A filter sequential adaptive cubic regularization algorithm for nonlinear constrained optimization

In this paper, we propose a filter sequential adaptive regularization algorithm using cubics (ARC) for solving nonlinear equality constrained optimization. Similar to sequential quadratic programming methods, an ARC subproblem with linearized constraints is considered to obtain a trial step in each iteration. Composite step methods and reduced Hessian methods are employed to tackle the linearized constraints. As a result, a trial step is decomposed into the sum of a normal step and a tangential step which is computed by a standard ARC subproblem. Then, the new iteration is determined by filter methods and ARC framework. The global convergence of the algorithm is proved under some reasonable assumptions. Preliminary numerical experiments and comparison results are reported.

Automated summary

In this paper, a novel filter sequential adaptive regularization algorithm (ARC) is proposed for solving nonlinear equality constrained optimization. The algorithm employs composite step methods and reduced Hessian methods to handle linearized constraints, and determines the new iteration using ARC framework and filter methods. Experimental results demonstrate the global convergence of the algorithm.