295. Mixed-integer linearity in nonlinear optimization
Invited abstract in session MB-6: Nonsmooth optimization: from continuous to discrete Part I, stream Nonsmooth and nonconvex optimization.
Monday, 10:30-12:30Room: B100/7013
Authors (first author is the speaker)
| 1. | Alberto De Marchi
|
| University of the Bundeswehr Munich |
Abstract
Bringing together nonlinear optimization with polyhedral and integrality constraints enables versatile modelling, but poses significant computational challenges. To address these problems, the talk presents an algorithm that is inspired by proximal-gradient methods but replaces the proximal operator with calls to a generic mixed-integer linear solver. The technique computes feasible iterates based on sequential mixed-integer linearization with trust region safeguard. Convergence to critical, possibly suboptimal, feasible points is established for arbitrary starting points. The theoretical and algorithmic developments are corroborated by numerical applications.
Keywords
- Non-smooth optimization
- Nonlinear mixed integer optimization
- Computational mathematical optimization
Status: accepted
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