A nonmonotone trust region method based on simple conic models for unconstrained optimization

作者：Zhou Qunyan^*; Zhou Fen; Cao Fengxue

来源：Applied Mathematics and Computation, 2013, 225: 295-305.

DOI：10.1016/j.amc.2013.09.038

• Nonmonotone trust region method
• Simple conic model
• Global convergence
• Unconstrained optimization

A new nonmonotone trust region algorithm with simple conic models for unconstrained optimization is proposed. Compared to traditional conic trust region methods, the new method needs less memory capacitance and computational complexity. The global convergence and fast local convergence rate of the proposed algorithm are established under some reasonable conditions. Numerical tests indicate that the new algorithm is efficient and robust.

• 出版日期2013-12-1
• 单位江苏大学; 河海大学