| A SUPERLINEARLY CONVERGENT SPLITTING FEASIBLE SEQUENTIAL QUADRATIC OPTIMIZATION METHOD FOR TWO-BLOCK LARGE-SCALE SMOOTH OPTIMIZATION |
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| 作者姓名： | 简金宝 张晨 刘鹏杰 |
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| 作者单位： | 1. College of Mathematics and Physics,Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis,Center for Applied Mathematics and Artificial Intelligence,Guangxi Minzu University;2. School of Mechanical Engineering,University of Shanghai for Science and Technology;3. School of Mathematics,China University of Mining and Technology |
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| 基金项目： | supported by the National Natural Science Foundation of China (12171106)；;the Natural Science Foundation of Guangxi Province (2020GXNSFDA238017 and 2018GXNSFFA281007)；;the Shanghai Sailing Program (21YF1430300)； |
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| 摘 要： | This paper discusses the two-block large-scale nonconvex optimization problem with general linear constraints.Based on the ideas of splitting and sequential quadratic optimization(SQO),a new feasible descent method for the discussed problem is proposed.First,we consider the problem of quadratic optimal(QO) approximation associated with the current feasible iteration point,and we split the QO into two small-scale QOs which can be solved in parallel.Second,a feasible descent direction for the prob...
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