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A new family of trust region algorithms for unconstrained optimization
查看参考文献10篇
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文摘
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Trust region (TR) algorithms are a class of recently developed algorithms for nonlinear optimization. A new family of TR algorithms for unconstrained optimization, which is the extension of the usual TR method, is presented in this paper. When the objective function is bounded below and continuously differentiable, and the norm of the Hesse approximations increases at most linearly with the iteration number, we prove the global convergence of the algorithms. Limited numerical results are reported, which indicate that our new TR algorithm is competitive. |
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来源
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Journal of Computational Mathematics
,2003,21(2):221-228 【核心库】
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关键词
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trust region method
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global convergence
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quasi-newton method
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unconstrained optimization
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nonlinear programming
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地址
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Institute of Computational Mathematics and Scientific/Engineering Computing, Academy of Mathematics and System Sciences, Chinese Academy of Sciences, State Key Laboratory of Scientific/Engineering Computing, 北京, 100080
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语种
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英文 |
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文献类型
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研究性论文 |
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ISSN
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0254-9409 |
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学科
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数学 |
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基金
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国家自然科学基金
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国家973计划
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High-Performance Software program of China
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香港王宽诚教育基金
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文献收藏号
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CSCD:1574665
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参考文献 共
10
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