A Nonmonotone Adaptive Trust Region Method Based on Conic Model for Unconstrained Optimization
Keyword(s):
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.
2011 ◽
Vol 18
(9)
◽
pp. 1303-1309
◽
2005 ◽
Vol 163
(1)
◽
pp. 489-504
◽
2005 ◽
Vol 19
(1-2)
◽
pp. 165-177
◽
A New Nonmonotone Adaptive Retrospective Trust Region Method for Unconstrained Optimization Problems
2015 ◽
Vol 167
(2)
◽
pp. 676-692
◽
2009 ◽
Vol 232
(2)
◽
pp. 318-326
◽
2015 ◽
Vol 61
(2)
◽
pp. 321-341
◽
2010 ◽
Vol 60
(3)
◽
pp. 411-422
◽