The problem is converted to canonical form by adding slack, surplus and artificial variables as appropriate
1. As the constraint-1 is of type '≤' we should add slack variable "S_1"
2. As the constraint-2 is of type '≤' we should add slack variable "S_2"
3. As the constraint-3 is of type '≤' we should add slack variable "S_3"
4. As the constraint-4 is of type '≤' we should add slack variable "S_4"
After introducing slack variables
"\\text{Max } z=4x_1+3x_2+0S_1+0S_2+0S_3+0S_4\\\\\n\\text{Subject to }\\\\\n~~2x_1+x_2+S_1~~~~~~~~~~~~~~~~~~~~~~~~=1000\\\\\n~~~~x_1+x_2~~~~~~~~+S_2~~~~~~~~~~~~~~~~=800\\\\\n~~~~x_1~~~~~~~~~~~~~~~~~~~~~~~~~+S_3~~~~~~~~=400\\\\\n~~~~~~~~~~~~~x_2~~~~~~~~~~~~~~~~~~~~~~~~+S_4=700\\\\\n\\text{All variables nonnegative}"
Negative minimum "z_j-c_j"  is -4 and its column index is 1. So, the entering variable is "x_1" .
Minimum ratio is 400 and its row index is 3. So, the leaving basis variable is "S_3" .
∴ The pivot element is 1.
"R_3(new)=R_3(old), R_1(new)=R_1(old)-2R_3(new)\\\\\nR_2(new)=R_2(old)-R_3(new), R_4(new)=R_4(old)"
Negative minimum "z_j-c_j"  is -3 and its column index is 2. So, the entering variable is "x_2" .
Minimum ratio is 200 and its row index is 1. So, the leaving basis variable is "S_1" .
∴ The pivot element is 1.
"R_1(new)=R_1(old), R_2(new)=R_2(old)-R_1(new)\\\\\nR_3(new)=R_3(old), R_4(new)=R_4(old)-R_1(new)"
Negative minimum "z_j-c_j"  is -2 and its column index is 5. So, the entering variable is "S_3" .
Minimum ratio is 200 and its row index is 2. So, the leaving basis variable is "S_2" .
∴ The pivot element is 1.
"R_2(new)=R_2(old), R_1(new)=R_1(old)+2R_2(new)\\\\\nR_3(new)=R_3(old)-R_2(new), R_4(new)=R_4(old)-2R_2(new)"
Since all "z_j-c_j\\geq0"
Hence, optimal solution is arrived with value of variables as :
"x_1=200,x_2=600"
"Max~z=2600"
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