👉 Supervision math, also known as constraint-based optimization, is a method used in machine learning and control systems to ensure that the solutions generated by an optimization algorithm adhere to specified constraints. These constraints can be equality or inequality conditions that the solution must satisfy, such as bounds on parameters, physical laws, or operational limits. By incorporating these constraints into the optimization process, supervision math helps produce feasible and reliable solutions that not only optimize a given objective function but also respect the underlying system or problem's requirements. This approach is particularly useful in scenarios where violating constraints would lead to infeasible or unsafe outcomes, such as in robotics, aerospace, and financial modeling.
