Year: 2003
Journal of Computational Mathematics, Vol. 21 (2003), Iss. 2 : pp. 247–256
Abstract
A new algorithm for inequality constrained optimization is presented, which solves a linear programming subproblem and a quadratic subproblem at each iteration. The algorithm can circumvent the difficulties associated with the possible inconsistency of $QP$ subproblem of the original $SQP$ method. Moreover, the algorithm can converge to a point which satisfies a certain first-order necessary condition even if the original problem is itself infeasible. Under certain condition, some global convergence results are proved and local superlinear convergence results are also obtained. Preliminary numerical results are reported.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/2003-JCM-10279
Journal of Computational Mathematics, Vol. 21 (2003), Iss. 2 : pp. 247–256
Published online: 2003-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 10
Keywords: nonlinear optimization SQP method global convergence superlinear convergence.