Local convergence of a trust-region algorithm with line search filter technique for nonlinear constrained optimization

2016 ◽  
Vol 273 ◽  
pp. 797-808
Author(s):  
Yonggang Pei ◽  
Detong Zhu
Keyword(s):  
Constrained Optimization ◽  
Local Convergence ◽  
Line Search ◽  
Trust Region ◽  
Nonlinear Constrained Optimization ◽  
Search Filter ◽  
Trust Region Algorithm ◽  
Filter Technique

scholarly journals An Improved Nonmonotone Filter Trust Region Method for Equality Constrained Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Zhong Jin
Keyword(s):  
Global Convergence ◽  
Constrained Optimization ◽  
Trust Region Method ◽  
Trust Region ◽  
Numerical Results ◽  
Trust Region Algorithm ◽  
Filter Technique ◽  
Sqp Methods ◽  
Equality Constrained Optimization ◽  
Nonlinear Equality

Motivated by the method of Su and Pu (2009), we present an improved nonmonotone filter trust region algorithm for solving nonlinear equality constrained optimization. In our algorithm a modified nonmonotone filter technique is proposed and the restoration phase is not needed. At every iteration, in common with the composite-step SQP methods, the step is viewed as the sum of two distinct components, a quasinormal step and a tangential step. A more relaxed accepted condition for trial step is given and a crucial criterion is weakened. Under some suitable conditions, the global convergence is established. In the end, numerical results show our method is effective.

scholarly journals A Derivative-Free Trust Region Algorithm with Nonmonotone Filter Technique for Bound Constrained Optimization

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Jing Gao ◽  
Jian Cao ◽  
Yueting Yang
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Trust Region ◽  
Computational Time ◽  
Test Problems ◽  
Trust Region Algorithm ◽  
Filter Technique ◽  
Derivative Free ◽  
Bound Constrained Optimization ◽  
Parameter Values

We propose a derivative-free trust region algorithm with a nonmonotone filter technique for bound constrained optimization. The derivative-free strategy is applied for special minimization functions in which derivatives are not all available. A nonmonotone filter technique ensures not only the trust region feature but also the global convergence under reasonable assumptions. Numerical experiments demonstrate that the new algorithm is effective for bound constrained optimization. Locally, optimal parameters with respect to overall computational time on a set of test problems are identified. The performance of the best choice of parameter values obtained by the algorithm we presented which differs from traditionally used values indicates that the algorithm proposed in this paper has a certain advantage for the nondifferentiable optimization problems.

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