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• W2095331394 endingPage "964" @default.
• W2095331394 startingPage "934" @default.
• W2095331394 abstract "In this paper, we propose an augmented Lagrangian affine scaling (ALAS) algorithm for general nonlinear programming, for which a quadratic approximation to the augmented Lagrangian is minimized at each iteration. Different from the classical sequential quadratic programming (SQP), the linearization of nonlinear constraints is put into the penalty term of this quadratic approximation, which results in smooth objective of the subproblem and avoids possible inconsistency among the linearized constraints and trust region constraint. By applying affine scaling techniques to handle the strict bound constraints, an active set type affine scaling trust region subproblem is proposed. Through special strategies of updating Lagrange multipliers and adjusting penalty parameters, this subproblem is able to reduce the objective function value and feasibility errors in an adaptive well-balanced way. Global convergence of the ALAS algorithm is established under mild assumptions. Furthermore, boundedness of the penalty parameter is analyzed under certain conditions. Preliminary numerical experiments of ALAS are performed for solving a set of general constrained nonlinear optimization problems in CUTEr problem library." @default.
• W2095331394 created "2016-06-24" @default.
• W2095331394 creator A5072377630 @default.
• W2095331394 creator A5090268268 @default.
• W2095331394 date "2015-04-07" @default.
• W2095331394 modified "2023-09-27" @default.
• W2095331394 title "An augmented Lagrangian affine scaling method for nonlinear programming" @default.
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• W2095331394 doi "https://doi.org/10.1080/10556788.2015.1004332" @default.
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