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Synergizing Neural Network Theory and Combinatorial Optimization via Extension Complexity

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Synergizing Neural Network Theory and Combinatorial Optimization via Extension Complexity Artificial intelligence is changing our lives. Artificial neural networks are present and entering various fields of modern technology such as medicine, engineering, education and many more. Even a small-scale theoretical understanding of why and how neural networks succeed in practice can have a considerable impact on the future development of such technologies. In contrast, combinatorial optimization is a well-established discipline at the intersection of mathematics and computer science, dealing with classical algorithmic questions like the Shortest Path or Traveling Salesperson Problems. A powerful tool to study structural and algorithmic properties of combinatorial optimization problems is polyhedral geometry. For example, the geometric notion of extension complexity classifies how well a specific problem can be expressed and solved via an extremely successful general-purpose techn

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recordType

award

status

TERMINATED

region

EU

value

175920

unit

EUR