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Journal articles on the topic 'Nonlinear optimization'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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1

Moulard, Thomas, Benjamin Chr^|^eacute;tien, and Eiichi Yoshida. "Software Tools for Nonlinear Optimization." Journal of the Robotics Society of Japan 32, no. 6 (2014): 536–41. http://dx.doi.org/10.7210/jrsj.32.536.

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2

YUGE, Kohei, Susumu Ejima, and Junichi ABE. "Nonlinear Optimization." Reference Collection of Annual Meeting VIII.03.1 (2003): 61–62. http://dx.doi.org/10.1299/jsmemecjsm.viii.03.1.0_61.

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3

Salman, Abbas Musleh, and Ahmed Sabah Al-Jilawi. "Combinatorial Optimization and Nonlinear Optimization." Journal of Physics: Conference Series 1818, no. 1 (March 1, 2021): 012134. http://dx.doi.org/10.1088/1742-6596/1818/1/012134.

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4

NASSERI, S. H. "FUZZY NONLINEAR OPTIMIZATION." Journal of Nonlinear Sciences and Applications 01, no. 04 (December 21, 2008): 230–35. http://dx.doi.org/10.22436/jnsa.001.04.05.

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5

Mardle, S., and K. M. Miettinen. "Nonlinear Multiobjective Optimization." Journal of the Operational Research Society 51, no. 2 (February 2000): 246. http://dx.doi.org/10.2307/254267.

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6

Yabe, Hiroshi, and Naoki Sakaiwa. "A NEW NONLINEAR CONJUGATE GRADIENT METHOD FOR UNCONSTRAINED OPTIMIZATION." Journal of the Operations Research Society of Japan 48, no. 4 (2005): 284–96. http://dx.doi.org/10.15807/jorsj.48.284.

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7

AMIR, Hossain M., and Takashi HASEGAWA. "Nonlinear discrete structural optimization." Doboku Gakkai Ronbunshu, no. 392 (1988): 61–71. http://dx.doi.org/10.2208/jscej.1988.392_61.

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8

Pardalos, Panos, and Stephen A. Vavasis. "Nonlinear Optimization: Complexity Issues." Mathematics of Computation 60, no. 201 (January 1993): 440. http://dx.doi.org/10.2307/2153188.

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9

Belotti, Pietro, Christian Kirches, Sven Leyffer, Jeff Linderoth, James Luedtke, and Ashutosh Mahajan. "Mixed-integer nonlinear optimization." Acta Numerica 22 (April 2, 2013): 1–131. http://dx.doi.org/10.1017/s0962492913000032.

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Many optimal decision problems in scientific, engineering, and public sector applications involve both discrete decisions and nonlinear system dynamics that affect the quality of the final design or plan. These decision problems lead to mixed-integer nonlinear programming (MINLP) problems that combine the combinatorial difficulty of optimizing over discrete variable sets with the challenges of handling nonlinear functions. We review models and applications of MINLP, and survey the state of the art in methods for solving this challenging class of problems.Most solution methods for MINLP apply some form of tree search. We distinguish two broad classes of methods: single-tree and multitree methods. We discuss these two classes of methods first in the case where the underlying problem functions are convex. Classical single-tree methods include nonlinear branch-and-bound and branch-and-cut methods, while classical multitree methods include outer approximation and Benders decomposition. The most efficient class of methods for convex MINLP are hybrid methods that combine the strengths of both classes of classical techniques.Non-convex MINLPs pose additional challenges, because they contain non-convex functions in the objective function or the constraints; hence even when the integer variables are relaxed to be continuous, the feasible region is generally non-convex, resulting in many local minima. We discuss a range of approaches for tackling this challenging class of problems, including piecewise linear approximations, generic strategies for obtaining convex relaxations for non-convex functions, spatial branch-and-bound methods, and a small sample of techniques that exploit particular types of non-convex structures to obtain improved convex relaxations.We finish our survey with a brief discussion of three important aspects of MINLP. First, we review heuristic techniques that can obtain good feasible solution in situations where the search-tree has grown too large or we require real-time solutions. Second, we describe an emerging area of mixed-integer optimal control that adds systems of ordinary differential equations to MINLP. Third, we survey the state of the art in software for MINLP.

