Готові списки джерел за темами / Quasiconvex programming

Добірка наукової літератури з теми "Quasiconvex programming"

Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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Статті в журналах з теми "Quasiconvex programming"

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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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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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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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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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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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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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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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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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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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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Дисертації з теми "Quasiconvex programming"

Zhang, Qianggong. "Robust and large-scale quasiconvex programming in structure-from-motion." Thesis, 2018. http://hdl.handle.net/2440/114269.

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Анотація:

Structure-from-Motion (SfM) is a cornerstone of computer vision. Briefly speaking, SfM is the task of simultaneously estimating the poses of the cameras behind a set of images of a scene, and the 3D coordinates of the points in the scene. Often, the optimisation problems that underpin SfM do not have closed-form solutions, and finding solutions via numerical schemes is necessary. An objective function, which measures the discrepancy of a geometric object (e.g., camera poses, rotations, 3D coordi- nates) with a set of image measurements, is to be minimised. Each image measurement gives rise to an error function. For example, the reprojection error, which measures the distance between an observed image point and the projection of a 3D point onto the image, is a commonly used error function. An influential optimisation paradigm in SfM is the ℓ₀₀ paradigm, where the objective function takes the form of the maximum of all individual error functions (e.g. individual reprojection errors of scene points). The benefit of the ℓ₀₀ paradigm is that the objective function of many SfM optimisation problems become quasiconvex, hence there is a unique minimum in the objective function. The task of formulating and minimising quasiconvex objective functions is called quasiconvex programming. Although tremendous progress in SfM techniques under the ℓ₀₀ paradigm has been made, there are still unsatisfactorily solved problems, specifically, problems associated with large-scale input data and outliers in the data. This thesis describes novel techniques to tackle these problems. A major weakness of the ℓ₀₀ paradigm is its susceptibility to outliers. This thesis improves the robustness of ℓ₀₀ solutions against outliers by employing the least median of squares (LMS) criterion, which amounts to minimising the median error. In the context of triangulation, this thesis proposes a locally convergent robust algorithm underpinned by a novel quasiconvex plane sweep technique. Imposing the LMS criterion achieves significant outlier tolerance, and, at the same time, some properties of quasiconvexity greatly simplify the process of solving the LMS problem. Approximation is a commonly used technique to tackle large-scale input data. This thesis introduces the coreset technique to quasiconvex programming problems. The coreset technique aims find a representative subset of the input data, such that solving the same problem on the subset yields a solution that is within known bound of the optimal solution on the complete input set. In particular, this thesis develops a coreset approximate algorithm to handle large-scale triangulation tasks. Another technique to handle large-scale input data is to break the optimisation into multiple smaller sub-problems. Such a decomposition usually speeds up the overall optimisation process, and alleviates the limitation on memory. This thesis develops a large-scale optimisation algorithm for the known rotation problem (KRot). The proposed method decomposes the original quasiconvex programming problem with potentially hundreds of thousands of parameters into multiple sub-problems with only three parameters each. An efficient solver based on a novel minimum enclosing ball technique is proposed to solve the sub-problems.
Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Computer Science, 2018

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Книги з теми "Quasiconvex programming"

Joaquim António dos Santos Gromicho. Quasiconvex optimization and location theory. Amsterdam: Thesis Publishers, 1995.

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Joaquim António dos Santos Gromicho. Quasiconvex optimization and location theory. Dordrecht: Kluwer, 1997.

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Частини книг з теми "Quasiconvex programming"

dos Santos Gromicho, Jaoquim António. "Quasiconvex Programming." In Quasiconvex Optimization and Location Theory, 125–81. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4613-3326-5_5.

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dos Santos Gromicho, Jaoquim António. "Convex Programming." In Quasiconvex Optimization and Location Theory, 33–78. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4613-3326-5_3.

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Stancu-Minasian, I. M. "Convex, Quasiconvex, Pseudoconvex, Logarithmic Convex, αm-Convex, and Invex Functions." In Fractional Programming, 34–61. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-009-0035-6_3.

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Crouzeix, Jean-Pierre. "Some Properties of Dini-Derivatives of Quasiconvex Functions." In New Trends in Mathematical Programming, 41–57. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2878-1_5.

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Frenk, J. G. B., J. Gromicho, F. Plastria, and S. Zhang. "A deep cut ellipsoid algorithm and quasiconvex programming." In Lecture Notes in Economics and Mathematical Systems, 62–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-46802-5_6.

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Thi Thanh Tuoi, Tran, Truong Tuan Khang, Nguyen Thi Ngoc Anh, and Tran Ngoc Thang. "Fuzzy Portfolio Selection with Flexible Optimization via Quasiconvex Programming." In Intelligent Systems and Networks, 360–68. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3394-3_41.

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"9. Generalizations of Convex Functions: Quasiconvex, Strictly Quasiconvex, and Pseudoconvex Functions." In Nonlinear Programming, 131–50. Society for Industrial and Applied Mathematics, 1994. http://dx.doi.org/10.1137/1.9781611971255.ch9.

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Тези доповідей конференцій з теми "Quasiconvex programming"

Seiler, Peter, and Gary J. Balas. "Quasiconvex sum-of-squares programming." In 2010 49th IEEE Conference on Decision and Control (CDC). IEEE, 2010. http://dx.doi.org/10.1109/cdc.2010.5717672.

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Liu, Wei, Fang Wei Li, Hai Bo Zhang, and Bo Li. "Research on wireless Powered Communication Networks Sum Rate Maximization based on time Reversal OFDM." In 8th International Conference on Signal, Image Processing and Embedded Systems (SIGEM 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.122002.

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Анотація:

This paper studies a wireless power communication network(WPCN) based on orthogonal frequency division multiplexing (OFDM) with time reversal(TR). In this paper, the " Harvest Then Transmit " protocol is adopted, and the transmission time block is divided into three stages, the first stage is for power transmission, the second stage is for TR detection, and the third stage is for information transmission. The energy limited access point (AP) and the terminal node obtain energy from the radiofrequency signal sent by the power beacon (PB) to assist the terminal data transmission. The energy limited AP and the terminal node obtain energy from the radio frequency signal sent by the PB to assist the terminal data transmission. In the TR phase and the wireless information transmission (WIT) phase, the terminal transmits the TR detection signal to the AP using the collected energy, and the AP uses the collected energy to transmit independent signals to a plurality of terminals through OFDM. In order to maximize the sum rate of WPCN, the energy collection time and AP power allocation are jointly optimized. Under the energy causal constraint, the subcarrier allocation, power allocation and time allocation of the whole process are studied, and because of the binary variables involved in the subcarrier allocation, the problem belongs to the mixed integer non-convex programming problem. the problem is transformed into a quasiconvex problem, and then binary search is used to obtain the optimal solution. The simulation results verify the effectiveness of this scheme. The results show that the proposed scheme significantly improves the sum rate of the terminal compared to the reference scheme.

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