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• W2023346104 abstract "In this paper, a kind of nonlinear optimization problems with nonlinear inequality constraints are discussed, and a new SQP feasible descent algorithm for solving the problems is presented. At each iteration of the new algorithm, a convex quadratic program (QP) which always has feasible solution is solved and a master direction is obtained, then, an improved (feasible descent) direction is yielded by updating the master direction with an explicit formula, and in order to avoid the Maratos effect, a height-order correction direction is computed by another explicit formula of the master direction and the improved direction. The new algorithm is proved to be globally convergent and superlinearly convergent under mild conditions without the strict complementarity. Furthermore, the quadratic convergence rate of the algorithm is obtained when the twice derivatives of the objective function and constrained functions are adopted. Finally, some numerical tests are reported." @default.
• W2023346104 created "2016-06-24" @default.
• W2023346104 creator A5034536092 @default.
• W2023346104 creator A5066627199 @default.
• W2023346104 date "2005-01-01" @default.
• W2023346104 modified "2023-10-18" @default.
• W2023346104 title "An SQP feasible descent algorithm for nonlinear inequality constrained optimization without strict complementarity" @default.
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• W2023346104 doi "https://doi.org/10.1016/j.camwa.2004.09.004" @default.
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