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- W2360981505 abstract "This paper reviews recent advances in the development of the operational optimization in process system. Successive quadratic programming (SQP) algorithm has been widely used in medium|scale non-linear programming. But when solving large-scale models, some problems has been encountered, such as the storage requirements for the Hessian matrix, the computational expense of solving large quadratic programs, and the combinatorial complexity associated with active set QP methods. The reduced space SQP algorithm decomposes the variables into two part:the independent variables and the dependent variables, and only the independent variables are involved during optimization, which reduces the storage requirements for the Hessian matrix and the computational expense of solving QP. There are three types of the basis for the decomposition:standard orthogonal basis, orthogonal basis and coordinate basis. Mehrotra present a modified interior-point method, which can start from infeasible initial point. The modified interior-point method has been integrated into the reduced SQP algorithm, which leads more efficiency in solving large-scale optimization. The modified barrier function method integrated constraints into the object function as penalty functions. The penalty factors decrease when iterate, so that the object function can converge in the optimal point. Computational experiences show that the penalty factors affect iterations and results very much. The interior-point method and the modified barrier function method have been shown to solve large programs with many non-equal constraints much faster than the active set method." @default.
- W2360981505 created "2016-06-24" @default.
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- W2360981505 date "2000-01-01" @default.
- W2360981505 modified "2023-09-25" @default.
- W2360981505 title "Operational Optimization of Complex Process Industrial System" @default.
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