← 2018 Paper 2

UPSC 2018 Maths Optional Paper 2 Q2b — Step-by-Step Solution

20 marks · Section A

Big-M / two-phase method (artificial variables) · Linear Programming · asked 5× in 14 yrs · Read the full method →

Question

Solve the following linear programming problem by Big M-method and show that the problem has finite optimal solutions. Also find the value of the objective function:

Minimize z=3x1+5x2\text{Minimize } z=3x_1+5x_2

subject to

x1+2x2≥83x1+2x2≥125x1+6x2≤60,x1,x2≥0.\begin{aligned}x_1+2x_2&\ge 8\\ 3x_1+2x_2&\ge 12\\ 5x_1+6x_2&\le 60,\\ x_1,x_2&\ge 0.\end{aligned}

Technique

Big-M penalty method; surplus + artificial variables on the two ≥\ge constraints, slack on the ≤\le constraint; pivot until both artificials leave and all reduced costs are non-positive (minimization).

Solution

Step 1 — Standard form with slack/surplus and artificial variables

Convert each constraint to an equation. The two ≥\ge constraints need surplus variables s1,s2≥0s_1,s_2\ge0 (subtracted) and artificial variables A1,A2≥0A_1,A_2\ge0; the ≤\le constraint needs a slack s3≥0s_3\ge0:

x1+2x2−s1+A1=8,3x1+2x2−s2+A2=12,5x1+6x2+s3=60.\begin{aligned} x_1+2x_2-s_1+A_1&=8,\\ 3x_1+2x_2-s_2+A_2&=12,\\ 5x_1+6x_2+s_3&=60. \end{aligned}

For minimization, artificials are penalized with +M+M (MM a large positive number):

Minimize z=3x1+5x2+0 s1+0 s2+0 s3+MA1+MA2.\text{Minimize } z=3x_1+5x_2+0\,s_1+0\,s_2+0\,s_3+M A_1+M A_2.

Initial basis: A1=8, A2=12, s3=60A_1=8,\ A_2=12,\ s_3=60 (all else 00).

Step 2 — Initial tableau and reduced costs

With cB=(M,M,0)c_B=(M,M,0) for the basis (A1,A2,s3)(A_1,A_2,s_3), the reduced cost zj−cjz_j-c_j for each non-basic column is cB⋅(column)−cjc_B\cdot(\text{column})-c_j:

variablex1x_1x2x_2s1s_1s2s_2
zj−cjz_j-c_j4M−34M-34M−54M-5−M-M−M-M

(e.g. x1x_1: M⋅1+M⋅3+0⋅5−3=4M−3M\cdot1+M\cdot3+0\cdot5-3=4M-3). For minimization we improve while some zj−cj>0z_j-c_j>0. The most positive is x1x_1 (4M−34M-3) vs x2x_2 (4M−54M-5); enter x1x_1 (largest coefficient of MM tie broken by constant: 4M−3>4M−54M-3>4M-5).

Ratio test (RHS / positive entries in x1x_1 column =(1,3,5)=(1,3,5)): 8/1=8, 12/3=4, 60/5=128/1=8,\ 12/3=4,\ 60/5=12. Minimum =4=4 at row 2 ⇒ A2A_2 leaves.

Step 3 — Pivot 1 (enter x1x_1, leave A2A_2), pivot element 33

Row 2 ÷3\div 3:  x1+23x2−13s2+13A2=4\ x_1+\tfrac23x_2-\tfrac13s_2+\tfrac13A_2=4. Eliminate x1x_1 elsewhere:

R1←R1−R2′: 43x2−s1+13s2+A1−13A2=4,R3←R3−5R2′: 83x2+53s2+s3−53A2=40.\begin{aligned} R_1\leftarrow R_1-R_2':&\ \tfrac43x_2-s_1+\tfrac13s_2+A_1-\tfrac13A_2=4,\\ R_3\leftarrow R_3-5R_2':&\ \tfrac83x_2+\tfrac53s_2+s_3-\tfrac53A_2=40. \end{aligned}

New basis (A1,x1,s3)=(4,4,40)(A_1,x_1,s_3)=(4,4,40). Recompute reduced costs; the x2x_2 column still has a positive zj−cjz_j-c_j (coefficient of MM is 43>0\tfrac43>0), so enter x2x_2.

Ratio test (x2x_2 column entries in rows (A1,x1,s3)=(43,23,83)(A_1,x_1,s_3)=(\tfrac43,\tfrac23,\tfrac83), RHS (4,4,40)(4,4,40)): 4/43=3, 4/23=6, 40/83=154/\tfrac43=3,\ 4/\tfrac23=6,\ 40/\tfrac83=15. Minimum =3=3 at row 1 ⇒ A1A_1 leaves.

Step 4 — Pivot 2 (enter x2x_2, leave A1A_1), pivot element 43\tfrac43

Row 1 ÷43\div\tfrac43:  x2−34s1+14s2=3\ x_2-\tfrac34 s_1+\tfrac14 s_2=3. Update:

x1=4−23x2 ⇒ x1=4−23(3)=2,s3=40−83x2=40−83(3)=32.\begin{aligned} x_1&=4-\tfrac23x_2\ \Rightarrow\ x_1=4-\tfrac23(3)=2,\\ s_3&=40-\tfrac83x_2=40-\tfrac83(3)=32. \end{aligned}

New basis (x2,x1,s3)=(3,2,32)(x_2,x_1,s_3)=(3,2,32); both artificials are now non-basic (at 00).

Step 5 — Optimality check

With both artificials expelled and the basis (x1,x2,s3)(x_1,x_2,s_3), all reduced costs zj−cj≤0z_j-c_j\le0 for the non-basic variables s1,s2,A1,A2s_1,s_2,A_1,A_2 (the surplus columns give zj−cj≤0z_j-c_j\le0 and artificials carry the −M-M penalty). No entering variable improves zz, so the current basic feasible solution is optimal:

x1=2,x2=3,s3=32,s1=s2=0,A1=A2=0.x_1=2,\quad x_2=3,\quad s_3=32,\quad s_1=s_2=0,\quad A_1=A_2=0.

Objective value:

z=3(2)+5(3)=6+15=21.z=3(2)+5(3)=6+15=21.

Answer

  x1=2, x2=3,zmin⁡=21(finite optimum, artificials driven to 0).  \boxed{\;x_1=2,\ x_2=3,\quad z_{\min}=21\quad(\text{finite optimum, artificials driven to }0).\;}
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