Table of Contents
Graphical Method of Solving Linear Programming Problems
The graphical method of solving linear programming problems is a technique that uses a graph to help solve a problem. The graph is used to identify the feasible region, which is the area of the graph that represents all of the possible solutions to the problem. The graph also helps to identify the optimal solution, which is the solution that provides the best results.
Methods used for Solving Linear Programming
Problems
There are a number of methods that can be used to solve linear programming problems. The most common methods are the graphical method, the simplex method, and the dual simplex method.
The graphical method is a graphical technique that can be used to solve linear programming problems. The method involves plotting the points representing the linear programming problem on a graph. The feasible region is then shaded on the graph, and the optimal solution is located.
The simplex method is a mathematical technique that can be used to solve linear programming problems. The method involves solving a series of linear equations to find the optimal solution.
The dual simplex method is a mathematical technique that can be used to solve linear programming problems. The method involves solving a series of linear equations to find the optimal solution. The method is similar to the simplex method, but it takes into account the duality of the linear programming problem.
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Ways to Solve the LP Problem Law
There are a few ways to solve the LP problem law:
1. Graphically
2. Algorithmically
3. Numerically
4. Heuristically
Iso-Cost Method
The iso-cost method is a technique used to estimate the cost of a product or service. The iso-cost method calculates the cost of a product or service by estimating the cost of the inputs required to produce the product or service. The iso-cost method is useful for estimating the cost of a product or service that does not have a market price.
Information for the Wooden Tables and Chairs Linear Programming Problem
The data for the problem are:
Number of tables: 10
Number of chairs: 16
The profit per table is $10 and the profit per chair is $1.
The maximum profit that can be made is $160.
