321. The improvement function in branch-and-bound methods
Invited abstract in session TB-13: Numerical Methods and Applications I, stream Numerical Methods and Applications.
Tuesday, 10:30-12:30Room: B100/6009
Authors (first author is the speaker)
| 1. | Stefan Schwarze
|
| Institute of Operations Research, Karlsruhe Institute of Technology | |
| 2. | Oliver Stein
|
| Institute of Operations Research, Karlsruhe Institute of Technology | |
| 3. | Peter Kirst
|
| Wageningen University & Research | |
| 4. | Marc Rodestock
|
| Institute of Operations Research (IOR), Karlsruhe Institute of Technology (KIT) |
Abstract
We present a new spatial branch-and-bound approach for treating optimization problems with nonconvex inequality constraints. It is able to approximate the set of all global minimal points in case of solvability, and else to detect unsolvability. The new technique covers the nonconvex constraints by means of an improvement function which, although nonsmooth, can be treated by standard bounding operations.
The method is shown to be successful under a weak regularity condition, and we also give a transparent interpretation of the output in case that this condition is violated. Numerical tests illustrate the performance of the algorithm.
Keywords
- Global optimization
- Linear and nonlinear optimization
- Computational mathematical optimization
Status: accepted
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