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Linear programming (lp), also called linear optimization, is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical model whose requirements and objective are represented by linear relationships The most common occurrence is in connection with regression models and the term is often taken as synonymous with linear regression model. Linear programming is a special case of mathematical programming (also known as mathematical optimization).

The graph illustrates the simplex algorithm solving a linear programming problem with two variables In statistics, the term linear model refers to any model which assumes linearity in the system In mathematical optimization, dantzig 's simplex algorithm (or simplex method) is an algorithm for linear programming

[1] the name of the algorithm is derived from the concept of a simplex and was suggested by t

[2] simplices are not actually used in the method, but one. The theory of linear programming dictates that under mild assumptions (if the linear program has an optimal solution, and if the feasible region does not contain a line), one can always find an extreme point or a corner point that is optimal The obtained optimum is tested for being an integer solution. Big m method in operations research, the big m method is a method of solving linear programming problems using the simplex algorithm

Integer programming an integer programming problem is a mathematical optimization or feasibility program in which some or all of the variables are restricted to be integers In many settings the term refers to integer linear programming (ilp), in which the objective function and the constraints (other than the integer constraints) are linear. Linear programming relaxation is a standard technique for designing approximation algorithms for hard optimization problems In this application, an important concept is the integrality gap, the maximum ratio between the solution quality of the integer program and of its relaxation.

Because of the complexity of the simulation, the objective function may become difficult and expensive to evaluate

Usually, the underlying simulation model is stochastic, so that the objective function must be estimated using statistical. Fundamental theorem of linear programming in mathematical optimization, the fundamental theorem of linear programming states, in a weak formulation, that the maxima and minima of a linear function over a convex polygonal region occur at the region's corners.

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