A NEW FAMILY OF TRUST REGION ALGORITHMS FOR UNCONSTRAINED OPTIMIZATION

• 来源期刊: 计算数学（英文版）
• 关键词: trust region method global convergence quasi-Newton method unconstrainedoptimization nonlinear programming.
• 年，卷（期）: 2003,（2）
• 页码范围: 221-228
• 页数: 8页
• 语种: 英文

摘要

Trust region (TR) algorithms are a class of recently developed algorithms for nonlinear optimization. A new family of TR algorithms for unconstrained optimization, which is the extension of the usual TR method, is presented in this paper. When the objective function is bounded below and continuously. differentiable, and the norm of the Hesse approximations increases at most linearly with the iteration number, we prove the global convergence of the algorithms. Limited numerical results are reported, which indicate that our new TR algorithm is competitive.