A new strategy is presented for large-scale optimization. The BOXSTEP method creates an algorithmic continuum between feasible-directions methods and cutting-plane methods. Several specific applications are described and computational results are reported.
机构:
Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, ShanghaiKey Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai
Tian M.
Chen M.
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Department of Computer Science and Engineering, Southern University of Science and Technology, ShenzhenKey Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai
Chen M.
Du W.
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Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, ShanghaiKey Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai
Du W.
Tang Y.
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机构:
Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, ShanghaiKey Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai
Tang Y.
Jin Y.
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School of Engineering, Westlake University, HangzhouKey Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai
Jin Y.
Yen G.G.
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机构:
School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OKKey Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai