An SQP feasible descent algorithm for nonlinear inequality constrained optimization without strict complementarity

作者：Jian, JB^*; Tang, CM

来源：Computers & Mathematics with Applications, 2005, 49(2-3): 223-238.

DOI：10.1016/j.camwa.2004.09.004

• nonlinear inequality
• constrained optimization
• SQP
• feasible descent algorith
• superlinear convergence

In this paper, a kind of nonlinear optimization problems with nonlinear inequality constraints are discussed, and a new SQP feasible descent algorithm for solving the problems is presented. At each iteration of the new algorithm, a convex quadratic program (QP) which always has feasible solution is solved and a master direction is obtained, then, an improved (feasible descent) direction is yielded by updating the master direction with an explicit formula, and in order to avoid the Maxatos effect, a height-order correction direction is computed by another explicit formula of the master direction and the improved direction. The new algorithm is proved to be globally convergent and superlinearly convergent under mild conditions without the strict complementarity. Furthermore, the quadratic convergence rate of the algorithm is obtained when the twice derivatives of the objective function and constrained functions are adopted. Finally, some numerical tests are reported.

• 出版日期2005-2
• 单位广西大学