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Volume 21, Issue 2
A Robust SQP Method for Optimization with Inequality Constraints

Juliang Zhang & Xiangsun Zhang

J. Comp. Math., 21 (2003), pp. 247-256.

Published online: 2003-04

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  • 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.

  • Keywords

nonlinear optimization, SQP method, global convergence, superlinear convergence.

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COPYRIGHT: © Global Science Press

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@Article{JCM-21-247, author = {Juliang Zhang and Xiangsun Zhang}, title = {A Robust SQP Method for Optimization with Inequality Constraints}, journal = {Journal of Computational Mathematics}, year = {2003}, volume = {21}, number = {2}, pages = {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.

}, issn = {1991-7139}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jcm/10279.html} }
TY - JOUR T1 - A Robust SQP Method for Optimization with Inequality Constraints AU - Juliang Zhang & Xiangsun Zhang JO - Journal of Computational Mathematics VL - 2 SP - 247 EP - 256 PY - 2003 DA - 2003/04 SN - 21 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jcm/10279.html KW - nonlinear optimization, SQP method, global convergence, superlinear convergence. AB -

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.

Juliang Zhang and Xiangsun Zhang. (2003). A Robust SQP Method for Optimization with Inequality Constraints. Journal of Computational Mathematics. 21 (2). 247-256. doi:
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