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Artykuły w czasopismach na temat „Trust region bundle method”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Jie, Shen, and Gao Ya-Li. "Research on the Dual Problem of Trust Region Bundle Method." British Journal of Mathematics & Computer Science 22, no. 3 (2017): 1–6. https://doi.org/10.9734/BJMCS/2017/33880.

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With the rapid development of science and technology as well as the cross-integration between the various disciplines, the nonsmooth optimization problem plays an increasingly important role in operational research. In this paper, we use the trust region method to study nonsmooth unconstrained optimization problems. Trust region subproblem is constructed to produce the next iteration point by using feasible set as constraint condition. As the number of iterations increases, the compression principle is used to control the elements in a bundle of information. And then the subproblem is studied

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2

Kiwiel, Krzysztof C. "An Ellipsoid Trust Region Bundle Method for Nonsmooth Convex Minimization." SIAM Journal on Control and Optimization 27, no. 4 (1989): 737–57. http://dx.doi.org/10.1137/0327039.

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3

Shen, Jie, and Ya-Li Gao. "Research on the Dual Problem of Trust Region Bundle Method." British Journal of Mathematics & Computer Science 22, no. 3 (2017): 1–6. http://dx.doi.org/10.9734/bjmcs/2017/33880.

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4

Apkarian, P., D. Noll, and O. Prot. "A Trust Region Spectral Bundle Method for Nonconvex Eigenvalue Optimization." SIAM Journal on Optimization 19, no. 1 (2008): 281–306. http://dx.doi.org/10.1137/060665191.

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5

YIN, Yingjie, Kazuya TAMURA, and Shigeyuki HOSOE. "Optimal Planning of Multi-contacted Grasp by the Bundle Trust Region Method." Transactions of the Institute of Systems, Control and Information Engineers 21, no. 8 (2008): 244–52. http://dx.doi.org/10.5687/iscie.21.244.

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6

Kim, Kibaek, Cosmin G. Petra, and Victor M. Zavala. "An Asynchronous Bundle-Trust-Region Method for Dual Decomposition of Stochastic Mixed-Integer Programming." SIAM Journal on Optimization 29, no. 1 (2019): 318–42. http://dx.doi.org/10.1137/17m1148189.

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7

Tamura, Kazuya, Yingjie Yin, and Shigeyuki Hosoe. "1A1-N-089 Optimal planning of hand grasping based on Bundle Trust Region Method(Robot Hand Mechanism and Grasping Strategy 1,Mega-Integration in Robotics and Mechatronics to Assist Our Daily Lives)." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2005 (2005): 25. http://dx.doi.org/10.1299/jsmermd.2005.25_3.

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8

Rynkiewich, Katharina, Jinal Makhija, Mary Carl Froilan, et al. "Healthcare Worker Experiences Implementing CRE Infection Control Measures at a vSNF—A Qualitative Analysis." Infection Control & Hospital Epidemiology 41, S1 (2020): s244—s245. http://dx.doi.org/10.1017/ice.2020.802.

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Background: During 2017–2019 in the Chicago region, several ventilator-capable skilled nursing facilities (vSNFs) participated in a quality improvement project to control the spread of highly prevalent carbapenem-resistant Enterobacteriaceae (CRE). With guidance from regional project coordinators and public health departments that involved education, assistance with implementation, and adherence monitoring, the facilities implemented a CRE prevention bundle that included a hand hygiene campaign that promoted alcohol-based hand rub, contact precautions (personal protective equipment with glove/

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9

Huang, Sheng, Le Rong, Zhuoqun Jiang, and Yuriy V. Tokovyy. "Inverse Identification of Constituent Elastic Parameters of Ceramic Matrix Composites Based on Macro–Micro Combined Finite Element Model." Aerospace 11, no. 11 (2024): 936. http://dx.doi.org/10.3390/aerospace11110936.

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Accurate material performance parameters are the prerequisite for conducting composite material structural analysis and design. However, the complex multiscale structure of ceramic matrix composites (CMCs) makes it extremely difficult to accurately obtain their mechanical performance parameters. To address this issue, a CMC micro-scale constituents (fiber bundles and matrix) elastic parameter inversion method was proposed based on the integration of macro–micro finite element models. This model was established based on the μCT scan data of a plain-woven CMC tensile specimen using the chemical

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10

Dempe, S., and J. F. Bard. "Bundle Trust-Region Algorithm for Bilinear Bilevel Programming." Journal of Optimization Theory and Applications 110, no. 2 (2001): 265–88. http://dx.doi.org/10.1023/a:1017571111854.

