Oliver Stein
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View article: Granularity for Mixed-Integer Polynomial Optimization Problems
Granularity for Mixed-Integer Polynomial Optimization Problems Open
Finding good feasible points is crucial in mixed-integer programming. For this purpose we combine a sufficient condition for consistency, called granularity, with the moment-/sum-of-squares-hierarchy from polynomial optimization. If the mi…
View article: A tutorial on properties of the epigraph reformulation
A tutorial on properties of the epigraph reformulation Open
This paper systematically surveys useful properties of the epigraph reformulation for optimization problems, and complements them by some new results. We focus on the complete compatibility of the original formulation and the epigraph refo…
View article: Optimal configurations for modular systems at the example of crane bridges
Optimal configurations for modular systems at the example of crane bridges Open
The aim of this paper is to optimize modular systems which cover the construction of products that can be assembled on a modular basis. Increasing the number of different variants of individual components on the one hand decreases the cost…
View article: The standard $L$-function attached to a vector valued modular form
The standard $L$-function attached to a vector valued modular form Open
We define two $L$-functions associated to a common vector valued eigenform $f$ transforming with the ``finite'' Weil representation. The first one can be seen as a standard zeta function defined by the eigenvalues of $f$. The second one ca…
View article: Reinforcement learning-trained optimisers and Bayesian optimisation for online particle accelerator tuning
Reinforcement learning-trained optimisers and Bayesian optimisation for online particle accelerator tuning Open
Online tuning of particle accelerators is a complex optimisation problem that continues to require manual intervention by experienced human operators. Autonomous tuning is a rapidly expanding field of research, where learning-based methods…
View article: On the weakest constraint qualification for sharp local minimizers
On the weakest constraint qualification for sharp local minimizers Open
The sharp local minimality of feasible points of nonlinear optimization problems is known to possess a characterization by a strengthened version of the Karush–Kuhn–Tucker conditions, as long as the Mangasarian–Fromovitz constraint qualifi…
View article: A branch-and-prune algorithm for discrete Nash equilibrium problems
A branch-and-prune algorithm for discrete Nash equilibrium problems Open
We present a branch-and-prune procedure for discrete Nash equilibrium problems with a convex description of each player’s strategy set. The derived pruning criterion does not require player convexity, but only strict convexity of some play…
View article: A converse theorem for Borcherds products and the injectivity of the Kudla-Millson theta lift
A converse theorem for Borcherds products and the injectivity of the Kudla-Millson theta lift Open
We prove a converse theorem for the multiplicative Borcherds lift for lattices of square-free level whose associated discriminant group is anisotropic. This can be seen as generalization of Bruinier's results in \cite{Br2}, which provides …
View article: Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning
Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning Open
Online tuning of real-world plants is a complex optimisation problem that continues to require manual intervention by experienced human operators. Autonomous tuning is a rapidly expanding field of research, where learning-based methods, su…
View article: Biological Feasibility of Spiny Lobster Jasus edwardsii Stock Enhancement
Biological Feasibility of Spiny Lobster Jasus edwardsii Stock Enhancement Open
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View article: Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning
Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning Open
Dataset of optimisation runs performed for a study comparing reinforcement learning and Bayesian optimisation for online continuous tuning at the example of a linear particle accelerator tuning task. Abstract of the Paper on the Study Onli…
View article: Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning
Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning Open
Dataset of optimisation runs performed for a study comparing reinforcement learning and Bayesian optimisation for online continuous tuning at the example of a linear particle accelerator tuning task. Abstract of the Paper on the Study Onli…
View article: 2021 MMOR best paper award
2021 MMOR best paper award Open
The European gas market is organized as a so-called entry-exit system with the main goal to decouple transport and trading.To this end, gas traders and the transmission system operator (TSO) sign so-called booking contracts that grant capa…
View article: Optimisation of manufacturing process parameters for variable component geometries using reinforcement learning
Optimisation of manufacturing process parameters for variable component geometries using reinforcement learning Open
Tailoring manufacturing processes to optimum part quality often requires numerous resource-intensive trial experiments in practice. Physics-based process simulations in combination with general-purpose optimisation algorithms allow for an …
View article: Handling Occlusions in Automated Driving Using a Multiaccess Edge Computing Server-Based Environment Model From Infrastructure Sensors
Handling Occlusions in Automated Driving Using a Multiaccess Edge Computing Server-Based Environment Model From Infrastructure Sensors Open
The on-board sensors’ view of an automated vehicle (AV) can suffer from occlusions by other traffic participants, buildings, or vegetation, especially in urban areas. However, knowledge of possible other traffic participants in the occlude…
View article: 2020 MMOR best paper award
2020 MMOR best paper award Open
In this paper we explore convex reformulation strategies for non-convex quadratically constrained optimization problems (QCQPs).First we investigate such reformulations using Pataki's rank theorem iteratively.We show that the result can be…
View article: A general branch-and-bound framework for continuous global multiobjective optimization
A general branch-and-bound framework for continuous global multiobjective optimization Open
Current generalizations of the central ideas of single-objective branch-and-bound to the multiobjective setting do not seem to follow their train of thought all the way. The present paper complements the various suggestions for generalizat…