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d.given the following tableau find an alternative basic feasible optima…

Question

d.given the following tableau find an alternative basic feasible optimal solution. (10 pts)

Explanation:

Step1: Identify Non-Basic Variables with Zero Reduced Cost

In the tableau, the reduced costs (coefficients in the \( z \)-row for non-basic variables) for \( x_5 \) and \( x_6 \) are \( 2 \) and \( 3 \)? Wait, no, wait. Wait, the \( z \)-row is \( [1, 0, 0, 0, 0, 2, 3] \)? Wait, no, the columns are \( z, x_1, x_2, x_3, x_4, x_5, x_6 \), RHS. So the \( z \)-row coefficients for non-basic variables ( \( x_3, x_4, x_5, x_6 \) since \( x_1, x_2 \) are basic) are \( 0, 0, 2, 3 \)? Wait, no, the \( z \)-row is \( z: [1, 0, 0, 0, 0, 2, 3] \), RHS 4. Wait, no, the reduced cost for a non-basic variable is the coefficient in the \( z \)-row. For a maximization problem, if a non-basic variable has a reduced cost of zero, we can introduce it into the basis to get an alternative optimal solution. Wait, here, the \( z \)-row for \( x_3 \): coefficient is \( 0 \), \( x_4 \): \( 0 \), \( x_5 \): \( 2 \), \( x_6 \): \( 3 \). Wait, no, maybe I misread. Let's check the tableau again:

Rows:

  • \( z \): \( [1, 0, 0, 0, 0, 2, 3] \), RHS \( 4 \)
  • \( x_1 \): \( [0, 1, 0, 2, -1, -1, 1] \), RHS \( 2 \)
  • \( x_2 \): \( [0, 0, 1, -2, 1, 2, 3] \), RHS \( 2 \)

So basic variables are \( x_1, x_2 \) (since their columns have 1 in their row and 0 elsewhere). Non-basic variables: \( x_3, x_4, x_5, x_6 \), with \( x_3 = 0 \), \( x_4 = 0 \), \( x_5 = 0 \), \( x_6 = 0 \).

Reduced cost for \( x_3 \): coefficient in \( z \)-row is \( 0 \), \( x_4 \): \( 0 \), \( x_5 \): \( 2 \), \( x_6 \): \( 3 \). Wait, no, for a maximization problem, the reduced cost \( c_j - z_j \). Wait, maybe the tableau is in canonical form, so \( z_j = \sum c_i a_{ij} \), and reduced cost is \( c_j - z_j \). But in the \( z \)-row, the coefficients for non-basic variables are \( c_j - z_j \). If \( c_j - z_j = 0 \), we can pivot to get an alternative solution. Here, \( x_3 \) and \( x_4 \) have \( c_j - z_j = 0 \) (since their coefficients in \( z \)-row are 0). Let's pick \( x_3 \) (or \( x_4 \)) to enter the basis.

Let's try \( x_3 \). Now, find the pivot row: for each basic variable row, compute \( \frac{\text{RHS}}{a_{ij}} \) where \( a_{ij} > 0 \) (since we want to maintain non-negativity).

For \( x_1 \) row: \( a_{13} = 2 \), RHS = 2, so \( \frac{2}{2} = 1 \)

For \( x_2 \) row: \( a_{23} = -2 \), which is negative, so we ignore it.

So the pivot row is \( x_1 \) row, pivot element is \( a_{13} = 2 \).

Step2: Perform Pivot Operation on \( x_3 \) and \( x_1 \)

Divide \( x_1 \) row by 2:

\( x_1 \) row: \( [0, \frac{1}{2}, 0, 1, -\frac{1}{2}, -\frac{1}{2}, \frac{1}{2}] \), RHS \( 1 \)

Now, update \( z \) row and \( x_2 \) row to eliminate \( x_3 \).

\( z \) row: \( z - 0 \times x_1 \) row (since \( z \)-row has 0 for \( x_3 \) coefficient, no change? Wait, original \( z \)-row: \( z = 4 + 0x_3 + 0x_4 + 2x_5 + 3x_6 \). After pivoting, \( x_3 \) is basic, \( x_1 \) is non-basic? Wait, no, pivot is \( x_3 \) entering, \( x_1 \) leaving? Wait, no, when we pivot, the entering variable is \( x_3 \), leaving variable is the one with the minimum ratio. We had \( x_1 \) row with ratio 1, \( x_2 \) row had negative coefficient, so leaving variable is \( x_1 \).

