An affine scaling trust-region approach to bound-constrained nonlinear systems
Applied Numerical Mathematics
CUTEr and SifDec: A constrained and unconstrained testing environment, revisited
ACM Transactions on Mathematical Software (TOMS)
Value functions and error bounds of trust region methods
Journal of Applied Mathematics and Computing
A new trust region method for unconstrained optimization
Journal of Computational and Applied Mathematics
Numerical research on the sensitivity of nonmonotone trust region algorithms to their parameters
Computers & Mathematics with Applications
The convergence of subspace trust region methods
Journal of Computational and Applied Mathematics
A self-adaptive trust region method with line search based on a simple subproblem model
Journal of Computational and Applied Mathematics
A Heuristic for Nonlinear Global Optimization
INFORMS Journal on Computing
A Nonmonotone trust region method with adaptive radius for unconstrained optimization problems
Computers & Mathematics with Applications
Updating the regularization parameter in the adaptive cubic regularization algorithm
Computational Optimization and Applications
A new modified nonmonotone adaptive trust region method for unconstrained optimization
Computational Optimization and Applications
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This paper presents a simple but efficient way to find a good initial trust region radius (ITRR) in trust region methods for nonlinear optimization. The method consists of monitoring the agreement between the model and the objective function along the steepest descent direction, computed at the starting point. Further improvements for the starting point are also derived from the information gleaned during the initializing phase. Numerical results on a large set of problems show the impact the initial trust region radius may have on trust region methods behavior and the usefulness of the proposed strategy.