Functions." In Quantitative Methoden in den Wirtschaftswissenschaften, 31–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-74306-1_3.

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Gass, Saul I., and Carl M. Harris. "Quasi-concave function." In Encyclopedia of Operations Research and Management Science, 745. New York, NY: Springer US, 2001. http://dx.doi.org/10.1007/1-4020-0611-x_933.

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Hosoya, Yuhki. "A Characterization of Quasi-concave Function in View of the Integrability Theory." In Advances in Mathematical Economics, 135–40. Tokyo: Springer Japan, 2014. http://dx.doi.org/10.1007/978-4-431-54834-8_4.

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Vashist, Akshay, Casimir Kulikowski, and Ilya Muchnik. "Screening for Ortholog Clusters Using Multipartite Graph Clustering by Quasi-Concave Set Function Optimization." In Lecture Notes in Computer Science, 409–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11548706_43.

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Kempner, Yulia, Vadim E., and Ilya Muchnik. "Quasi-Concave Functions and Greedy Algorithms." In Greedy Algorithms. InTech, 2008. http://dx.doi.org/10.5772/6340.

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"Dual Sets of Convex-Concave Functions." In Dual Sets of Envelopes and Characteristic Regions of Quasi-Polynomials, 19–50. WORLD SCIENTIFIC, 2009. http://dx.doi.org/10.1142/9789814277280_0003.

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"Quasi-concave Function." In Encyclopedia of Operations Research and Management Science, 1227. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4419-1153-7_200673.

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Conference papers on the topic "Quasi concave functions"

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Maddulapalli, K., S. Azarm, and A. Boyars. "Interactive Product Design Selection With an Implicit Value Function." In ASME 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/detc2002/dac-34080.

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We present an automated method to aid a Decision Maker (DM) in selecting the ‘most preferred’ from a set of design alternatives. The method assumes that the DM’s preferences reflect an implicit value function that is quasi-concave. The method is iterative, using three approaches in sequence to eliminate lower-value alternatives at each trial design. The method is interactive, with the DM stating preferences in the form of attribute tradeoffs at each trial design. We present an approach for finding a new trial design at each iteration. We provide an example, the design selection for a cordless electric drill, to demonstrate the method.
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Maddulapalli, K., and S. Azarm. "Product Design Selection With Variability in Preferences for an Implicit Value Function." In ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/detc2004-57339.

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Many existing selection methods require that the Decision Maker (DM) state his/her preferences precisely. However, the DM may not have enough information about the needs of end users thus causing variability in the preferences. To address this problem, we present a method for selection that accounts for variability in the DM’s preferences. Our method is interactive and iterative and assumes only that the preferences of the DM reflect an implicit value function that is quasi-concave and non-decreasing with respect to attributes. Due to the variability, the DM states his/her preferences with a range for Marginal Rate of Substitution (MRS) between attributes at a series of trial designs. The method uses the range of MRS preferences to eliminate “dominated designs” and find a set of “non-eliminated designs”. We present a heuristic to reduce the set of non-eliminated designs and obtain a set of “potentially optimal designs”. The significance of potentially optimal designs is that only one of these designs will be the most preferred for any subset of the range of MRS preferences. We present a payload design selection example to demonstrate and verify that our method indeed finds the set of potentially optimal designs.
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