scholarly journals Corrigendum to “A Fractional Trust Region Method for Linear Equality Constrained Optimization”

2017 ◽  
Vol 2017 ◽  
pp. 1-1
Author(s):  
Honglan Zhu ◽  
Qin Ni ◽  
Liwei Zhang ◽  
Weiwei Yang
Keyword(s):  
Constrained Optimization ◽  
Trust Region Method ◽  
Trust Region ◽  
Region Method ◽  
Linear Equality ◽  
Equality Constrained Optimization

scholarly journals A Fractional Trust Region Method for Linear Equality Constrained Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Honglan Zhu ◽  
Qin Ni ◽  
Liwei Zhang ◽  
Weiwei Yang
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Null Space ◽  
Trust Region Method ◽  
Trust Region ◽  
Test Problems ◽  
Fractional Model ◽  
Region Method ◽  
Linear Equality ◽  
Space Technique

A quasi-Newton trust region method with a new fractional model for linearly constrained optimization problems is proposed. We delete linear equality constraints by using null space technique. The fractional trust region subproblem is solved by a simple dogleg method. The global convergence of the proposed algorithm is established and proved. Numerical results for test problems show the efficiency of the trust region method with new fractional model. These results give the base of further research on nonlinear optimization.

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.

Nonmonotone conic trust region method with line search technique for bound constrained optimization

2019 ◽  
Vol 53 (3) ◽  
pp. 787-805
Author(s):  
Lijuan Zhao
Keyword(s):  
Constrained Optimization ◽  
Line Search ◽  
Optimization Problems ◽  
Trust Region Method ◽  
Trust Region ◽  
Affine Scaling ◽  
Search Technique ◽  
Region Method ◽  
Bound Constrained Optimization ◽  
Line Search Technique

In this paper, we propose a nonmonotone trust region method for bound constrained optimization problems, where the bounds are dealt with by affine scaling technique. Differing from the traditional trust region methods, the subproblem in our algorithm is based on a conic model. Moreover, when the trial point isn’t acceptable by the usual trust region criterion, a line search technique is used to find an acceptable point. This procedure avoids resolving the trust region subproblem, which may reduce the total computational cost. The global convergence and Q-superlinear convergence of the algorithm are established under some mild conditions. Numerical results on a series of standard test problems are reported to show the effectiveness of the new method.

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