A trust region method with a conic model for nonlinearly constrained optimization

2006 ◽  
Vol 21 (3) ◽  
pp. 263-275 ◽  
Author(s):  
Wang Chengjing
Keyword(s):  
Constrained Optimization ◽  
Trust Region Method ◽  
Trust Region ◽  
Conic Model ◽  
Region Method ◽  
Nonlinearly Constrained Optimization

scholarly journals A Nonmonotone Adaptive Trust Region Method Based on Conic Model for Unconstrained Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Zhaocheng Cui
Keyword(s):  
Unconstrained Optimization ◽  
Optimization Problems ◽  
Trust Region Method ◽  
Trust Region ◽  
New Method ◽  
Convergence Properties ◽  
Initial Trust ◽  
Conic Model ◽  
Region Method ◽  
Unconstrained Optimization Problems

We propose a nonmonotone adaptive trust region method for unconstrained optimization problems which combines a conic model and a new update rule for adjusting the trust region radius. Unlike the traditional adaptive trust region methods, the subproblem of the new method is the conic minimization subproblem. Moreover, at each iteration, we use the last and the current iterative information to define a suitable initial trust region radius. The global and superlinear convergence properties of the proposed method are established under reasonable conditions. Numerical results show that the new method is efficient and attractive for unconstrained optimization problems.

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.

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