Feasible Solution

Finding feasible solutions—optimal or near-optimal solutions that satisfy all constraints—is a central challenge across diverse optimization problems, from integer programming and job scheduling to causal inference and AI-driven decision-making. Current research focuses on developing and improving algorithms, including machine learning models like neural networks and diffusion models, and leveraging techniques such as double machine learning and answer set programming to efficiently explore solution spaces and generate high-quality feasible solutions. These advancements have significant implications for various fields, improving the efficiency and effectiveness of complex systems and enabling more robust and reliable decision-making in areas ranging from autonomous systems to resource allocation.