Martin Schmidt:The Cost of Not Knowing Enough: Mixed-Integer Optimization with Lipschitz Nonlinearities
Many mixed-integer optimization problems are constrained by nonlinear functions that do not possess desirable analytical properties like convexity or factorability or cannot even be evaluated exactly. This is, e.g., the case for many problems constrained by differential equations or for models that rely on black-box simulation runs. For these problem classes, we present, analyze, and test algorithms that solve mixed-integer problems with Lipschitz continuous nonlinearities. Our theoretical results depend on the assumptions made on the (in)exactness of function evaluations and on the knowledge of Lipschitz constants. If Lipschitz constants are known, we prove finite termination at approximate globally optimal points both for the case of exact and inexact function evaluations. If only approximate Lipschitz constants are known, we prove finite termination and derive additional conditions under which infeasibility can be detected. A computational study for gas transport problems and an academic case study show the applicability of our algorithms to real-world problems and how different assumptions on the constraint functions up- or downgrade the practical performance of the methods.
Time: Mon 2022-05-02 11.00 - 12.00
Location: KTH, 3721
Video link: Zoom link, meeting id 63658381373
Participating: Martin Schmidt: Professor at Trier University in Nonlinear Optimization.
His research includes projects, such as, Algorithmic Optimization, MINOA: Mixed-Integer Nonlinear Optimization Algorithms, Decomposition methods for mixed-integer optimal control, Multilevel mixed-integer nonlinear optimization for gas markets, Methods and Models for Energy Transformation and Integration Systems, and Optimization of Gas Transport Networks.