So new basic variables: \( x_3, x_2 \)

Update \( x_2 \) row: \( x_2 \) row = original \( x_2 \) row + 2 * new \( x_1 \) row (since \( a_{23} = -2 \), so to eliminate \( x_3 \), we do \( x_2 + 2x_1 \))

Original \( x_2 \) row: \( [0, 0, 1, -2, 1, 2, 3] \), RHS 2

New \( x_1 \) row: \( [0, \frac{1}{2}, 0, 1, -\frac{1}{2}, -\frac{1}{2}, \frac{1}{2}] \), RHS 1

So \( x_2 \) row new: \( [0, 0 + 2\frac{1}{2}, 1 + 20, -2 + 21, 1 + 2(…

Answer:

Step1: Identify Non-Basic Variables with Zero Reduced Cost

In the tableau, the reduced costs (coefficients in the \( z \)-row for non-basic variables) for \( x_5 \) and \( x_6 \) are \( 2 \) and \( 3 \)? Wait, no, wait. Wait, the \( z \)-row is \( [1, 0, 0, 0, 0, 2, 3] \)? Wait, no, the columns are \( z, x_1, x_2, x_3, x_4, x_5, x_6 \), RHS. So the \( z \)-row coefficients for non-basic variables ( \( x_3, x_4, x_5, x_6 \) since \( x_1, x_2 \) are basic) are \( 0, 0, 2, 3 \)? Wait, no, the \( z \)-row is \( z: [1, 0, 0, 0, 0, 2, 3] \), RHS 4. Wait, no, the reduced cost for a non-basic variable is the coefficient in the \( z \)-row. For a maximization problem, if a non-basic variable has a reduced cost of zero, we can introduce it into the basis to get an alternative optimal solution. Wait, here, the \( z \)-row for \( x_3 \): coefficient is \( 0 \), \( x_4 \): \( 0 \), \( x_5 \): \( 2 \), \( x_6 \): \( 3 \). Wait, no, maybe I misread. Let's check the tableau again:

Rows:

  • \( z \): \( [1, 0, 0, 0, 0, 2, 3] \), RHS \( 4 \)
  • \( x_1 \): \( [0, 1, 0, 2, -1, -1, 1] \), RHS \( 2 \)
  • \( x_2 \): \( [0, 0, 1, -2, 1, 2, 3] \), RHS \( 2 \)

So basic variables are \( x_1, x_2 \) (since their columns have 1 in their row and 0 elsewhere). Non-basic variables: \( x_3, x_4, x_5, x_6 \), with \( x_3 = 0 \), \( x_4 = 0 \), \( x_5 = 0 \), \( x_6 = 0 \).

Reduced cost for \( x_3 \): coefficient in \( z \)-row is \( 0 \), \( x_4 \): \( 0 \), \( x_5 \): \( 2 \), \( x_6 \): \( 3 \). Wait, no, for a maximization problem, the reduced cost \( c_j - z_j \). Wait, maybe the tableau is in canonical form, so \( z_j = \sum c_i a_{ij} \), and reduced cost is \( c_j - z_j \). But in the \( z \)-row, the coefficients for non-basic variables are \( c_j - z_j \). If \( c_j - z_j = 0 \), we can pivot to get an alternative solution. Here, \( x_3 \) and \( x_4 \) have \( c_j - z_j = 0 \) (since their coefficients in \( z \)-row are 0). Let's pick \( x_3 \) (or \( x_4 \)) to enter the basis.

Let's try \( x_3 \). Now, find the pivot row: for each basic variable row, compute \( \frac{\text{RHS}}{a_{ij}} \) where \( a_{ij} > 0 \) (since we want to maintain non-negativity).

For \( x_1 \) row: \( a_{13} = 2 \), RHS = 2, so \( \frac{2}{2} = 1 \)

For \( x_2 \) row: \( a_{23} = -2 \), which is negative, so we ignore it.

So the pivot row is \( x_1 \) row, pivot element is \( a_{13} = 2 \).

Step2: Perform Pivot Operation on \( x_3 \) and \( x_1 \)

Divide \( x_1 \) row by 2:

\( x_1 \) row: \( [0, \frac{1}{2}, 0, 1, -\frac{1}{2}, -\frac{1}{2}, \frac{1}{2}] \), RHS \( 1 \)

Now, update \( z \) row and \( x_2 \) row to eliminate \( x_3 \).

\( z \) row: \( z - 0 \times x_1 \) row (since \( z \)-row has 0 for \( x_3 \) coefficient, no change? Wait, original \( z \)-row: \( z = 4 + 0x_3 + 0x_4 + 2x_5 + 3x_6 \). After pivoting, \( x_3 \) is basic, \( x_1 \) is non-basic? Wait, no, pivot is \( x_3 \) entering, \( x_1 \) leaving? Wait, no, when we pivot, the entering variable is \( x_3 \), leaving variable is the one with the minimum ratio. We had \( x_1 \) row with ratio 1, \( x_2 \) row had negative coefficient, so leaving variable is \( x_1 \).

