Manuel Berkemeier

PhD student at Paderborn University. My main research interest lies in multiobjective (non-linear) optimization.

Talks:

20:00 UTC

A Derivative-Free Local Optimizer for Multi-Objective Problems

07/30/2021, 8:00 PM8:30 PM UTC
JuMP Track

In real-world applications, optimization problems might arise where there is more than one objective. Additionally, some objectives could be computationally expensive to evaluate, with no gradient information available. I present a derivative-free local optimizer (written in Julia) aimed at such problems. It employs a trust-region strategy and local surrogate models (e.g., polynomials or radial basis function models) to save function evaluations.

Platinum sponsors

Julia Computing

Gold sponsors

Relational AI

Silver sponsors

Invenia LabsConningPumas AIQuEra Computing Inc.King Abdullah University of Science and TechnologyDataChef.coJeffrey Sarnoff

Media partners

Packt Publication

Fiscal Sponsor

NumFOCUS