Nonlinear optimisation
Building mathematical foundations for data analytics, researching optimality, stability, and robustness of optimisation models.
Nonlinear optimisation theory and algorithms provide a major mathematical foundation for many applications in data science and engineering. CAS and PolyU are working together to enhance mathematical foundations of nonlinear optimisation, which many tasks like predicting, forecasting, learning, reconstructing and controlling problems rely on. Despite advances in machine learning and AI, understanding the optimality, stability, and robustness of these models remains challenging. This collaboration looks to address three critical problems: 1) developing new mathematical optimisation theories; 2) creating efficient algorithms for min-max problems in GANs with non-smooth activation functions; 3) design problem-specific networks for rapid disaster response in smart cities. By addressing these theoretical gaps, the research may enhance the stability, robustness, and performance of optimisation models, advancing the field of data analytics and significantly contributing to applied mathematics, statistics, and machine learning.
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Original project funded for three years from 2022
Key topics
- Nonlinear optimisation theory
- Algorithm development
- Applications in data science
Directors
Xiaojun Chen
Director of University Research Facility in Big Data Analytics at Hong Kong Polytechnic University
Ya-xiang Yuan
Academician at the CAS Institute of Computational Mathematics and Scientific/Engineering Computing, Beijing
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