Feasibility Constraint

Feasibility constraints represent limitations or restrictions within optimization problems, impacting diverse fields from engineering systems to voting models. Current research focuses on developing efficient algorithms, such as Bayesian neural networks and randomized approaches, to handle these constraints, particularly in scenarios with limited data or complex, high-dimensional spaces. This work is significant because it improves the accuracy and efficiency of solving optimization problems across various applications, leading to better decision-making in areas like energy networks, autonomous vehicle control, and fair collective decision processes. The development of robust and scalable methods for incorporating feasibility constraints is crucial for advancing these fields.