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10

Levy, Robert, and Huei-Shiang Perng. "Optimization for nonlinear stability." Computers & Structures 30, no. 3 (January 1988): 529–35. http://dx.doi.org/10.1016/0045-7949(88)90286-6.

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11

Hansen, E. R., and G. W. Walster. "Nonlinear equations and optimization." Computers & Mathematics with Applications 25, no. 10-11 (May 1993): 125–45. http://dx.doi.org/10.1016/0898-1221(93)90288-7.

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12

Izmailov, Alexey F., Fernando Lobo Pereira, and Boris S. Mordukhovich. "Nonlinear Analysis and Optimization." Journal of Optimization Theory and Applications 180, no. 1 (November 22, 2018): 1–4. http://dx.doi.org/10.1007/s10957-018-1444-9.

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13

Neghab, Hamed Keshmiri, and Hamid Keshmiri Neghab. "Calibration of a Nonlinear DC Motor under Uncertainty Using Nonlinear Optimization Techniques." Periodica Polytechnica Electrical Engineering and Computer Science 65, no. 1 (January 28, 2021): 42–52. http://dx.doi.org/10.3311/ppee.16165.

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The use of DC motors is increasingly high and it has more parameters which should be normalized. Now the calibration of each parameters is important for each motor, because it affects in its performance and accuracy. A lot of researches are investigated in this area. In this paper demonstrated how to estimate the parameters of a Nonlinear DC Motor using different Nonlinear Optimization techniques of fitting parameters to model, that called model calibration. First, three methods for calibration of a DC motor are defined, then unknown parameters of the mathematical model with the nonlinear optimization techniques for the fitting routines and model calibration process, are identified. In addition, three optimization techniques such as Levenberg-Marquardt, Constrained Nonlinear Optimization and Gauss-Newton, are compared. The goal of this paper is to estimate nonlinear parameters of a DC motor under uncertainty with nonlinear optimization methods by using LabVIEW software as an industrial software and compare the nonlinear optimization methods based on position, velocity and current. Finally, results are illustrated and comparison between these methods based on the results are made.

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14

Liu, Xin. "Subspace Methods for Nonlinear Optimization." CSIAM Transactions on Applied Mathematics 2, no. 4 (June 2021): 585–651. http://dx.doi.org/10.4208/csiam-am.so-2021-0016.

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15

Forsgren, Anders, Philip E. Gill, and Margaret H. Wright. "Interior Methods for Nonlinear Optimization." SIAM Review 44, no. 4 (January 2002): 525–97. http://dx.doi.org/10.1137/s0036144502414942.

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16

Holzapfel, Eduardo A., and Miguel A. Mariño. "Surface‐Irrigation Nonlinear Optimization Models." Journal of Irrigation and Drainage Engineering 113, no. 3 (August 1987): 379–92. http://dx.doi.org/10.1061/(asce)0733-9437(1987)113:3(379).

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17

Holzapfel, Eduardo A., Miguel A. Mariño, and Alejandro Valenzuela. "Drip Irrigation Nonlinear Optimization Model." Journal of Irrigation and Drainage Engineering 116, no. 4 (July 1990): 479–96. http://dx.doi.org/10.1061/(asce)0733-9437(1990)116:4(479).

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18

Wang, S., and J. Kang. "Topology optimization of nonlinear magnetostatics." IEEE Transactions on Magnetics 38, no. 2 (March 2002): 1029–32. http://dx.doi.org/10.1109/20.996264.

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19

Chu, Liang-Ju. "Unified Approaches to Nonlinear Optimization." Optimization 46, no. 1 (January 1999): 25–60. http://dx.doi.org/10.1080/02331939908844443.

