46. Benchmarking Nonlinear Multi-Objective Optimizers in Julia
Invited abstract in session WC-10: Computational Aspects in Multiobjective Optimization, stream Multiobjective and Vector Optimization.
Wednesday, 14:00-16:00Room: B100/8011
Authors (first author is the speaker)
| 1. | Manuel Berkemeier
|
| Computer Science, TU Dortmund University |
Abstract
We present tools and techniques to benchmark solver software for multi-objective optimization. When trying to compare solvers, one is often faced with various difficulties: For example, the solvers might target different problem classes, or their interfaces differ significantly. Additionally, there is (luckily!) no single agreed-upon programming language for research code. We will restrict ourselves to optimizers for nonlinear multi-objective optimization, possibly with constraints, and leverage features of the Julia language to overcome some of the other difficulties. Thanks to shared library loading, we can dynamically use Fortran software that previously required static compilation. It is also possible to dynamically interact with Python or Matlab scripts.
Lastly, we will show the results of benchmarking a list of select solvers on a curated set of test problems, which we provide as a Julia package.
Keywords
- Multi-objective optimization
- Optimization software
Status: accepted
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