A nonmonotone line search technique for Newton's method
SIAM Journal on Numerical Analysis
Efficient hybrid conjugate gradient techniques
Journal of Optimization Theory and Applications
On a subproblem of trust region algorithms for constrained optimization
Mathematical Programming: Series A and B
A trust region algorithm for equality constrained optimization
Mathematical Programming: Series A and B
Nonmonotonic trust region algorithm
Journal of Optimization Theory and Applications
An assessment of nonmonotone linesearch techniques for unconstrained optimization
SIAM Journal on Scientific Computing
Testing Unconstrained Optimization Software
ACM Transactions on Mathematical Software (TOMS)
Trust-region methods
On the nonmonotone line search
Journal of Optimization Theory and Applications
Nonmonotone Globalization Techniques for the Barzilai-Borwein Gradient Method
Computational Optimization and Applications
Global convergence of nonmonotone descent methods for unconstrained optimization problems
Journal of Computational and Applied Mathematics - Special issue: Papers presented at the 1st Sino--Japan optimization meeting, 26-28 October 2000, Hong Kong, China
Combination trust-region line-search methods for unconstrained optimization
Combination trust-region line-search methods for unconstrained optimization
A Nonmonotone Line Search Technique and Its Application to Unconstrained Optimization
SIAM Journal on Optimization
Journal of Computational and Applied Mathematics
Incorporating nonmonotone strategies into the trust region method for unconstrained optimization
Computers & Mathematics with Applications
A Nonmonotone trust region method with adaptive radius for unconstrained optimization problems
Computers & Mathematics with Applications
Hi-index | 0.00 |
In this paper, a new nonmonotone inexact line search rule is proposed and applied to the trust region method for unconstrained optimization problems. In our line search rule, the current nonmonotone term is a convex combination of the previous nonmonotone term and the current objective function value, instead of the current objective function value . We can obtain a larger stepsize in each line search procedure and possess nonmonotonicity when incorporating the nonmonotone term into the trust region method. Unlike the traditional trust region method, the algorithm avoids resolving the subproblem if a trial step is not accepted. Under suitable conditions, global convergence is established. Numerical results show that the new method is effective for solving unconstrained optimization problems.