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20

Amir, Hossain M., and Takashi Hasegawa. "Nonlinear Mixed‐Discrete Structural Optimization." Journal of Structural Engineering 115, no. 3 (March 1989): 626–46. http://dx.doi.org/10.1061/(asce)0733-9445(1989)115:3(626).

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21

MCLOONE, SEÁN, and GEORGE IRWIN. "Nonlinear optimization of RBF networks." International Journal of Systems Science 29, no. 2 (February 1998): 179–89. http://dx.doi.org/10.1080/00207729808929510.

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22

Guddat, J., and H. TH Jongen. "Structural stability in nonlinear optimization." Optimization 18, no. 5 (January 1987): 617–31. http://dx.doi.org/10.1080/02331938708843275.

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23

HEYNE, GREGOR, MICHAEL KUPPER, and LUDOVIC TANGPI. "PORTFOLIO OPTIMIZATION UNDER NONLINEAR UTILITY." International Journal of Theoretical and Applied Finance 19, no. 05 (July 29, 2016): 1650029. http://dx.doi.org/10.1142/s0219024916500291.

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This paper studies the utility maximization problem of an agent with nontrivial endowment, and whose preferences are modeled by the maximal subsolution of a backward stochastic differential equation (BSDE). We prove existence of an optimal trading strategy and relate our existence result to the existence of a maximal subsolution to a controlled decoupled forward–BSDE (FBSDE). Using BSDE duality, we show that the utility maximization problem can be seen as a robust control problem admitting a saddle point if the generator of the BSDE additionally satisfies a specific growth condition. We show by convex duality that any saddle point of the robust control problem agrees with a primal and a dual optimizer of the utility maximization problem, and can be characterized in terms of a BSDE solution.

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24

Son, Jaeho, Martin Mack, and Kris G. Mattila. "Nonlinear cash flow optimization model." Canadian Journal of Civil Engineering 33, no. 11 (November 1, 2006): 1450–54. http://dx.doi.org/10.1139/l06-086.

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During construction, progress payments (cash inflow) are made periodically to contractors based on work performed. Contractors are required to pay the direct costs (cash outflow) during construction. The net difference between the cash inflow and outflow is the overdraft, which contractors must finance either from the bank or from their own resources. To increase profit margin, contractors consider methods to improve their cash flow, which will increase profit. These methods include front end loading (Stark 1974) and shifting of activities (Easa 1992). These two linear procedures could be done sequentially. However, this sequential linear formulation may not produce an optimized solution because of the nonlinear characteristics of the model. This note examines the combination of the two linear procedures into a single nonlinear formulation such that better profit margin can be achieved.Key words: cost analysis, optimization, linear programming, nonlinear programming.

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25

Wallin, Mathias, and Daniel A. Tortorelli. "Nonlinear homogenization for topology optimization." Mechanics of Materials 145 (June 2020): 103324. http://dx.doi.org/10.1016/j.mechmat.2020.103324.

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26

Alamir, Mazen. "Optimization based nonlinear observers revisited." IFAC Proceedings Volumes 32, no. 2 (July 1999): 2357–62. http://dx.doi.org/10.1016/s1474-6670(17)56400-9.

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27

Migdalas, A., G. Toraldo, and V. Kumar. "Nonlinear optimization and parallel computing." Parallel Computing 29, no. 4 (April 2003): 375–91. http://dx.doi.org/10.1016/s0167-8191(03)00013-9.

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28

Wang, M. Y., S. Zhou, and H. Ding. "Nonlinear diffusions in topology optimization." Structural and Multidisciplinary Optimization 28, no. 4 (July 6, 2004): 262–76. http://dx.doi.org/10.1007/s00158-004-0436-6.

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29

Saouma, Victor E., and Efthimios S. Sikiotis. "Interactive graphics nonlinear constrained optimization." Computers & Structures 21, no. 4 (January 1985): 759–69. http://dx.doi.org/10.1016/0045-7949(85)90152-x.

