Linear Programming is a type of mathematical programming problem where the objective function and constraints are linear.

In Integer Programming, the decision variables are restricted to be discrete.

The Simplex Method is a common algorithm used to solve Linear Programming problems.

In Nonlinear Programming, the objective function and/or constraints are nonlinear.

Sequential Quadratic Programming is a common algorithm used to solve Nonlinear Programming problems.

Dynamic Programming is a technique used to solve multi-stage decision problems.

In a mathematical programming problem, the objective function represents the goal to be achieved.

Linear Constraints, Nonlinear Constraints, and Integer Constraints are all common types of constraints in mathematical programming problems.

The feasible region in a mathematical programming problem is defined by the set of all feasible solutions.

Branch and Bound, Cutting Planes, and Lagrangian Relaxation are all common methods for solving integer programming problems.

In a linear programming problem, the optimal solution is always at a vertex of the feasible region.

Resource Allocation, Production Planning, and Scheduling are all common applications of mathematical programming.

The dual problem of a linear programming problem is another linear programming problem.

Penalty Methods, Barrier Methods, and Sequential Quadratic Programming are all common techniques for solving nonlinear programming problems.

Mathematical programming is a branch of mathematics that deals with optimizing objective functions.