Abstract

This lecture offers a tutorial on integrating submodularity with risk-averse optimization. We begin with foundational concepts, using intuitive examples to define submodularity and its critical role in classical maximization problems. To bridge theory and practice, we provide a step-by-step instructional framework for identifying and modeling submodular structures in real-world scenarios. The discussion then moves on to polyhedral analysis in integer programming. These geometric insights provide tools for solving complex, risk-averse submodular optimization problems across diverse operational environments.

Bio.

Hao-Hsiang Wu is an assistant professor in the Department of Management Science at National Yang Ming Chiao Tung University. He received his Ph.D. in Industrial and Systems Engineering from the University of Washington. His research interests lie in polyhedral analysis and exact methods for mixed-integer programming.

All faculty and students are welcome to join.