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Is it possible for a linear programming problem to have no solution?
Yes. There need not be a feasible region.
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 simplex method of linear programming?
The simplex method is an algorithm used for solving linear programming problems, which aim to maximize or minimize a linear objective function subject to linear constraints. It operates on a feasible region defined by these constraints, moving along the edges of the feasible polytope to find the optimal vertex. The method iteratively improves the solution by pivoting between basic feasible solutions until no further improvements can be made. It's widely used due to its efficiency and effectiveness in handling large-scale linear optimization problems.
Is it possible for an linear programming model to have exact two optimal solutions?
Yes, but only if the solution must be integral. There is a segment of a straight line joining the two optimal solutions. Since the two solutions are in the feasible region part of that line must lie inside the convex simplex. Therefore any solution on the straight line joining the two optimal solutions would also be an optimal solution.
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.
Is it possible for a linear programming problem to have no solution?
Yes. There need not be a feasible region.
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 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.
Distinguish between integer programming problem and linear programming problem?
Integer programming is a subset of linear programming where the feasible region is reduced to only the integer values that lie within it.
What is simplex method of linear programming?
The simplex method is an algorithm used for solving linear programming problems, which aim to maximize or minimize a linear objective function subject to linear constraints. It operates on a feasible region defined by these constraints, moving along the edges of the feasible polytope to find the optimal vertex. The method iteratively improves the solution by pivoting between basic feasible solutions until no further improvements can be made. It's widely used due to its efficiency and effectiveness in handling large-scale linear optimization problems.
What has the author Shinji Mizuno written?
Shinji Mizuno has written: 'Determination of optimal vertices from feasible solutions in unimodular linear programming' -- subject(s): Accessible book
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.
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.
Non-degenerate basic feasible solution?
A non-degenerate basic feasible solution in linear programming is one where at least one of the basic variables is strictly positive. In contrast to degenerate solutions where basic variables might be zero, non-degenerate solutions can help optimize algorithms as they ensure progress in the search for the optimal solution.
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 is degeneracy in linear programing problem?
the phenomenon of obtaining a degenerate basic feasible solution in a linear programming problem known as degeneracy.
Can a linear programming problem have two optimal solutions?
No. However, a special subset of such problems: integer programming, can have two optimal solutions.
