A secant algorithm with line search filter method for nonlinear optimization

作者：Gu, Chao^*; Zhu, Detong

来源：Applied Mathematical Modelling, 2011, 35(2): 879-894.

DOI：10.1016/j.apm.2010.07.042

• Line search
• Filter method
• Secant algorithm
• Lagrangian function
• BFGS update
• Convergence
• Maratos effect

Filter methods were initially designed for nonlinear programming problems by Fletcher and Leyffer. In this paper we propose a secant algorithm with line search filter method for nonlinear equality constrained optimization. The algorithm yields the global convergence under some reasonable conditions. By using the Lagrangian function value in the filter we establish that the proposed algorithm can overcome the Maratos effect without using second order correction step, so that fast local superlinear convergence to second order sufficient local solution is achieved. The primary numerical results are presented to confirm the robustness and efficiency of our approach.

• 出版日期2011-2
• 单位上海师范大学; 上海立信会计金融学院