A self-adaptive trust region method for extreme ℬ $\mathcal {B}$ -eigenvalues of symmetric tensors

2018 ◽  
Vol 81 (2) ◽  
pp. 407-420 ◽  
Author(s):  
Mingyuan Cao ◽  
Qingdao Huang ◽  
Yueting Yang
Keyword(s):  
Trust Region Method ◽  
Trust Region ◽  
Symmetric Tensors ◽  
Region Method ◽  
Self Adaptive

A new self-adaptive trust region method for unconstrained optimization

2011 ◽  
Vol 18 (9) ◽  
pp. 1303-1309 ◽  
Author(s):  
Zhaocheng Cui ◽  
Boying Wu
Keyword(s):  
Unconstrained Optimization ◽  
Superlinear Convergence ◽  
Optimization Problems ◽  
Trust Region Method ◽  
Trust Region ◽  
Convergence Properties ◽  
Region Method ◽  
Unconstrained Optimization Problems ◽  
Global And Superlinear Convergence ◽  
Self Adaptive

In this paper, we propose a new self-adaptive trust region method for unconstrained optimization problems and develop some convergence properties. In our algorithm, we use the previous and current iterative information to define a suitable trust region radius at each iteration. The global and superlinear convergence properties of the algorithm are established under reasonable assumptions. Preliminary numerical results show that the new method is efficient and attractive for solving unconstrained optimization problems.

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