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11

Apkarian, Pierre, Dominikus Noll, and Laleh Ravanbod. "Nonsmooth Bundle Trust-region Algorithm with Applications to Robust Stability." Set-Valued and Variational Analysis 24, no. 1 (2015): 115–48. http://dx.doi.org/10.1007/s11228-015-0352-5.

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12

Monjezi, Najmeh Hoseini. "A Bundle Trust Region Algorithm for Minimizing Locally Lipschitz Functions." SIAM Journal on Optimization 33, no. 1 (2023): 319–37. http://dx.doi.org/10.1137/22m1476125.

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13

Shi, Zhenjun, and Shengquan Wang. "Nonmonotone adaptive trust region method." European Journal of Operational Research 208, no. 1 (2011): 28–36. http://dx.doi.org/10.1016/j.ejor.2010.09.007.

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14

Zhang, Xiangsun. "Trust region method in neural network." Acta Mathematicae Applicatae Sinica 13, no. 4 (1997): 342–52. http://dx.doi.org/10.1007/bf02009542.

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15

Liu, Jianjun, Xiangmin Xu, and Xuehui Cui. "An accelerated nonmonotone trust region method with adaptive trust region for unconstrained optimization." Computational Optimization and Applications 69, no. 1 (2017): 77–97. http://dx.doi.org/10.1007/s10589-017-9941-6.

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16

Jia, Zhongxiao, and Fa Wang. "The Convergence of the Generalized Lanczos Trust-Region Method for the Trust-Region Subproblem." SIAM Journal on Optimization 31, no. 1 (2021): 887–914. http://dx.doi.org/10.1137/19m1279691.

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17

Mohades, M. M., M. H. Kahaei, and H. Mohades. "Haplotype assembly using Riemannian trust-region method." Digital Signal Processing 112 (May 2021): 102999. http://dx.doi.org/10.1016/j.dsp.2021.102999.

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18

Xue, Yanqin, Hongwei Liu, and Zexian Liu. "An improved nonmonotone adaptive trust region method." Applications of Mathematics 64, no. 3 (2019): 335–50. http://dx.doi.org/10.21136/am.2019.0138-18.

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19

Zhang, Yang, Quanming Ji, and Qinghua Zhou. "A New Nonmonotone Adaptive Trust Region Method." Journal of Applied Mathematics and Physics 09, no. 12 (2021): 3102–14. http://dx.doi.org/10.4236/jamp.2021.912202.

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20

Zhang, Lei-Hong, Chungen Shen, and Ren-Cang Li. "On the Generalized Lanczos Trust-Region Method." SIAM Journal on Optimization 27, no. 3 (2017): 2110–42. http://dx.doi.org/10.1137/16m1095056.

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21

Wang, Yang, Xiuqiao Xiang, Shunping Zhou, Zhongwen Luo, and Fang Fang. "Compressed Sensing Based on Trust Region Method." Circuits, Systems, and Signal Processing 36, no. 1 (2016): 202–18. http://dx.doi.org/10.1007/s00034-016-0299-2.

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22

Sagara, Nobuko, and Masao Fukushima. "trust region method for nonsmooth convex optimization." Journal of Industrial & Management Optimization 1, no. 2 (2005): 171–80. http://dx.doi.org/10.3934/jimo.2005.1.171.

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23

Gong, Xing. "A trust region method for microwave tomography." Journal of Electronics (China) 18, no. 2 (2001): 181–84. http://dx.doi.org/10.1007/s11767-001-0025-4.

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24

Birgin, Ernesto G., Emerson V. Castelani, André L. M. Martinez, and J. M. Martínez. "Outer Trust-Region Method for Constrained Optimization." Journal of Optimization Theory and Applications 150, no. 1 (2011): 142–55. http://dx.doi.org/10.1007/s10957-011-9815-5.

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25

Zhou, Qing Hua, Feng Xia Xu, Yan Geng, and Ya Rui Zhang. "A Note on Wedge Trust Region Radius Update." Applied Mechanics and Materials 52-54 (March 2011): 926–31. http://dx.doi.org/10.4028/www.scientific.net/amm.52-54.926.

