Bilevel Optimization Research Group is a dedicated team of researchers focused on advancing the field of bilevel optimization, with particular emphasis on multiobjective bilevel optimization, engineering applications, bilevel deep learning, and combinatorial bilevel optimization.
The group's primary objective is to develop novel algorithms, methodologies, and tools to tackle complex problems that arise in various domains, including engineering, operations research, and machine learning. They strive to bridge the gap between theory and practice by addressing real-world challenges and developing practical solutions.
The major areas of interest for the group are:
Multiobjective Bilevel Optimization within the context of bilevel optimization. Research on optimizing multiple conflicting objectives at the upper or lower level, considering trade-offs and Pareto optimality.
Engineering applications of bilevel optimization, aiming to develop efficient algorithms and techniques to solve complex engineering problems. To address optimization challenges in areas such as transportation, energy systems, and logistics.
Combinatorial bilevel optimization is a specialized area of interest within the group. To focus on problems that involve discrete decisions and seek to develop algorithms that can handle combinatorial optimization problems at both the upper and lower levels.
Bilevel Optimization + Machine Learning is another prominent aspect of the research group's work. To explore the integration of deep learning techniques within bilevel optimization frameworks. To improve the efficiency and effectiveness of bilevel optimization algorithms.
Decision-making processes where multiple stakeholders and objectives need to be considered, leading to more robust and well-informed solutions.
The Bilevel Optimization Research Group collaborates with academic institutions, industry partners, and other research groups to foster interdisciplinary research and knowledge exchange.
see members