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Journal articles on the topic 'Quasiconvex programming'

To see the other types of publications on this topic, follow the link: Quasiconvex programming.

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Agrawal, Akshay, and Stephen Boyd. "Disciplined quasiconvex programming." Optimization Letters 14, no. 7 (March 2, 2020): 1643–57. http://dx.doi.org/10.1007/s11590-020-01561-8.

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2

Khan, Zulfiqar Ali. "On Nondifferentiable Quasiconvex Programming Problem." Journal of Information and Optimization Sciences 12, no. 1 (January 1991): 57–64. http://dx.doi.org/10.1080/02522667.1991.10699050.

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3

Suzuki, Satoshi. "Duality Theorems for Quasiconvex Programming with a Reverse Quasiconvex Constraint." Taiwanese Journal of Mathematics 21, no. 2 (March 2017): 489–503. http://dx.doi.org/10.11650/tjm/7256.

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4

Fang, Donghui, XianFa Luo, and Xianyun Wang. "Strong and Total Lagrange Dualities for Quasiconvex Programming." Journal of Applied Mathematics 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/453912.

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We consider the strong and total Lagrange dualities for infinite quasiconvex optimization problems. By using the epigraphs of thez-quasi-conjugates and the Greenberg-Pierskalla subdifferential of these functions, we introduce some new constraint qualifications. Under the new constraint qualifications, we provide some necessary and sufficient conditions for infinite quasiconvex optimization problems to have the strong and total Lagrange dualities.

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5

Xu, H. "Level Function Method for Quasiconvex Programming." Journal of Optimization Theory and Applications 108, no. 2 (February 2001): 407–37. http://dx.doi.org/10.1023/a:1026446503110.

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6

Suzuki, Satoshi, and Daishi Kuroiwa. "Set containment characterization for quasiconvex programming." Journal of Global Optimization 45, no. 4 (December 24, 2008): 551–63. http://dx.doi.org/10.1007/s10898-008-9389-4.

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7

Aussel, D., and J. J. Ye. "Quasiconvex programming with locally starshaped constraint region and applications to quasiconvex MPEC." Optimization 55, no. 5-6 (October 2006): 433–57. http://dx.doi.org/10.1080/02331930600808830.

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8

Liu, Xue Wen, and Dou He. "Equivalent Conditions of Generalized Convex Fuzzy Mappings." Scientific World Journal 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/412534.

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We obtain some equivalent conditions of (strictly) pseudoconvex and quasiconvex fuzzy mappings. These results will be useful to present some characterizations of solutions for fuzzy mathematical programming.

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9

Suzuki, Satoshi. "Optimality Conditions and Constraint Qualifications for Quasiconvex Programming." Journal of Optimization Theory and Applications 183, no. 3 (May 17, 2019): 963–76. http://dx.doi.org/10.1007/s10957-019-01534-7.

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10

Hassouni, A. "Quasimonotone Multifunctions; Applications to Optimality Conditions in Quasiconvex Programming." Numerical Functional Analysis and Optimization 13, no. 3-4 (January 1992): 267–75. http://dx.doi.org/10.1080/01630569208816477.

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11

Giorgi, G. "On sufficient optimality conditions for a quasiconvex programming problem." Journal of Optimization Theory and Applications 81, no. 2 (May 1994): 401–5. http://dx.doi.org/10.1007/bf02191671.

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12

Ivanov, Vsevolod I. "Characterizations of Solution Sets of Differentiable Quasiconvex Programming Problems." Journal of Optimization Theory and Applications 181, no. 1 (August 23, 2018): 144–62. http://dx.doi.org/10.1007/s10957-018-1379-1.

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13

Chen, Frank Y., and Y. Feng. "OPTIMIZATION AND OPTIMALITY OF (s,S) STOCHASTIC INVENTORY SYSTEMS WITH NON-QUASICONVEX COSTS." Probability in the Engineering and Informational Sciences 20, no. 2 (March 6, 2006): 287–306. http://dx.doi.org/10.1017/s0269964806060189.

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This article considers the optimization and optimality of single-item/location, infinite-horizon, (s,S) inventory models. Departing from the conventional approach, we do not assume the loss function describing holding and shortage costs per period to be quasiconvex. As the existing optimization algorithms have been established on the condition of quasiconvexity, our goal in this article is to develop a computational procedure for obtaining optimal (s,S) policies for models with general loss functions. Our algorithm is based on the parametric method commonly used in fractional programming and is intuitive, exact, and efficient. Moreover, this method allows us to extend the optimality of (s,S) policies to a broader class of loss functions that can be non-quasiconvex.

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14

Suzuki, Satoshi, and Daishi Kuroiwa. "Optimality conditions and the basic constraint qualification for quasiconvex programming." Nonlinear Analysis: Theory, Methods & Applications 74, no. 4 (February 2011): 1279–85. http://dx.doi.org/10.1016/j.na.2010.09.066.

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15

Bector, C. R., S. Chandra, and M. K. Bector. "Sufficient optimality conditions and duality for a quasiconvex programming problem." Journal of Optimization Theory and Applications 59, no. 2 (November 1988): 209–21. http://dx.doi.org/10.1007/bf00938309.

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16

Suzuki, Satoshi, and Daishi Kuroiwa. "Characterizations of the solution set for non-essentially quasiconvex programming." Optimization Letters 11, no. 8 (September 19, 2016): 1699–712. http://dx.doi.org/10.1007/s11590-016-1084-7.

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17

Suzuki, Satoshi, and Daishi Kuroiwa. "On Set Containment Characterization and Constraint Qualification for Quasiconvex Programming." Journal of Optimization Theory and Applications 149, no. 3 (January 21, 2011): 554–63. http://dx.doi.org/10.1007/s10957-011-9804-8.

