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Constrained Optimization, the constrained optimization problem, is a branch of the optimization problem. It is to find a set of parameter values under a series of constraints to optimize the target value of a certain group or a set of functions. The constraint can be either an equality constraint or an inequality constraint. The key to finding the value of this set of parameters is to satisfy the constraints and target values to be optimal. The methods for solving constrained problems can be divided into traditional methods and evolutionary algorithms.
In mathematical optimization, constrained optimization (called constrained optimization in some contexts) is the process of optimizing the objective function for certain variables in the presence of constraints on these variables. The objective function is the cost function or energy function to be minimized, or the bonus function or utility function to be maximized. A constraint can be a hard constraint that sets conditions for variables that need to be satisfied, or soft constraints, and if and based on the extent to which the condition of the variable is not met, has some variable values that are penalized in the objective function.