Lec-7 Basic Feasible Solution || Basic & Non-Basic variables || Linear Programming in Hindi & Urdu
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Basic Solution in LPP | Basic Feasible Solution | Basic & Non-Basic variables | Linear Programming
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A linear programming problem (LPP) is a mathematical optimization model that seeks to maximize or minimize a linear objective function subject to a set of linear constraints. The basic solution of an LPP refers to a solution that satisfies the constraints and has a non-negative value for all decision variables.
A basic solution is obtained by setting a subset of the variables to zero and solving the resulting set of linear equations for the remaining variables. The subset of variables that are set to zero is called the non-basic variables, while the remaining variables are called the basic variables.
The number of basic variables is equal to the number of constraints in the LPP. A basic feasible solution is a basic solution that also satisfies the non-negativity constraints on the decision variables.
To find a basic solution, we can use the simplex method, which is an algorithm that starts with a basic feasible solution and iteratively improves it until an optimal solution is found. The simplex method operates by moving from one basic feasible solution to another along edges of the feasible region.
In summary, a basic solution in LPP is a feasible solution where a subset of the variables is set to zero, and the remaining variables satisfy the constraints and have a non-negative value. The simplex method can be used to find a basic feasible solution, which can then be improved iteratively to find an optimal solution.
In this video we will describe the following. .
• liner programming
• programming model
• unique optimal solution
• maximize
• graphical method
• solution of problem
• optimal solution
• feasible region
• inequalities
and maximize
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