A Globally and Superlinearly Convergent SQP Algorithm for Nonlinear Constrained Optimization

Liqun Qi, Y. U Fei Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)


Based on a continuously differentiable exact penalty function and a regularization technique for dealing with the inconsistency of subproblems in the SQP method, we present a new SQP algorithm for nonlinear constrained optimization problems. The proposed algorithm incorporates automatic adjustment rules for the choice of the parameters and makes use of an approximate directional derivative of the merit function to avoid the need to evaluate second order derivatives of the problem functions. Under mild assumptions the algorithm is proved to be globally convergent, and in particular the superlinear convergence rate is established without assuming that the strict complementarity condition at the solution holds. Numerical results reported show that the proposed algorithm is promising.
Original languageEnglish
Pages (from-to)157-184
Number of pages28
JournalJournal of Global Optimization
Issue number2
Publication statusPublished - 1 Oct 2001


  • Constrained optimization
  • Exact penalty function
  • Global convergence
  • SQP method
  • Superlinear convergence

ASJC Scopus subject areas

  • Management Science and Operations Research
  • Global and Planetary Change
  • Applied Mathematics
  • Control and Optimization


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