Mathematical optimization deals with maximizing or minimizing an objective function by choosing values from within an allowed feasible set of possible values. Mathematical optimization is often also referred to as mathematical programming or simply as optimization.
Mathematical optimization deals with maximizing or minimizing a real function by choosing values from within an allowed feasible set of possible values. Mathematical optimization is often also referred to as mathematical programming or simply as optimization.
Thus, the study of Mathematical optimization includes formulating the problem (as a set of mathematical equations), and developing several solution techniques. These techniques exploit the underlying structure of the problem. Different optimization algorithms are suited for different types of problems and vary in solution times and computational complexity.
The goal (to be maximized or minimized) is called the "Objective Function." The set of equations that limit the solution space are the "constraints" and the possible solution space is the "feasible region." In some problems, the aim is to just find any acceptable solution, and these are called "constraint satisfaction problems" in which case there is no real objective function to be minimized or maximized.
Broadly, Mathematical Optimization falls under the area of "Applied Mathematics."
