The simplex method is a mathematical approach used to solve linear programming problems by maximizing or minimizing a linear objective function subject to constraints. The process involves setting up a tableau, where rows and columns represent the equations and variables, respectively.
Continue to perform row operations until there are no other negatives in the last row
Continue to perform row operations to set the value above and below the exit pivot to 0
Row Operations are performed to set pivot exit element to 1
Rewrite equations using Y
Set Up Tableau
Problem turns into a maximization problem
Transpose (Columns become rows)
Minimize
Maximize
Identify Pivot Elements; Entering pivot is column with most negative in last row; Exit pivot is the smallest value product of last row divided by enter column
Non Basic
Basic
Exit Pivot
Enter Pivot
Slack Variables are used to "pick up slack" on the left & right hand side of the equations
Slack Variables for Minimization problems are represented by (x)
Slack Variables for Maximization problems are represented by (s)
Solution is determined; point in the feasible area that maximizes or minimizes the objective function
Maximization - solution is the largest value
Minimizing - solution is the smallest value
Create a table with the corner point coordinates and insert coordinates to solve for the objection function
Identify the feasible area; Is it bounded or unbounded?
Graph conditions to find feasible region
Maximizing or minimizing a linear objective function subject to constraints (conditions