Feasibility Guarantee

Feasibility guarantee research focuses on developing methods and algorithms that ensure solutions to complex problems are not only optimal but also practically achievable within given constraints. Current research explores diverse areas, including robust backdoor detection in machine learning, efficient distributed resource allocation algorithms (like those employing signum functions), and generative models (e.g., variational autoencoders) for creating realistic synthetic data for problems like mixed-integer linear programming (MILP). This work is significant because it improves the reliability and efficiency of solutions across various fields, from robotics and resource management to machine learning model training and optimization.