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Wedge trust region method based on traditional trust region is designed for derivative free optimization problems. This method adds a constraint to the trust region problem, which is called “wedge method”. The problem is that the updating strategy of wedge trust region radius is somewhat simple. In this paper, we develop and combine a new radius updating rule with this method. For most test problems, the number of function evaluations is reduced significantly. The experiments demonstrate the effectiveness of the improvement through our algorithm.

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26

Ding, Xianfeng, Quan Qu, and Xinyi Wang. "A modified filter nonmonotone adaptive retrospective trust region method." PLOS ONE 16, no. 6 (2021): e0253016. http://dx.doi.org/10.1371/journal.pone.0253016.

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In this paper, aiming at the unconstrained optimization problem, a new nonmonotone adaptive retrospective trust region line search method is presented, which takes advantages of multidimensional filter technique to increase the acceptance probability of the trial step. The new nonmonotone trust region ratio is presented, which based on the convex combination of nonmonotone trust region ratio and retrospective ratio. The global convergence and the superlinear convergence of the algorithm are shown in the right circumstances. Comparative numerical experiments show the better effective and robust

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27

Zhu, Honglan, Qin Ni, Liwei Zhang, and Weiwei Yang. "A Fractional Trust Region Method for Linear Equality Constrained Optimization." Discrete Dynamics in Nature and Society 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/8676709.

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A quasi-Newton trust region method with a new fractional model for linearly constrained optimization problems is proposed. We delete linear equality constraints by using null space technique. The fractional trust region subproblem is solved by a simple dogleg method. The global convergence of the proposed algorithm is established and proved. Numerical results for test problems show the efficiency of the trust region method with new fractional model. These results give the base of further research on nonlinear optimization.

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28

Grapiglia, Geovani N., and Gabriel F. D. Stella. "An adaptive trust-region method without function evaluations." Computational Optimization and Applications 82, no. 1 (2022): 31–60. http://dx.doi.org/10.1007/s10589-022-00356-0.

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29

Li, Xiaochuan, Ruyue Hou, and Ke Su. "A TRUST REGION FILTER METHOD FOR MINIMAX PROBLEMS." Journal of Mathematical Sciences: Advances and Applications 44 (April 10, 2017): 119–34. http://dx.doi.org/10.18642/jmsaa_7100121786.

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30

Heinkenschloss, Matthias. "A Trust Region Method for Norm Constrained Problems." SIAM Journal on Numerical Analysis 35, no. 4 (1998): 1594–620. http://dx.doi.org/10.1137/s0036142994273987.

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31

Gould, Nick I. M., Caroline Sainvitu, and Philippe L. Toint. "A Filter-Trust-Region Method for Unconstrained Optimization." SIAM Journal on Optimization 16, no. 2 (2005): 341–57. http://dx.doi.org/10.1137/040603851.

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32

Martínez, José Mario. "Discrimination by means of a trust region method." International Journal of Computer Mathematics 55, no. 1-2 (1995): 91–103. http://dx.doi.org/10.1080/00207169508804365.

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33

Babaie-Kafaki, Saman, and Saeed Rezaee. "A randomized adaptive trust region line search method." An International Journal of Optimization and Control: Theories & Applications (IJOCTA) 10, no. 2 (2020): 259–63. http://dx.doi.org/10.11121/ijocta.01.2020.00900.

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Hybridizing the trust region, line search and simulated annealing methods, we develop a heuristic algorithm for solving unconstrained optimization problems. We make some numerical experiments on a set of CUTEr test problems to investigate efficiency of the suggested algorithm. The results show that the algorithm is practically promising.

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34

Liu, Jun. "An improved trust region method for unconstrained optimization." Journal of Vibration and Control 19, no. 5 (2012): 643–48. http://dx.doi.org/10.1177/1077546312436750.

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35

Zhou, QingHua, YaRui Zhang, FengXia Xu, Yan Geng, and XiaoDian Sun. "An improved trust region method for unconstrained optimization." Science China Mathematics 56, no. 2 (2012): 425–34. http://dx.doi.org/10.1007/s11425-012-4507-7.

