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Can a linear programming problem have more than two decision variables?

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Can a linear programming problem have more than two decision variables?

However, that approach limits the number of decision variables to two, and problems with only two decision variables are often trans- parent and inauthentic. Additional decision variables give problems more authenticity but raise the question of obtaining an optimal solution.

Which method is used when there are more than two decision variables in LPP?

If there are or more decision variables in a LPP, SIMPLEX method is used.

How many variables in LPP can be solved easily?

two variables
With graphical methods, any optimization programming problems consisting of only two variables can easily be solved. These variables can be referred as x₁ and x₂ and with the help of these variables, most of the analysis can be done on a two-dimensional graph.

What is linear programming problem in operation research?

The Linear Programming Problems (LPP) is a problem that is concerned with finding the optimal value of the given linear function. The optimal value can be either maximum value or minimum value. Here, the given linear function is considered an objective function.

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What is an Optimisation problem in LPP?

A mathematical optimization problem is one in which some function is either maximized or minimized relative to a given set of alternatives. The function to be minimized or maximized is called the objective function and the set of alternatives is called the feasible region (or constraint region).

What are the restriction or limitation imposed on the linear programming problem?

Constraints: These are the restrictions on the variables of an linear programming problem are called as linear constraints.

How do you solve linear programming problems?

Steps to Solve a Linear Programming Problem

  1. Step 1 – Identify the decision variables.
  2. Step 2 – Write the objective function.
  3. Step 3 – Identify Set of Constraints.
  4. Step 4 – Choose the method for solving the linear programming problem.
  5. Step 5 – Construct the graph.
  6. Step 6 – Identify the feasible region.