Tootfinder

Heute vor 27 Jahren: Am 11. Juni 1998 löste eine Fehlauslösung des Sprühsystems im #Kernkraftwerk #Belleville eine automatische #Reaktorabschaltung aus. Dabei klemmte ein Steuereleme…

» Angesprochen sind in erster Linie Privatpersonen, die ihren privaten Parkplatz dem Personal von Pflegediensten zur Verfügung stellen, wenn sie ihn selbst nicht brauchen (zum Beispiel wenn sie gerade bei der Arbeit sind). 
Bringen Sie dafür einfach das Plakat gut sichtbar bei Ihrer privaten Parkfläche an und zeigen Sie den Pflegediensten, dass sie hier willkommen sind. «

Ein Parkplatz für die Pflege | Bielefeld

Deploying SICNav in the Field: Safe and Interactive Crowd Navigation using MPC and Bilevel Optimization
Sepehr Samavi, Garvish Bhutani, Florian Shkurti, Angela P. Schoellig
https://arxiv.org/abs/2506.08851

Deploying SICNav in the Field: Safe and Interactive Crowd Navigation using MPC and Bilevel Optimization
Safe and efficient navigation in crowded environments remains a critical challenge for robots that provide a variety of service tasks such as food delivery or autonomous wheelchair mobility. Classical robot crowd navigation methods decouple human motion prediction from robot motion planning, which neglects the closed-loop interactions between humans and robots. This lack of a model for human reactions to the robot plan (e.g. moving out of the way) can cause the robot to get stuck. Our proposed …

This https://arxiv.org/abs/2401.17852 has been replaced.
link: https://scholar.google.com/scholar?q=a

Theoretical smoothing frameworks for nonsmooth simple bilevel problems
Bilevel programming has recently received a great deal of attention due to its abundant applications in many areas. The optimal value function approach provides a useful reformulation of the bilevel problem, but its utility is often limited due to the nonsmoothness of the value function even in cases when the associated lower-level function is smooth. In this paper, we present two smoothing strategies for the value function associated with lower-level functions that are not necessarily smooth b…

Nur damit die ICE-Verbindung von #Bielefeld nach #Hannover ein paar Minuten schneller wird, will die #Bahn die Johannesbachaue zubetonieren – ein wichtiges Naherholungsgebiet und Naturreservat in

CR-BLEA: Contrastive Ranking for Adaptive Resource Allocation in Bilevel Evolutionary Algorithms
Dejun Xu, Jijia Chen, Gary G. Yen, Min Jiang
https://arxiv.org/abs/2506.06362

CR-BLEA: Contrastive Ranking for Adaptive Resource Allocation in Bilevel Evolutionary Algorithms
Bilevel optimization poses a significant computational challenge due to its nested structure, where each upper-level candidate solution requires solving a corresponding lower-level problem. While evolutionary algorithms (EAs) are effective at navigating such complex landscapes, their high resource demands remain a key bottleneck -- particularly the redundant evaluation of numerous unpromising lower-level tasks. Despite recent advances in multitasking and transfer learning, resource waste persis…

Die #LagederNation ist dieses Mal außerordentlich interessant.
#Dobrindt kriegt sein Fett weg, #Linartas im Interview, Polen, Energiewende - guckt selbst ins Inhaltsverzeichnis. 😉
…

LdN434 Bundesregierung ignoriert Gericht, Rechtsaußen neuer Präsident Polens (Kornelia Kończal, Uni Bielefeld), Wie Kommunen Akzeptanz von Windrädern steigern, Ungleichheit gefährdet Demokratie (Martyna Linartas, Autorin), Korrektur: Bundeswehr und Google-Cloud

This https://arxiv.org/abs/2412.06436 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

An Adaptively Inexact Method for Bilevel Learning Using Primal-Dual Style Differentiation
We consider a bilevel learning framework for learning linear operators. In this framework, the learnable parameters are optimized via a loss function that also depends on the minimizer of a convex optimization problem (denoted lower-level problem). We utilize an iterative algorithm called `piggyback' to compute the gradient of the loss and minimizer of the lower-level problem. Given that the lower-level problem is solved numerically, the loss function and thus its gradient can only be computed …

Bilevel Optimization for Improved Flexibility Aggregation Models of Electric Vehicle Fleets
Philipp H\"artel, Michael von Bonin
https://arxiv.org/abs/2506.04843

Bilevel Optimization for Improved Flexibility Aggregation Models of Electric Vehicle Fleets
Electric vehicle (EV) fleets are expected to become an increasingly important source of flexibility for power system operations. However, accurately capturing the flexibility potential of numerous and heterogeneous EVs remains a significant challenge. We propose a bilevel optimization formulation to enhance flexibility aggregations of electric vehicle fleets. The outer level minimizes scheduling deviations between the aggregated and reference EV units, while the inner level maximizes the aggreg…

Set Smoothness Unlocks Clarke Hyper-stationarity in Bilevel Optimization
He Chen, Jiajin Li, Anthony Man-cho So
https://arxiv.org/abs/2506.04587 https://…

Set Smoothness Unlocks Clarke Hyper-stationarity in Bilevel Optimization
Solving bilevel optimization (BLO) problems to global optimality is generally intractable. A common alternative is to compute a hyper-stationary point -- a stationary point of the hyper-objective function formed by minimizing/maximizing the upper-level function over the lower-level solution set. However, existing approaches either yield weak notions of stationarity or rely on restrictive assumptions to ensure the smoothness of hyper-objective functions. In this paper, we remove these impractica…