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36

Bastin, Fabian, Vincent Malmedy, Mélodie Mouffe, Philippe L. Toint, and Dimitri Tomanos. "A retrospective trust-region method for unconstrained optimization." Mathematical Programming 123, no. 2 (2008): 395–418. http://dx.doi.org/10.1007/s10107-008-0258-1.

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37

Huang, Wen, P. A. Absil, and K. A. Gallivan. "A Riemannian symmetric rank-one trust-region method." Mathematical Programming 150, no. 2 (2014): 179–216. http://dx.doi.org/10.1007/s10107-014-0765-1.

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38

Shi, Zhen-Jun, and Jin-Hua Guo. "A new trust region method for unconstrained optimization." Journal of Computational and Applied Mathematics 213, no. 2 (2008): 509–20. http://dx.doi.org/10.1016/j.cam.2007.01.027.

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39

Yuan, Gonglin, Xiwen Lu, and Zengxin Wei. "BFGS trust-region method for symmetric nonlinear equations." Journal of Computational and Applied Mathematics 230, no. 1 (2009): 44–58. http://dx.doi.org/10.1016/j.cam.2008.10.062.

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40

Yang, Y. F. "A new trust region method for nonsmooth equations." ANZIAM Journal 44, no. 4 (2003): 595–607. http://dx.doi.org/10.1017/s1446181100012967.

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AbstractWe propose a new trust region algorithm for solving the system of nonsmooth equations F(x) = 0 by using a smooth function satisfying the Jacobian consistency property to approximate the nonsmooth function F(x). Compared with existing trust region methods for systems of nonsmooth equations, the proposed algorithm possesses some nice convergence properties. Global convergence is established and, in particular, locally superlinear or quadratical convergence is obtained if F is semismooth or strongly semismooth at the solution.

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41

Baker, C. G., P. A. Absil, and K. A. Gallivan. "An implicit trust-region method on Riemannian manifolds." IMA Journal of Numerical Analysis 28, no. 4 (2008): 665–89. http://dx.doi.org/10.1093/imanum/drn029.

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42

Zhang, Ju-liang, and Yong Wang. "A new trust region method for nonlinear equations." Mathematical Methods of Operations Research (ZOR) 58, no. 2 (2003): 283–98. http://dx.doi.org/10.1007/s001860300302.

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43

Lukšan, L., C. Matonoha, and J. Vlček. "Trust-region interior-point method for large sparsel1optimization." Optimization Methods and Software 22, no. 5 (2007): 737–53. http://dx.doi.org/10.1080/10556780601114204.

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44

Rezaee, Saeed, and Saman Babaie-Kafaki. "A modified nonmonotone trust region line search method." Journal of Applied Mathematics and Computing 57, no. 1-2 (2017): 421–36. http://dx.doi.org/10.1007/s12190-017-1113-4.

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45

Yuan, Gonglin, Zengxin Wei, and Xiwen Lu. "A BFGS trust-region method for nonlinear equations." Computing 92, no. 4 (2011): 317–33. http://dx.doi.org/10.1007/s00607-011-0146-z.

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46

Shi, Zhen-Jun, and Jinhua Guo. "A new trust region method with adaptive radius." Computational Optimization and Applications 41, no. 2 (2007): 225–42. http://dx.doi.org/10.1007/s10589-007-9099-8.

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47

Huang, Aiqun, and Chengxian Xu. "A trust region method for solving semidefinite programs." Computational Optimization and Applications 55, no. 1 (2012): 49–71. http://dx.doi.org/10.1007/s10589-012-9514-7.

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48

Sun, Wenyu. "Nonmonotone trust region method for solving optimization problems." Applied Mathematics and Computation 156, no. 1 (2004): 159–74. http://dx.doi.org/10.1016/j.amc.2003.07.008.

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49

Mo, Jiangtao, Kecun Zhang, and Zengxin Wei. "A nonmonotone trust region method for unconstrained optimization." Applied Mathematics and Computation 171, no. 1 (2005): 371–84. http://dx.doi.org/10.1016/j.amc.2005.01.048.

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50

Zhang, Xiangsun, Juliang Zhang, and Lizhi Liao. "An adaptive trust region method and its convergence." Science China Mathematics 45, no. 5 (2002): 620–31. http://dx.doi.org/10.1360/02ys9067.

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