A nonmonotone line search filter method with reduced Hessian updating for nonlinear optimization

作者：Gu Chao^*; Zhu Detong

来源：Journal of Systems Science and Complexity, 2013, 26(4): 534-555.

DOI：10.1007/s11424-012-0036-2

• Convergence
• filter method
• lagrangian function
• line search
• maratos effect
• nonmonotone

This paper proposes a nonmonotone line search filter method with reduced Hessian updating for solving nonlinear equality constrained optimization. In order to deal with large scale problems, a reduced Hessian matrix is approximated by BFGS updates. The new method assures global convergence without using a merit function. By Lagrangian function in the filter and nonmonotone scheme, the authors prove that the method can overcome Maratos effect without using second order correction step so that the locally superlinear convergence is achieved. The primary numerical experiments are reported to show effectiveness of the proposed algorithm.

• 出版日期2013-8
• 单位上海立信会计金融学院; 上海师范大学