So new basic variables: \( x_3, x_2 \)

Update \( x_2 \) row: \( x_2 \) row = original \( x_2 \) row + 2 * new \( x_1 \) row (since \( a_{23} = -2 \), so to eliminate \( x_3 \), we do \( x_2 + 2x_1 \))

Original \( x_2 \) row: \( [0, 0, 1, -2, 1, 2, 3] \), RHS 2

New \( x_1 \) row: \( [0, \frac{1}{2}, 0, 1, -\frac{1}{2}, -\frac{1}{2}, \frac{1}{2}] \), RHS 1

So \( x_2 \) row new: \( [0, 0 + 2\frac{1}{2}, 1 + 20, -2 + 21, 1 + 2(-\frac{1}{2}), 2 + 2(-\frac{1}{2}), 3 + 2\frac{1}{2}] \), RHS \( 2 + 2*1 = 4 \)

Simplify: \( [0, 1, 1, 0, 0, 1, 4] \), RHS 4? Wait, no:

Wait, \( a_{21} = 0 + 2*(1/2) = 1 \)

\( a_{22} = 1 + 2*0 = 1 \)

\( a_{23} = -2 + 2*1 = 0 \)

\( a_{24} = 1 + 2*(-1/2) = 1 - 1 = 0 \)

\( a_{25} = 2 + 2*(-1/2) = 2 - 1 = 1 \)

\( a_{26} = 3 + 2*(1/2) = 3 + 1 = 4 \)

RHS: \( 2 + 2*1 = 4 \)

Now, \( z \) row: \( z = 4 + 0x_3 + 0x_4 + 2x_5 + 3x_6 \). But now \( x_1 \) is non-basic, \( x_3 \) is basic. Wait, maybe I made a mistake. Alternatively, let's check the reduced cost for \( x_4 \). \( x_4 \) has \( c_j - z_j = 0 \) (coefficient in \( z \)-row is 0). Let's try \( x_4 \) entering the basis.

For \( x_4 \), find pivot row: \( a_{ij} > 0 \)

\( x_1 \) row: \( a_{14} = -1 \) (negative, ignore)

\( x_2 \) row: \( a_{24} = 1 \), RHS = 2, so ratio \( \frac{2}{1} = 2 \)

So pivot row is \( x_2 \) row, pivot element \( a_{24} = 1 \)

Step3: Perform Pivot Operation on \( x_4 \) and \( x_2 \)

Divide \( x_2 \) row by 1 (no change): \( x_2 \) row: \( [0, 0, 1, -2, 1, 2, 3] \), RHS 2

Update \( x_1 \) row: \( x_1 \) row = original \( x_1 \) row + 1 * \( x_2 \) row (since \( a_{14} = -1 \), so \( x_1 + x_2 \) to eliminate \( x_4 \))

Original \( x_1 \) row: \( [0, 1, 0, 2, -1, -1, 1] \), RHS 2

New \( x_1 \) row: \( [0, 1 + 0, 0 + 1, 2 + (-2), -1 + 1, -1 + 2, 1 + 3] \), RHS \( 2 + 2 = 4 \)

Simplify: \( [0, 1, 1, 0, 0, 1, 4] \), RHS 4

Update \( z \) row: \( z \)-row has \( x_4 \) coefficient 0, so no change (since \( c_j - z_j = 0 \))

Now, the new basic variables are \( x_1 \) and \( x_4 \)? Wait, no, after pivoting, \( x_4 \) enters, \( x_2 \) leaves? Wait, \( x_2 \) row was the pivot row, so \( x_4 \) enters, \( x_2 \) leaves. Now basic variables are \( x_1 \) and \( x_4 \).

Wait, let's check the solution. Original solution: \( x_1 = 2 \), \( x_2 = 2 \), \( x_3 = 0 \), \( x_4 = 0 \), \( x_5 = 0 \), \( x_6 = 0 \), \( z = 4 \).

After pivoting with \( x_4 \) and \( x_2 \):

Basic variables: \( x_1 \) (row 1: \( x_1 + x_2 - 2x_3 + 0x_4 + x_5 + 4x_6 = 4 \)? Wait, no, maybe I messed up the pivot. Let's start over.