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18

Thang, Tran Ngoc, Vijender Kumar Solanki, Tuan Anh Dao, Nguyen Thi Ngoc Anh, and Pham Van Hai. "A monotonic optimization approach for solving strictly quasiconvex multiobjective programming problems." Journal of Intelligent & Fuzzy Systems 38, no. 5 (May 29, 2020): 6053–63. http://dx.doi.org/10.3233/jifs-179690.

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19

Cunha, F. G. M., J. X. da Cruz Neto, and P. R. Oliveira. "A proximal point algorithm with a ϕ-divergence for quasiconvex programming." Optimization 59, no. 5 (July 2010): 777–92. http://dx.doi.org/10.1080/02331930902884273.

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20

Suzuki, Satoshi, and Daishi Kuroiwa. "Necessary and sufficient conditions for some constraint qualifications in quasiconvex programming." Nonlinear Analysis: Theory, Methods & Applications 75, no. 5 (March 2012): 2851–58. http://dx.doi.org/10.1016/j.na.2011.11.025.

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21

Kerdkaew, Jutamas, Rabian Wangkeeree, and Gue Lee. "Approximate optimality for quasi approximate solutions in nonsmooth semi-infinite programming problems, using ε-upper semi-regular semi-convexificators." Filomat 34, no. 6 (2020): 2073–89. http://dx.doi.org/10.2298/fil2006073k.

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In this paper, we study optimality conditions of quasi approximate solutions for nonsmooth semi-infinite programming problems (for short, (SIP)), in terms of ?-upper semi-regular semi-convexificator which is introduced here. Some classes of functions, namely (?-?*?)-pseudoconvex functions and (?-?*?)-quasiconvex functions with respect to a given ?-upper semi-regular semi-convexificator are introduced, respectively. By utilizing these new concepts, sufficient optimality conditions of approximate solutions for the nonsmooth (SIP) are established. Moreover, as an application, optimality conditions of quasi approximate weakly efficient solution for nonsmooth multi-objective semi-infinite programming problems (for short, (MOSIP)) are presented.

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22

Syau, Yu-Ru, and E. Stanley Lee. "Semi-E-Preinvex Functions." International Journal of Artificial Life Research 1, no. 3 (July 2010): 31–39. http://dx.doi.org/10.4018/jalr.2010070103.

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A class of functions called semi--preinvex functions is defined as a generalization of semi--convex functions. Similarly, the concept of semi--quasiconvex functions is also generalized to semi--prequasiinvex functions. Properties of these proposed classes are studied, and sufficient conditions for a nonempty subset of the -dimensional Euclidean space to be an -convex or -invex set are given. The relationship between semi--preinvex and -preinvex functions are discussed along with results for the corresponding nonlinear programming problems.

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23

Ait Mansour, M., and H. Riahi. "Extended radial epiderivatives of non-convex vector-valued maps and parametric quasiconvex programming." Optimization 64, no. 4 (August 25, 2013): 771–97. http://dx.doi.org/10.1080/02331934.2013.811665.

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24

Wantao, Fu, and Zhou Kunping. "Connectedness of the efficient solution sets for a strictly path quasiconvex programming problem." Nonlinear Analysis: Theory, Methods & Applications 21, no. 12 (December 1993): 903–10. http://dx.doi.org/10.1016/0362-546x(93)90115-9.

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25

Brito, Arnaldo S., J. X. da Cruz Neto, Jurandir O. Lopes, and P. Roberto Oliveira. "Interior Proximal Algorithm for Quasiconvex Programming Problems and Variational Inequalities with Linear Constraints." Journal of Optimization Theory and Applications 154, no. 1 (February 15, 2012): 217–34. http://dx.doi.org/10.1007/s10957-012-0002-0.

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26

Suzuki, Satoshi, and Daishi Kuroiwa. "Characterizations of the solution set for quasiconvex programming in terms of Greenberg–Pierskalla subdifferential." Journal of Global Optimization 62, no. 3 (December 21, 2014): 431–41. http://dx.doi.org/10.1007/s10898-014-0255-2.

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27

Ivanov, Vsevolod I. "Second-order optimality conditions and Lagrange multiplier characterizations of the solution set in quasiconvex programming." Optimization 69, no. 4 (June 6, 2019): 637–55. http://dx.doi.org/10.1080/02331934.2019.1625351.

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28

Zhou, Xuan-wei, and Yu-da Hu. "Connectedness of Cone-Efficient Solution Set for Cone-Quasiconvex Multiobjective Programming in Locally Convex Spaces." Acta Mathematicae Applicatae Sinica, English Series 20, no. 2 (June 2004): 309–16. http://dx.doi.org/10.1007/s10255-004-0169-8.

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29

"Strong and total Lagrange dualities for quasiconvex programming." Journal of Nonlinear and Variational Analysis 6, no. 1 (2022). http://dx.doi.org/10.23952/jnva.6.2022.1.01.

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30

Su, Tran Van, and Dinh Dieu Hang. "Second-order optimality conditions for efficiency in $$C^{1,1}$$-smooth quasiconvex multiobjective programming problem." Computational and Applied Mathematics 40, no. 6 (August 26, 2021). http://dx.doi.org/10.1007/s40314-021-01625-0.

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31

Suzuki, Satoshi. "Karush–Kuhn–Tucker type optimality condition for quasiconvex programming in terms of Greenberg–Pierskalla subdifferential." Journal of Global Optimization, July 22, 2020. http://dx.doi.org/10.1007/s10898-020-00926-8.

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