The trust region(TR) method for optimization is a class of effective *** conic model can be regarded as a generalized quadratic model and it possesses the good convergence properties of the quadratic model near the **...
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The trust region(TR) method for optimization is a class of effective *** conic model can be regarded as a generalized quadratic model and it possesses the good convergence properties of the quadratic model near the *** Barzilai and Borwein(BB) gradient method is also an effective method,it can be used for solving large scale optimization problems to avoid the expensive computation and storage of *** addition,the BB stepsize is easy to determine without large computational *** this paper,based on the conic trust region framework,we employ the generalized BB stepsize,and propose a new nonmonotone adaptive trust region method based on simple conic model for large scale unconstrained *** traditional conic model,the Hessian approximation is an scalar matrix based on the generalized BB stepsize,which resulting a simple conic *** adding the nonmonotone technique and adaptive technique to the simple conic model,the new method needs less storage location and converges *** global convergence of the algorithm is established under certain *** results indicate that the new method is effective and attractive for large scale unconstrained optimization problems.
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