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• W1597988005 abstract "Successive Quadratic Programming (SQP) algorithms have been effective and efficient in solving nonlinearly constrained optimization problems. To guarantee global convergence, however, a line search must be performed after solving the quadratic program. The line search terminates when a step size is found that causes a suitable decrease in some merit function. Because of some problems with previously suggested merit functions, a line search that uses an augmented Lagrangian is proposed. This function follows quite naturally from the derivation of SQP methods and exhibits superior convergence properties compared to an exact penalty function. First, global and local convergence results are presented which are valid for penalty parameters that allow descent directions. We then present an algorithm that chooses penalty parameters that allow good performance. Finally, a numerical study on 15 test problems is presented that compares the proposed line search to existing strategies." @default.
• W1597988005 created "2016-06-24" @default.
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• W1597988005 date "1982-01-01" @default.
• W1597988005 modified "2023-09-27" @default.
• W1597988005 title "Augmented Lagrangian line searches for successive quadratic programming" @default.
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