More test examples for nonlinear programming codes
More test examples for nonlinear programming codes
Testing Unconstrained Optimization Software
ACM Transactions on Mathematical Software (TOMS)
Frame based methods for unconstrained optimization
Journal of Optimization Theory and Applications
On the Convergence of Grid-Based Methods for Unconstrained Optimization
SIAM Journal on Optimization
On the Convergence of Pattern Search Algorithms
SIAM Journal on Optimization
Frames and Grids in Unconstrained and Linearly Constrained Optimization: A Nonsmooth Approach
SIAM Journal on Optimization
Hybrid pattern search and simulated annealing for fuzzy production planning problems
Computers & Mathematics with Applications
Free Pattern Search for global optimization
Applied Soft Computing
| Hi-index | 0.09 |
Recently, an increasing attention was paid on different procedures for an unconstrained optimization problem when the information of the first derivatives is unavailable or unreliable. In this paper, we consider a heuristic iterated-subspace minimization method with pattern search for solving such unconstrained optimization problems. The proposed method is designed to reduce the total number of function evaluations for the implementation of high-dimensional problems. Meanwhile, it keeps the advantages of general pattern search algorithm, i.e., the information of the derivatives is not needed. At each major iteration of such a method, a low-dimensional manifold, the iterated subspace, is constructed. And an approximate minimizer of the objective function in this manifold is then determined by a pattern search method. Numerical results on some classic test examples are given to show the efficiency of the proposed method in comparison with a conventional pattern search method and a derivative-free method.