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One.
To be a (non-trivial) linear programming problem both the objective function and the constraints must be linear.
If there were no constraints then the objective function could be made arbitrarily large or arbitrarily small. (Think of a line in two-space.)
By adding one constraint the objective function's value can be limited to a finite value.
What else can I help you with?
What are the scope of linear programming?
Linear programming can be used to solve problems requiring the optimisation (maximum or minimum) of a linear objective function when the variables are subject to a linear constraints.
What is the theoretical limit on the number of constraints on a linear programming problem?
There is no limit.
What is non linear programming problem?
It is a programming problem in which the objective function is to be optimised subject to a set of constraints. At least one of the constraints or the objective functions must be non-linear in at least one of the variables.
What are the components of linear programming?
A linear objective function and linear constraints.
What do you understand by linear programming problem?
1. What do you understand by Linear Programming Problem? What are the requirements of Linear Programming Problem? What are the basic assumptions of Linear Programming Problem?
When does infeasibility occur in a linear programming problem?
Infeasibility occurs in a linear programming problem when there is no solution that satisfies all the constraints simultaneously.
What are the scope of linear programming?
Linear programming can be used to solve problems requiring the optimisation (maximum or minimum) of a linear objective function when the variables are subject to a linear constraints.
What is the theoretical limit on the number of constraints on a linear programming problem?
There is no limit.
What is non linear programming problem?
It is a programming problem in which the objective function is to be optimised subject to a set of constraints. At least one of the constraints or the objective functions must be non-linear in at least one of the variables.
What is infeasibility in linear programming?
In linear programming, infeasibility refers to a situation where no feasible solution exists for a given set of constraints and objective function. This can occur when the constraints are contradictory or when the feasible region is empty. Infeasibility can be detected by solving the linear programming problem and finding that no solution satisfies all the constraints simultaneously. In such cases, the linear programming problem is said to be infeasible.
To solve a linear programming problem with thousands of variables and constraints?
a mainframe computer is required
What are the components of linear programming?
A linear objective function and linear constraints.
What are the components of programming?
A linear objective function and linear constraints.
What do you understand by linear programming problem?
1. What do you understand by Linear Programming Problem? What are the requirements of Linear Programming Problem? What are the basic assumptions of Linear Programming Problem?
What do you understand by linear programming?
1. What do you understand by Linear Programming Problem? What are the requirements of Linear Programming Problem? What are the basic assumptions of Linear Programming Problem?
What is optimal feasible solution?
It is usually the answer in linear programming. The objective of linear programming is to find the optimum solution (maximum or minimum) of an objective function under a number of linear constraints. The constraints should generate a feasible region: a region in which all the constraints are satisfied. The optimal feasible solution is a solution that lies in this region and also optimises the obective function.
What is an LP surface?
An LP (Linear Programming) surface refers to a geometric representation of the feasible region defined by the constraints of a linear programming problem. In a two-dimensional space, this surface is typically a polygon formed by the intersections of the constraint lines, while in higher dimensions, it becomes a polytope. The optimal solution to the linear programming problem is found at one of the vertices of this surface, where the objective function achieves its maximum or minimum value.
