Search Results - Box constrained nonlinear integer programming~
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Source: Journal of the Korean Mathematical Society. 48:985-999
Subject Terms: 0209 industrial biotechnology, 0211 other engineering and technologies, 02 engineering and technology
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Source: Journal of Computational and Applied Mathematics. 223:356-373
Subject Terms: discrete local minimizer, 0209 industrial biotechnology, convergence, algorithm, Applied Mathematics, 0211 other engineering and technologies, Integer programming, convexized method, 02 engineering and technology, Convexized method, Discrete local minimizer, box constrained nonlinear integer programming, Computational Mathematics, Numerical mathematical programming methods, Nonlinear programming, Box constrained nonlinear integer programming, Convergence
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Source: Digital Chemical Engineering, Vol 14, Iss , Pp 100218- (2025)
Subject Terms: Data-driven optimization, Integrated planning and scheduling, Bi-level programming, Mixed-integer nonlinear programming, Chemical engineering, TP155-156, Information technology, T58.5-58.64
File Description: electronic resource
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Source: Journal of Industrial & Management Optimization. 4:353-362
Subject Terms: 0209 industrial biotechnology, 0211 other engineering and technologies, 02 engineering and technology
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Source: Computers & Operations Research. Oct2009, Vol. 36 Issue 10, p2723-2728. 6p.
Subject Terms: *LINEAR programming, *INTEGER programming, *ALGORITHMS, *DYNAMIC programming, *MATHEMATICAL programming, *COMPUTER programming
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Source: Bulletin of the South Ural State University. Series "Mathematical Modelling, Programming and Computer Software". 12:130-139
Subject Terms: 0209 industrial biotechnology, нелинейное программирование, целочисленное программирование, nonlinear programming, numerical method, 02 engineering and technology, УДК 519.854.3, оптимизация, 0101 mathematics, численный метод, integer programming, optimization, 01 natural sciences
File Description: application/pdf
Access URL: http://dspace.susu.ru/xmlui/handle/0001.74/40270
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Source: Annals of Operations Research. 1995, Vol. 58 Issue 1-4, p279-293. 15p.
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Index Terms: Global optimization, radial basis functions, response surface model, surrogate model, expensive function, CPU-intensive, optimization software, splines, mixed-integer nonlinear programming, nonconvex, derivative-free, black-box, linear constraints, nonlinear constraints, Computational Mathematics, Beräkningsmatematik, Article in journal, info:eu-repo/semantics/article, text
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http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-77076
Optimization and Engineering, 1389-4420, 2008, 9:3, s. 311-339 -
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Authors: Lobato, Rafael Durbano
Thesis Advisors: Birgin, Ernesto Julian Goldberg
Subject Terms: Augmented Lagrangian, integer variables, Lagrangianos Aumentados, mixed-integer nonlinear programming, nonlinear programming., programação não-linear, programação não-linear inteira mista, variáveis inteiras.
File Description: application/pdf
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Source: IEEE Journal on Selected Areas in Communications; Aug2006, Vol. 24 Issue 8, p1603-1613, 11p, 2 Diagrams, 9 Graphs
Subject Terms: INTEGER programming, ALGORITHMS, LINEAR programming, QUADRATIC programming, MATHEMATICAL programming
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Source: IEEE Transactions on Pattern Analysis & Machine Intelligence; Jul2019, Vol. 41 Issue 7, p1695-1708, 14p
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Source: Water Environment Research, 2019 Apr 01. 91(4), 300-321.
Access URL: https://www.jstor.org/stable/26662541
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Source: IEEE Transactions on Pattern Analysis & Machine Intelligence; Sep2022, Vol. 44 Issue 9, p5225-5242, 18p
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Source: IEEE Transactions on Neural Networks & Learning Systems; Jul2021, Vol. 32 Issue 7, p3005-3019, 15p
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Source: Journal of Environmental Informatics. Jun2019, Vol. 33 Issue 2, p68-81. 14p.
Subject Terms: Ant algorithms, Nonlinear programming, Flow velocity, Methods engineering, Heuristic
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Authors: Rafael Durbano Lobato
Thesis Advisors: Ernesto Julian Goldberg Birgin, Francisco de Assis Magalhães Gomes Neto, Marcelo Gomes de Queiroz
Source: Biblioteca Digital de Teses e Dissertações da USPUniversidade de São PauloUSP.
