Abstract
The convergence rate of a rectangular partition based algorithm is considered. A hyper-rectangle for the subdivision is selected at each step according to a criterion rooted in the statistical models based theory of global optimization; only the objective function values are used to compute the criterion of selection. The convergence rate is analyzed assuming that the objective functions are twice- continuously differentiable and defined on the unit cube in d-dimensional Euclidean space. An asymptotic bound on the convergence rate is established. The results of numerical experiments are included.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 165-191 |
| Number of pages | 27 |
| Journal | Journal of Global Optimization |
| Volume | 71 |
| Issue number | 1 |
| DOIs | |
| State | Published - May 1 2018 |
| Externally published | Yes |
All Science Journal Classification (ASJC) codes
- Business, Management and Accounting (miscellaneous)
- Computer Science Applications
- Control and Optimization
- Management Science and Operations Research
- Applied Mathematics
Keywords
- Bayesian approach
- Convergence rate
- Global optimization
- P-algorithm
- Rectangular partition
- Statistical models for global optimization