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What is the feasible region of a linear programming problem?
After graphing the equations for the linear programming problem, the graph will have some intersecting lines forming some polygon. This polygon (triangle, rectangle, parallelogram, quadrilateral, etc) is the feasible region.
Is there no optimal solution in linear programming simplex method?
There usually is: particularly in examples that at set school or college level.
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 is the difference between linear and integer programming?
Integer programming is a method of mathematical programming that restricts some or all of the variables to integers. A subset of Integer programming is Linear programming. This is a form of mathematical programming which seeks to find the best outcome in such a way that the requirements are linear relationships.
Can a linear programming problem have exactly two optimal solutions?
Yes, a linear programming problem can have exactly two optimal solutions. This will be the case as long as only two decision variables are used within the problem.
Can a linear programming problem have multiple optimal solutions?
When solving linear prog. problems, we base our solutions on assumptions.one of these assumptions is that there is only one optimal solution to the problem.so in short NO. BY HADI It is possible to have more than one optimal solution point in a linear programming model. This may occur when the objective function has the same slope as one its binding 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 the IP-LP Diff and LP Diff expansion?
The IP-LP Diff (Integer Programming - Linear Programming Difference) refers to the gap between the optimal solutions of an integer programming problem and its linear programming relaxation, where integer constraints are relaxed to continuous ones. LP Diff expansion typically involves analyzing how changes in the coefficients of a linear program can affect the optimal solution, often used to study the robustness of solutions or the sensitivity to perturbations. Both concepts are crucial in understanding the efficiency and performance of optimization algorithms in combinatorial problems.
What is the strong duality proof for linear programming problems?
The strong duality proof for linear programming problems states that if a linear programming problem has a feasible solution, then its dual problem also has a feasible solution, and the optimal values of both problems are equal. This proof helps to show the relationship between the primal and dual problems in linear programming.
What is an optimization problem and how can it be effectively solved?
An optimization problem is a mathematical problem where the goal is to find the best solution from a set of possible solutions. It can be effectively solved by using mathematical techniques such as linear programming, dynamic programming, or heuristic algorithms. These methods help to systematically search for the optimal solution by considering various constraints and objectives.
Can a linear programming problem have multiple solutions?
Yes. If the feasible region has a [constraint] line that is parallel to the objective function.
What is the relationship between linear programming problem and transportation problem?
you learn linear programming before you learn the transportation problem.
Why does Phase 1 in linear programming have alternative optimal solutions?
Phase 1 of linear programming aims to find a feasible solution to the problem by minimizing a "penalty" function, often involving artificial variables. If the feasible region is unbounded or if multiple ways exist to achieve the same minimum value for the penalty function, there can be alternative optimal solutions. This occurs when the objective function is parallel to a constraint boundary, allowing for multiple feasible points that yield the same objective value. Hence, the presence of alternative optimal solutions is tied to the geometry of the feasible region and the nature of the objective function.
What has the author Toshinori Munakata written?
Toshinori Munakata has written: 'Matrices and linear programming with applications' -- subject(s): Linear programming, Matrices 'Solutions manual for Matrices and linear programming'
What are the essential characteristics of linear programming problem?
essential attributes of linear programming models and its uses