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30

Liu, Jiakun. "Light reflection is nonlinear optimization." Calculus of Variations and Partial Differential Equations 46, no. 3-4 (February 14, 2012): 861–78. http://dx.doi.org/10.1007/s00526-012-0506-3.

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31

Eskelinen, Petri. "Andrzej P. Ruszczyński: Nonlinear optimization." Mathematical Methods of Operations Research 65, no. 3 (October 12, 2006): 581–82. http://dx.doi.org/10.1007/s00186-006-0116-y.

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32

Cheng, H., V. Rokhlin, and N. Yarvin. "Nonlinear Optimization, Quadrature, and Interpolation." SIAM Journal on Optimization 9, no. 4 (January 1999): 901–23. http://dx.doi.org/10.1137/s1052623498349796.

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33

Jung, Daeyoon, and Hae Chang Gea. "Topology optimization of nonlinear structures." Finite Elements in Analysis and Design 40, no. 11 (July 2004): 1417–27. http://dx.doi.org/10.1016/j.finel.2003.08.011.

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34

Pintér, János D. "Nonlinear optimization with GAMS /LGO." Journal of Global Optimization 38, no. 1 (October 7, 2006): 79–101. http://dx.doi.org/10.1007/s10898-006-9084-2.

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35

Rapcsák, Tamás. "Sectional curvatures in nonlinear optimization." Journal of Global Optimization 40, no. 1-3 (August 3, 2007): 375–88. http://dx.doi.org/10.1007/s10898-007-9212-7.

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36

Hager, William W., and Delphine Mico-Umutesi. "Error estimation in nonlinear optimization." Journal of Global Optimization 59, no. 2-3 (April 9, 2014): 327–41. http://dx.doi.org/10.1007/s10898-014-0186-y.

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37

Birgin, E. G., R. D. Lobato, and J. M. Martínez. "Packing ellipsoids by nonlinear optimization." Journal of Global Optimization 65, no. 4 (December 19, 2015): 709–43. http://dx.doi.org/10.1007/s10898-015-0395-z.

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38

Kanzow, C. "Nonlinear complementarity as unconstrained optimization." Journal of Optimization Theory and Applications 88, no. 1 (January 1996): 139–55. http://dx.doi.org/10.1007/bf02192026.

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39

Betts, J. T., and P. D. Frank. "A sparse nonlinear optimization algorithm." Journal of Optimization Theory and Applications 82, no. 3 (September 1994): 519–41. http://dx.doi.org/10.1007/bf02192216.

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40

Levin, V. I. "Nonlinear optimization under interval uncertainty." Cybernetics and Systems Analysis 35, no. 2 (March 1999): 297–306. http://dx.doi.org/10.1007/bf02733477.

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41

Schwarz, St, R. Kemmler, and E. Ramm. "Structural optimization in nonlinear mechanics." ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik 81, S3 (2001): 695–96. http://dx.doi.org/10.1002/zamm.200108115123.

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42

Weltin, E. E. "Direct optimization of nonlinear parameters." International Journal of Quantum Chemistry 9, S9 (June 18, 2009): 337–41. http://dx.doi.org/10.1002/qua.560090842.

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43

Rapcsák, T. "Geodesic convexity in nonlinear optimization." Journal of Optimization Theory and Applications 69, no. 1 (April 1991): 169–83. http://dx.doi.org/10.1007/bf00940467.

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44

Cui-Cui Cai, Cui-Cui Cai, Mao-Sheng Fu Cui-Cui Cai, Xian-Meng Meng Mao-Sheng Fu, Qi-Jian Wang Xian-Meng Meng, and Yue-Qin Wang Qi-Jian Wang. "Modified Harris Hawks Optimization Algorithm with Multi-strategy for Global Optimization Problem." 電腦學刊 34, no. 6 (December 2023): 091–105. http://dx.doi.org/10.53106/199115992023123406007.

