Inequality Constraint

Inequality constraints represent limitations or restrictions within optimization problems, aiming to find optimal solutions while satisfying these constraints. Current research focuses on developing efficient algorithms, such as penalty-based methods, primal-dual approaches, and differentiable combinatorial frameworks, to handle these constraints within various contexts, including scheduling, machine learning, and reinforcement learning. These advancements are crucial for improving the scalability and efficiency of optimization in diverse fields, from resource allocation to the training of complex models, ultimately leading to more robust and reliable solutions. The development of methods that efficiently handle inequality constraints is particularly important in high-dimensional spaces and for problems with a large number of constraints.