202. Solving vector optimization problems with ADMM
Invited abstract in session WF-4: Multiobjective Optimization I, stream Multiobjective optimization.
Wednesday, 16:20 - 18:00Room: M:M
Authors (first author is the speaker)
| 1. | Daniel Hernandez Escobar
|
| Department of Information Technology, Uppsala University | |
| 2. | Joakim da Silva
|
| Elekta | |
| 3. | Jens Sjölund
|
| Department of Information Technology, Uppsala University |
Abstract
We consider the numerical solution to vector optimization problems. We focus mainly on convex problems whose preference order is defined by a generalized inequality. To approximate the set of efficient solutions, we employ the Alternating Direction Method of Multipliers and a parallel strategy. Although our approach may produce duplicate solutions, it can leverage GPUs or TPUs to achieve fast convergence. We illustrate this by solving multi-objective linear programs. Moreover, we outline how to adapt this technique to solve other problem classes, for instance, when a Lorentz cone defines the generalized inequality.
Keywords
- SS - Multiobjective Optimization
- Multi- and many-objective optimization
Status: accepted
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