Sequential systems of linear equations algorithm for nonlinear optimization problems––general constrained problems

摘要

In Ref. [J. Comput. Math. 20 (3) (2002) 301], a new superlinearly convergent algorithm of sequential systems of linear equations for nonlinear optimization problems with inequality constraints was proposed. Since the new algorithm only needs to solve four systems of linear equations having a same coefficient matrix per iteration, the computation amount of the algorithm is much less than that of the existing sequential quadratic programming algorithms per iteration. Under some mild assumptions, the new algorithm is globally convergent and its rate of convergence is one-step superlinearly. In this paper, it is shown that the new algorithm also can be used to deal with nonlinear optimization problems having nonlinearly equality and inequality constraints, by solving an auxiliary problem. Some numerical results are reported.