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Subject Terms: Binary integer linear programming, Maximum observability, Phasor measurement unit, State Estimation
File Description: application/pdf
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Optimal pmu placement for power system observability using bica, considering measurement redundancy. Electric Power Systems Research, 103:78–85, 2013. [22] Soumesh Chatterjee, Pronob K. Ghosh, and Biman K. Saha Roy. Pmu-based power system component monitoring scheme satisfying complete observability with multicriteria decision support. International Transactions on Electrical Energy Systems, 30(2):e12223, 2020. [23] Chawasak Rakpenthai, Suttichai Premrudeepreechacharn, Sermsak Uatrongjit, and Neville R. Watson. An optimal pmu placement method against measurement loss and branch outage. IEEE Transactions on Power Delivery, 22(1):101–107, 2007. [24] Rohit Babu, Saurav Raj, Joddumahanthi Vijaychandra, and Bugatha Ram Vara Prasad. Allocation of phasor measurement unit using an admissible searching-based algorithm a-star and binary search tree for full interconnected power network observability. Optimal Control Applications and Methods, 43(3):687–710, 2021. [25] Rohit Babu and Biplab Bhattacharyya. Allocation of phasor measurement unit using a-star method in connected power network. In 2015 IEEE Workshop on Computational Intelligence: Theories, Applications and Future Directions (WCI), pages 1–6, Kanpur, India, 2015. [26] Nikolaos P. Theodorakatos, Miltiadis Lytras, and Rohit Babu. Towards smart energy grids: A box-constrained nonlinear underdetermined model for power system observability using recursive quadratic programming. Energies, 13(7), 2020. [27] Rohit Babu, Saurav Raj, Bishwajit Dey, and Biplab Bhattacharyya. Modified branch-and-bound algorithm for unravelling optimal pmu placement problem for power grid observability: A comparative analysis. CAAI Transactions on Intelligence Technology, 6(4):450–470, 2021. [28] Joel E. Anderson and Aranya Chakrabortty. Pmu placement for dynamic equivalencing of power systems under flow observability constraints. Electric Power Systems Research, 106:51–61, 2014. [29] Yoshiaki Matsukawa, Masayuki Watanabe, Yasunori Mitani, and Mohammad Lutfi Othman. Multi-objective pmu placement optimization considering the placement cost including the current channel allocation and state estimation accuracy. Electrical Engineering in Japan, 207(2):20–27, 2019. [30] Ranjana Sodhi, SC Srivastava, and SN Singh. Multi-criteria decision-making approach for multi-stage optimal placement of phasor measurement units. IET Generation, Transmission & Distribution, 5(2):181–190, 2011. [31] Nikolaos P. Theodorakatos. Optimal phasor measurement unit placement for numerical observability using a two-phase branch-and-bound algorithm. International Journal of Emerging Electric Power Systems, 19(3):20170231, 2018. [32] Heloisa H. Müller and Carlos A. Castro. Genetic algorithm-based phasor measurement unit placement method considering observability and security criteria. IET Generation, Transmission & Distribution, 10(1):270–280, 2016. [33] Xin Zhou, Haishun Sun, Cong Zhang, and Qiangsheng Dai. Optimal placement of pmus using adaptive genetic algorithm considering measurement redundancy. International Journal of Reliability, Quality and Safety Engineering, 23(03):1640001, 2016. [34] Z. Miljani´c, I. Djurovi´c, and I. Vujoševi´c. Optimal placement of pmus with limited number of channels. Electric Power Systems Research, 90:93–98, 2012. [35] Zhong-Jie Wang, Shu-Ying Yuan, Xuan Zhao, and Cheng-Chao Lu. Differential evolution-based optimal placement of phase measurement unit considering measurement redundancy. International Journal of Modeling, Simulation, and Scientific Computing, 06(01):1550016, 2015. [36] Chunhua Peng, Huijuan Sun, and Jianfeng Guo. Multi-objective optimal pmu placement using a non-dominated sorting differential evolution algorithm. International Journal of Electrical Power & Energy Systems, 32(8):886–892, 2010. [37] Rohit Babu and Biplab Bhattacharyya. Optimal allocation of phasor measurement unit for full observability of the connected power network. International Journal of Electrical Power & Energy Systems, 79:89–97, 2016. [38] Rohit Babu and Biplab Bhattacharyya. Optimal placement of phasor measurement unit using binary particle swarm optimization in connected power network. In 2015 IEEE UP Section Conference on Electrical Computer and Electronics (UPCON), pages 1–5, Allahabad, India, 2015. [39] Nadia Hanis Abd Rahman and Ahmed Faheem Zobaa. Integrated mutation strategy with modified binary pso algorithm for optimal pmus placement. IEEE Transactions on Industrial Informatics, 13(6):3124–3133, 2017. [40] Nadia Hanis Abd Rahman. Optimal allocation of phasor measurement units using practical constraints in power systems. PhD thesis, Brunel University London, 2017. [41] Tapas Kumar Maji and P. Acharjee. Multiple solutions of optimal pmu placement using exponential binary pso algorithm. 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Source: Operations Research. May/Jun2013, Vol. 61 Issue 3, p745-761. 17p. 1 Black and White Photograph, 6 Charts.
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Source: Computational Optimization & Applications. Jun2022, Vol. 82 Issue 2, p293-327. 35p.
Subject Terms: *NONLINEAR programming, CONSTRAINED optimization, NONSMOOTH optimization
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Source: Mathematical Programming. Feb2023, Vol. 197 Issue 2, p587-619. 33p.
Subject Terms: BIPARTITE graphs, MIXED integer linear programming, NONLINEAR equations
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