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<p>As a novel metaheuristic algorithm, the Harris Hawks Optimization (HHO) algorithm has excellent search capability. Similar to other metaheuristic algorithms, the HHO algorithm has low convergence accuracy and easily traps in local optimal when dealing with complex optimization problems. A modified Harris Hawks optimization (MHHO) algorithm with multiple strategies is presented to overcome this defect. First, chaotic mapping is used for population initialization to select an appropriate initiation position. Then, a novel nonlinear escape energy update strategy is presented to control the transformation of the algorithm phase. Finally, a nonlinear control strategy is implemented to further improve the algorithm’s efficiency. The experimental results on benchmark functions indicate that the performance of the MHHO algorithm outperforms other algorithms. In addition, to validate the performance of the MHHO algorithm in solving engineering problems, the proposed algorithm is applied to an indoor visible light positioning system, and the results show that the high precision positioning of the MHHO algorithm is obtained.</p> <p> </p>

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45

Kobayashi, Masakazu, Shinji Nishiwaki, and Hiroshi Yamakawa. "Integrated Multi-Step Design Method for Practical and Sophisticated Compliant Mechanisms Combining Topology and Shape Optimizations." Journal of Robotics and Mechatronics 19, no. 2 (April 20, 2007): 141–47. http://dx.doi.org/10.20965/jrm.2007.p0141.

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Compliant mechanisms designed by traditional topology optimization have a linear output response, and it is difficult for traditional methods to implement mechanisms having nonlinear output responses, such as nonlinear deformation or path. To design a compliant mechanism having a specified nonlinear output path, we propose a two-stage design method based on topology and shape optimizations. In the first stage, topology optimization generates an initial conceptual compliant mechanism based on ordinary design conditions, with “additional” constraints used to control the output path in the second stage. In the second stage, an initial model for the shape optimization is created, based on the result of the topology optimization, and additional constraints are replaced by spring elements. The shape optimization is then executed, to generate the detailed shape of the compliant mechanism having the desired output path. At this stage, parameters that represent the outer shape of the compliant mechanism and of spring element properties are used as design variables in the shape optimization. In addition to configuring the specified output path, executing the shape optimization after the topology optimization also makes it possible to consider the stress concentration and large displacement effects. This is an advantage offered by the proposed method, because it is difficult for traditional methods to consider these aspects, due to inherent limitations of topology optimization.

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46

Tseng, Hsuan-Yu, Pao-Hsien Chu, Hao-Chun Lu, and Ming-Jyh Tsai. "Easy Particle Swarm Optimization for Nonlinear Constrained Optimization Problems." IEEE Access 9 (2021): 124757–67. http://dx.doi.org/10.1109/access.2021.3110708.

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47

Berahas, Albert S., Frank E. Curtis, Daniel Robinson, and Baoyu Zhou. "Sequential Quadratic Optimization for Nonlinear Equality Constrained Stochastic Optimization." SIAM Journal on Optimization 31, no. 2 (January 2021): 1352–79. http://dx.doi.org/10.1137/20m1354556.

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48

Curtis, Frank E., Travis C. Johnson, Daniel P. Robinson, and Andreas Wächter. "An Inexact Sequential Quadratic Optimization Algorithm for Nonlinear Optimization." SIAM Journal on Optimization 24, no. 3 (January 2014): 1041–74. http://dx.doi.org/10.1137/130918320.

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49

Khishvand, Mahdi, and Ehsan Khamehchi. "Nonlinear Risk Optimization Approach to Gas Lift Allocation Optimization." Industrial & Engineering Chemistry Research 51, no. 6 (January 30, 2012): 2637–43. http://dx.doi.org/10.1021/ie201336a.

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50

Zhang, Zhuhong. "Immune optimization algorithm for constrained nonlinear multiobjective optimization problems." Applied Soft Computing 7, no. 3 (June 2007): 840–57. http://dx.doi.org/10.1016/j.asoc.2006.02.008.

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