Correct approach: In a linear programming tableau, an alternative optimal solution exists if there is a non-basic variable with reduced cost zero (for maximization, \( c_j - z_j = 0 \); for minimization, \( z_j - c_j = 0 \)). Here, the \( z \)-row (objective row) for non-basic variables \( x_3, x_4, x_5, x_6 \) has coefficients: \( x_3: 0 \), \( x_4: 0 \), \( x_5: 2 \), \( x_6: 3 \). So \( x_3 \) and \( x_4 \) have reduced cost zero (since \( c_j - z_j = 0 \)), meaning we can introduce them into the basis to get an alternative solution.

Let's choose \( x_3 \) to enter the basis. The basic variables are \( x_1 \) (row 1) and \( x_2 \) (row 2). For \( x_3 \), the coefficients in the basic rows are \( a_{13} = 2 \) (row 1) and \( a_{23} = -2 \) (row 2). We only consider positive coefficients for the ratio test (to ensure non-negativity of RHS after pivoting). So only row 1 has \( a_{13} = 2 > 0 \), so pivot row is row 1, pivot element is 2.

Pivot on \( x_3 \) (column 3) and \( x_1 \) (row 1):
  1. Divide row 1 by 2:
  • New row 1: \( \frac{1}{2} \times [0, 1, 0, 2, -1, -1, 1] \) RHS \( \frac{2}{2} = 1 \)
  • So row 1: \( [0, \frac{1}{2}, 0, 1, -\frac{1}{2}, -\frac{1}{2}, \frac{1}{2}] \) RHS \( 1 \)
  1. Update row 2 ( \( x_2 \) row) to eliminate \( x_3 \):
  • Row 2: original row 2 + 2 × new row 1 (since \( a_{23} = -2 \), we add 2×new row 1 to make \( a_{23} = 0 \))
  • New row 2: \( [0, 0, 1, -2, 1, 2, 3] + 2×[0, \frac{1}{2}, 0, 1, -\frac{1}{2}, -\frac{1}{2}, \frac{1}{2}] \)
  • Calculate each element:
  • \( z \)-column: \( 0 + 2×0 = 0 \)
  • \( x_1 \)-column: \( 0 + 2×\frac{1}{2} = 1 \)
  • \( x_2 \)-column: \( 1 + 2×0 = 1 \)
  • \( x_3 \)-column: \( -2 + 2×1 = 0 \)
  • \( x_4 \)-column: \( 1 + 2×(-\frac{1}{2}) = 1 - 1 = 0 \)
  • \( x_5 \)-column: \( 2 + 2×(-\frac{1}{2}) = 2 - 1 = 1 \)
  • \( x_6 \)-column: \( 3 + 2×\frac{1}{2} = 3 + 1 = 4 \)
  • RHS: \( 2 + 2×1 = 4 \)
  • So row 2: \( [0, 1, 1, 0, 0, 1, 4] \) RHS \( 4 \)
  1. Update the \( z \)-row:
  • The \( z \)-row has \( x_3 \)-coefficient 0, so no change (since we're pivoting a variable with reduced cost 0, the objective value remains the same).

Now, the new basic variables are \( x_3 \) (row 1) and \( x_2 \) (row 2)? Wait, no, row 1 is now the \( x_3 \) row (since we pivoted \( x_3 \) into the basis, replacing \( x_1 \)). Wait, basic variables are the ones with a single 1 in their column and 0 elsewhere. Now, column 3 ( \( x_3 \)) has 1 in row 1, 0 in row 2. Column 2 ( \( x_2 \)) has 1 in row 2, 0 in row 1. So basic variables are \( x_3 \) and \( x_2 \), non-basic variables are \( x_1, x_4, x_5, x_6 \).

Now, find the solution:

  • \( x_3 = 1 \) (row 1 RHS)
  • \( x_2 = 4 \) (row 2 RHS)
  • \( x_1 = 0 \) (non-basic)
  • \( x_4 = 0 \) (non-basic)
  • \( x_5 = 0 \) (non-basic)
  • \( x_6 = 0 \) (non-basic)
  • \( z = 4 \) (from \( z \)-row RHS)

Wait, but let's check the constraints. Original constraints (assuming standard form, \( x_1, x_2, x_3, x_4, x_5, x_6 \geq 0 \)):

From row 1 (original): \( x_1 + 2x_3 - x_4 - x_5 + x_6 = 2 \)

From row 2 (original): \( x_2 - 2x_3 + x_4 + 2x_5 + 3x_6 = 2 \)

With \( x_1 = 0 \), \( x_2 = 4 \), \( x_3 = 1 \), \( x_4 = 0 \), \( x_5 = 0 \), \( x_6 = 0 \):

Row 1: \( 0 + 2(1) - 0 - 0 + 0 = 2 \) (satisfies)

Row 2: \( 4 - 2(1) + 0 + 0 + 0 = 2 \) (satisfies)