A superlinearly convergent sequential quadratically constrained quadratic programming algorithm for degenerate nonlinear programming

作     者：Anitescu, M 

作者机构：Univ Pittsburgh Dept Math Pittsburgh PA 15213 USA 

出 版 物：《SIAM JOURNAL ON OPTIMIZATION》 (工业与应用数学会最优化杂志)

年 卷 期：2002年第12卷第4期

页      面：949-978页

核心收录：

学科分类：07[理学] 070104[理学-应用数学] 0701[理学-数学] 

主　　题：sequential quadratic programming degenerate constraints quadratic constraints superlinear convergence 

摘      要：We present an algorithm that achieves superlinear convergence for nonlinear programs satisfying the Mangasarian-Fromovitz constraint qualification and the quadratic growth condition. This convergence result is obtained despite the potential lack of a locally convex augmented Lagrangian. The algorithm solves a succession of subproblems that have quadratic objectives and quadratic constraints, both possibly nonconvex. By the use of a trust-region constraint we guarantee that any stationary point of the subproblem induces superlinear convergence, which avoids the problem of computing a global minimum. We compare this algorithm with sequential quadratic programming algorithms on several degenerate nonlinear programs.