Solve the following quadratic programming problem(Wolf’s method)
Maximize Z = 2x1+x2-x12,
Subject to 2x1+3x2 ≤ 6,
2x1+x2 ≤ 4,
x1, x2≥ 0
Step-1 :
The problem is converted to canonical form by adding slack, surplus and artificial variables as appropiate.
"2x_1+3x_2+s_1=6\\\\2x_1+x_2+s_2=4"
The problem is converted to canonical form by adding slack, surplus and artificial variables as appropiate.
"Z'-2x_1-2x_2+x_3=0"
"2x_1+3x_2+s_1=6\\\\2x_1+x_2+s_2=4"
Now represent the new system of constraint equations in the matrix form
"\\begin{bmatrix}\n 1 & -2&-1&1&0&0 \\\\\n 0 & 2&3&0&1&0\\\\0 & 2&1&0&0&1\n\\end{bmatrix}\\begin{bmatrix}\n Z'\\\\\n x_1\\\\x_2\\\\x_3\\\\s_1\\\\s_2\n\\end{bmatrix}=\\begin{bmatrix}\n 0 \\\\\n 6\\\\4\n\\end{bmatrix}"
"or\\\\" "\\begin{bmatrix}\n -1 & -c \\\\\n 0 & A\n\\end{bmatrix}\\begin{bmatrix}\n z \\\\\n x \n\\end{bmatrix}=\\begin{bmatrix}\n 0\\\\\n b\n\\end{bmatrix}\\geq0"
"where e=\u03b2_0,a_3=\u03b2_1,a_4=\u03b2_2"
"Step\\ 2:The\\ basis \\ matrix\\ B_1\\ of\\ order (2+1)=3 can\\ be\\ expressed\\ as\\\\ \\beta_1=\\begin{bmatrix}\n \\beta_0 & \\beta_1&\\beta_2 \\\\\n \n\\end{bmatrix}=\\begin{bmatrix}\n 1 & 0&0\\\\\n 0& 1&0\\\\0 & 0&1\n\\end{bmatrix}"
Iteration=1 : Repeat steps 3 to 5 to get new solution
Step-3: To select the vector corresponding to a non-basic variable to enter into the basis, we compute
"c_k-z_k=Max\\lbrace{(c_j-z_j)>0;j=1,2}\\rbrace"
"=Max\\lbrace{-(1^{st}\\ row\\ of\\ \\beta_1^{-1})(Columns\\ a_j\\ not\\ in\\ basis)}\\rbrace"
"=max\\lbrace-\\begin{bmatrix}\n 1 & 0&0\n\\end{bmatrix}\\begin{bmatrix}\n 2 & -1& 1 \\\\\n 2 & 3& 0\\\\2 & 1& 0\n\\end{bmatrix}\\rbrace"
"=Max\\lbrace-\\begin{bmatrix}\n -2 & -1 &1\n\\end{bmatrix}\\rbrace\\\\ =Max\\lbrace\\begin{bmatrix}\n 2 & 1 &-1\n\\end{bmatrix}\\rbrace\\\\ =2 (correspnds\\ to\\ c_1-z_1)"
Thus, vector "x_1" is selected to enter into the basis, for k=1
Step-4: To select a basic variable to leave the basis, we compute "y_k" for k=1, as follows
"y_1=\\beta_1^{-1}a_1=\\begin{bmatrix}\n 1 & 0&0\\\\\n 0& 1&0\\\\0 & 0&1\n\\end{bmatrix}\\begin{bmatrix}\n -2\\\\\n 2\\\\2\n\\end{bmatrix}=\\begin{bmatrix}\n -2\\\\\n 2\\\\2\n\\end{bmatrix}and\\ X_B=\\begin{bmatrix}\n 0\\\\\n 6\\\\4\n\\end{bmatrix}"
Now, calculate the minimum ratio to select the basic variable to leave the basis
"\\frac{x_{\\beta r}}{y_{rk}}=min\\lbrace\\frac{x_{\\beta i}}{y_{ik}},y_{ik}\\gt0\\rbrace\\\\"
"=min\\lbrace \\frac{6}{2},\\frac{4}{2}\\rbrace\\\\=min\\lbrace3,2\\rbrace"
"=2(corresponds\\ to \\frac{x_{\\beta 2}}{y_{21}} )"
Thus, vector "S_2" is selected to leave the basis, for r=2
The table with new entries in column "Y_1" and the minimum ratio
The table solution is now updated by replacing variable "S_2" with the variable "X_1" into the basis.
For this we apply the following row operations in the same way as in the simplex method
The improved solution is
Iteration=2 : Repeat steps 3 to 5 to get new solution
Step-3: To select the vector corresponding to a non-basic variable to enter into the basis, we compute
"c_k-z_k=Max\\lbrace{(c_j-z_j)>0;j=1,2}\\rbrace"
"=Max\\lbrace{-(1^{st}\\ row\\ of\\ \\beta_1^{-1})(Columns\\ a_j\\ not\\ in\\ basis)}\\rbrace"
"=max\\lbrace-\\begin{bmatrix}\n 1 & 0&1\n\\end{bmatrix}\\begin{bmatrix}\n 0 & -1& 1 \\\\\n 0 & 3& 0\\\\1 & 1& 0\n\\end{bmatrix}\\rbrace"
"=Max\\lbrace-\\begin{bmatrix}\n -1 & 0 &1\n\\end{bmatrix}\\rbrace\\\\ =Max\\lbrace\\begin{bmatrix}\n -1 & 0 &-1\n\\end{bmatrix}\\rbrace"
"Since\\ all\\ Z_j-C\n\n\n_j\u22650"
"Hence, optimal\\ solution\\ is\\ arrived\\ with\\ value\\ of\\ variables\\ as :\\\\\nx_1=2,x_2=0,x_1^{2}=0"
"Max Z=4"
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