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A trust-region algorithm combining line search filter technique for nonlinear constrained optimization. (English) Zbl 1304.49067
Summary: In this paper, we propose a trust-region algorithm in association with line search filter technique for solving nonlinear equality constrained programming. At the current iteration, a trial step is formed as the sum of a normal step and a tangential step which is generated by a trust-region subproblem and the step size is decided by interior backtracking line search together with filter methods. Then, the next iteration is determined. This is different from general trust-region methods in which the next iteration is determined by the ratio of the actual reduction to the predicted reduction. A global convergence analysis for this algorithm is presented under some reasonable assumptions and some preliminary numerical results are reported.

MSC:
49M37 Numerical methods based on nonlinear programming
65K05 Numerical mathematical programming methods
90C30 Nonlinear programming
90C55 Methods of successive quadratic programming type
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