5 papers
ML-Guided Primal Heuristics for Mixed Binary Quadratic Programs
Weimin Huang, Natalie M. Isenberg, Ján DrgoÅa +2
Mixed Binary Quadratic Programs (MBQPs) are an important and complex set of problems in combinatorial optimization. As solving large-scale combinatorial optimization problems is ch…
Machine Learning Guided Optimal Transmission Switching to Mitigate Wildfire Ignition Risk
Weimin Huang, Ryan Piansky, Bistra Dilkina +1
To mitigate acute wildfire ignition risks, utilities de-energize power lines in high-risk areas. The Optimal Power Shutoff (OPS) problem optimizes line energization statuses to man…
PILOT: One Physics-Integrated Generation Framework to Unify 2D and 3D Radio Map Construction
Weiming Huang, Hao Sun, Junting Chen
Unified 2D and 3D radio map construction supports network planning, wireless digital twins, and unmanned aerial vehicle (UAV) applications. In urban environments, blockage, reflect…
Domain-Aware Machine Learning for Accelerating MILP-Based Motion Planning with Temporal Logic and Chance Constraints
Junyang Cai, Weimin Huang, Brendan Long +4
Motion-planning problems with temporal-logic or chance constraints are often encoded as mixed-integer linear programs (MILPs). Although these encodings provide rigorous specificati…
Efficient Primal Heuristics for Mixed Binary Quadratic Programs Using Suboptimal Rounding Guidance
Weimin Huang, Natalie M. Isenberg, Jan Drgona +2
Mixed Binary Quadratic Programs (MBQPs) are a class of NP-hard problems that arise in a wide range of applications, including finance, machine learning, and chemical and energy sys…