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Journal articles on the topic 'Nonlinear optimization'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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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 (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 (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 (2000): 246. http://dx.doi.org/10.2307/254267.

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6

Jovanović, Radiša, and Mitra Vesović. "Control of the servo motor using feedback linearization and artificial gorilla troops optimizer." Nonlinear Analysis: Modelling and Control 30 (March 10, 2025): 1–16. https://doi.org/10.15388/namc.2025.30.39328.

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This paper establishes a nonlinear optimization strategy for position control of a direct current motor. When experimental evidence showed that the linear model does not sufficiently represent the system, the model is modified from linear to nonlinear, using friction-induced nonlinearity. In the course of the research, an analysis of the nonlinear feedback linearizing controller and the up to date gorilla troops optimization algorithm are carried out. The proposed algorithm is juxtapose with four others metaheuristic optimizations. Furthermore, performances with and without different types of

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7

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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8

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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9

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

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10

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 s

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11

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

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12

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

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13

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

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14

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 (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 opti

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15

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 (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

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16

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

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17

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

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18

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

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19

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

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20

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

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21

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

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22

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

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23

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

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24

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

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25

HEYNE, GREGOR, MICHAEL KUPPER, and LUDOVIC TANGPI. "PORTFOLIO OPTIMIZATION UNDER NONLINEAR UTILITY." International Journal of Theoretical and Applied Finance 19, no. 05 (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 b

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26

Son, Jaeho, Martin Mack, and Kris G. Mattila. "Nonlinear cash flow optimization model." Canadian Journal of Civil Engineering 33, no. 11 (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

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27

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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28

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

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29

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

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30

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

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31

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

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32

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

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33

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

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34

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

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35

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

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36

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

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37

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

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38

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

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39

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

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40

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

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41

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

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42

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

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43

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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44

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

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45

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

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46

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 (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 t

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47

C.M., Agu,, Unaegbu, E.N., and Chikwendu, C.R. "Harnessing Karush-Kuhn-Tucker (KKT) as Optimality Conditions for Solving Nonlinear Constrained Optimization Problems." International Journal of Research and Innovation in Applied Science IX, no. VII (2024): 503–11. http://dx.doi.org/10.51584/ijrias.2024.907043.

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This research work succinctly investigated the solution of Nonlinear Optimization Problems using Karush-Kuhn-Tucker (KKT) as optimality condition. The historical development of nonlinear optimization was discussed. The nonlinear constrained optimization problems with inequality constraints were solved with the method of Karush-Kuhn-Tucker (KKT).

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48

Dou, Suguang, B. Scott Strachan, Steven W. Shaw, and Jakob S. Jensen. "Structural optimization for nonlinear dynamic response." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 373, no. 2051 (2015): 20140408. http://dx.doi.org/10.1098/rsta.2014.0408.

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Much is known about the nonlinear resonant response of mechanical systems, but methods for the systematic design of structures that optimize aspects of these responses have received little attention. Progress in this area is particularly important in the area of micro-systems, where nonlinear resonant behaviour is being used for a variety of applications in sensing and signal conditioning. In this work, we describe a computational method that provides a systematic means for manipulating and optimizing features of nonlinear resonant responses of mechanical structures that are described by a sin

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49

Huang, Risheng, Xiaorun Li, Haiqiang Lu, Jing Li, and Liaoying Zhao. "Parameterized Nonlinear Least Squares for Unsupervised Nonlinear Spectral Unmixing." Remote Sensing 11, no. 2 (2019): 148. http://dx.doi.org/10.3390/rs11020148.

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This paper presents a new parameterized nonlinear least squares (PNLS) algorithm for unsupervised nonlinear spectral unmixing (UNSU). The PNLS-based algorithms transform the original optimization problem with respect to the endmembers, abundances, and nonlinearity coefficients estimation into separate alternate parameterized nonlinear least squares problems. Owing to the Sigmoid parameterization, the PNLS-based algorithms are able to thoroughly relax the additional nonnegative constraint and the nonnegative constraint in the original optimization problems, which facilitates finding a solution

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50

Simamora, Rahel, and Sutarman. "Penaksiran Parameter Regresi Nonlinear Menggunakan Particle Swarm Optimization Dan Genetic Algorithm." Leibniz: Jurnal Matematika 4, no. 2 (2024): 71–83. http://dx.doi.org/10.59632/leibniz.v4i02.455.

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Penelitian ini bertujuan untuk mengestimasi dan membandingkan hasil estimasi parameter untuk regresi nonlinier. Metode nonlinier tradisional, yang dikenal sebagai "Regresi Kuadrat Terkecil Nonlinier", digunakan untuk estimasi parameter dalam Model Regresi Nonlinier. Sementara PSO dan GA telah memberikan jaminan untuk optimum global, Metode Gauss-Newton dan Metode Levenberg-Marquadrt masih tidak menjamin konvergensi dan optimum global ketika mengestimasi parameter model regresi nonlinear. Perbedaan nilai fitness yang dihasilkan dari kedua pendekatan inilah yang digunakan untuk menilainya. Algor

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