# CS计算机代考程序代写 min P  cT x Summary subject to Ax  b,

min P  cT x Summary subject to Ax  b,
x, b  0.  Initialization: Select a basic feasible solution
ˆ 1
xB bB b0,
with B the basis matrix.
 Optimality Test: The current basis is optimal, if
Reduced costs
cT cT cTB1N0. ˆN N B
Otherwise, select a variable
ˆ
x that satisfies c  0 as the entering variable.
tt
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 Recursive Step: Compute
Find an index s (determining the leaving variable, s‐th. basic variable in B) such that:
ˆˆ bs bi a0
ˆ minˆ ˆi,t 
a 1im a s,t  i,t
 
ˆ
The Pivot: From the “pivot entry” as,t , update the
basis matrix B and the vector of basis variables GO BACK TO STEP 2.
x. B
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86
ˆ 1 ABA.
tt

min Px 2x 12
In compact notation:
An Exercise
subjectto 2xx x 2 123
x 2x x 7 124
x x3 15
x , x , x , x , x  0. 12345
minPcTx, subjectto: Axb, x0, b0.
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Check the sign of reduced cost at each step cT cT cTB1N.Ifall 0,thenSTOP.
ˆN N B Step 1:
Step 2:
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TT x x,x,x,x x,x
B345N12 cT 0, 0, 0, cT 1, 2
BN
TT x x,x,x,x x,x
B245N13 cT 2, 0, 0, cT 1, 0
BN

Step 3:
TT x x,x,x,x x,x
Step 4:
TT x x,x,x,x x,x
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B215N34 cT 2, 1, 0, cT 0, 0
BN
B213N45 cT 2, 1, 0, cT 0, 0
BN
Optimal, because
cT cT cTB1N(1,2)0.
ˆN
School of Engineering
NB

Initial tableau:
The Simplex Tableau
Basic xB xN rhs -P cBT cTN 0
xB BNb Next, Basic xB xN rhs
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90
-P 0 cT cTB1N cTB1b xNBB
School of Engineering

The Simplex Tableau
For illustration purpose, study the same example.
Basic x1 x2 x3 x4 x5 rhs -P -1 -2 0 0 0 0
x3 -211002
x4 x5
-1 2 0 1 0 7 100013
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Use the rule to select the pivot entry
s=3, t=2:
ˆˆ b ba0
s mini ˆi,t 
ˆˆ
a 1im a 
s,t  i,t  Basic x1 x2 x3 x4 x5 rhs
-P -1 -2 0 0 0 0 x
x3 -211002
x4 5
-1 2 0 1 0 7 100013
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Add 2 times the x3 row to the –P row and subtract 2 times the x3 row from the x4 row:
Basic x1 x2 x3 x4 x5 rhs -P -5 0 2 0 0 4
x2 -211002
x4 5
3 0 -2 1 0 3 100013
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Basic x1 x2 x3 x4 x5 rhs -P 0 0 -4/3 5/3 0 9
x x2
0 1 -1/3 2/3 0 4 1 0 -2/3 1/3 0 1 0 0 2/3 -1/3 1 2
x1 5
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Final Step (Optimal solution):
Basic x x x3 x4 x rhs
125
-P 0 0 0 1 2 13
x2 x1
0 1 0 1/2 1/2 5 100013 0 0 1 -1/2 3/2 3
x3 2/24/2020
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95

Final Step (Optimal solution):
Basic x x x3 x4 x rhs
125
-P 0 0 0 1 2 13
x2 x1
0 1 0 1/2 1/2 5 100013
x3
P 13, atx x 3, x 5.
3
0 0 1 -1/2 3/2
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96
132

Solve the minimization problem:
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An Exercise
minP  3x x 3x 123
subjectto 2x x x 2 123
x 2x 3x 5 123
2x 2x x 6 123
x  0, x  0, x  0. 123

-P
First tableau (pivoting 1 leads to less computation):
x1 x2 x3 x4 x5 x6
-3 -1 -3 0 0 0 0
2111002 1230105 2210016
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rhs

-P
Second tableau:
x1 x2 x3 x4 x5 x6
-1 0 -2 1 0 0 2
2111002 -3 0 1 -2 1 0 1 -2 0 -1 -2 0 1 2
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rhs

-P
Third tableau:
x1 x2 x3 x4 x5 x6
-7 0 0 -3 2 0 4
5 1 0 3 -1 0 1 -3 0 1 -2 1 0 1 -5 0 0 -4 1 1 3
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rhs

-P
0 6/5
0 3/5 -1/5 0 1/5 1 -1/5 2/5 0 8/5
Fourth tableau:
x1 x2 x3 x4
x5 x6
3/5 0 27/5
0 7/5
1 1/5
0 3/5
0 1 0 -1 0 1 4
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rhs

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Optimal solution, read from the last tableau: x* 1,x* 0,x* 8,x* 0,x* 0,x* 4.
15235456
P* 27. 5

Artificial Variables
Goal: Find an (initial) basic feasible solution for the standard LP Problem:
min PcTx subject to Ax  b,
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x  0.

The Two‐Phase Method
The phase-I problem:

min P a
i i
subjectto Ax ab, x, a  0,
T
a  a ,, a  : artificial variables
1m
A direct application of the simplex method yields a basic feasible solution (not optimal)
for the original P !!
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An Example
min P2x 3x 12
subjectto 3x2x14 12
2x 4x 2 12
4x 3x 19 12
x , x  0. 12

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An Example (cont’d)
min P2x 3x 12
subjectto 3x2x 12
14 2
2x 4x x 123
x 19 124
4x 3x
x , x , x , x  0.
1234
No obvious basic feasible solution, due to less than (three) slack variables.

Phase‐1 Problem:
subjectto
3x 2x a 14 121

min Pa a 12
2x 4x x a 2 1232
4x 3x x 19 124
x,x,x,x,a,a 0. 123412
Now the (initial) basic feasible variable
TT x  a,a,x withvalues 14,2,19 .

B124
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Applying the simplex technique,
Initial tableau:

x1 x2 x3 x4 a1 a2 0000110
P
3 2 0 0 1 0 14
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2 -4 -1 0 0 1 2 4 3 0 1 0 0 19
rhs

(Use elimination to the first row to make the entries for the initial basic variables 0.)
First tableau:
x1 x2 x3 x4 a1 a2
-5 2 1 0 0 0 -16
P
3 2 0 0 1 0 14
2 -4 -1 0 0 1 2
4 3 0 1 0 0 19
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rhs

(The basic variable a2 leaving, so delete the irrelevant a2 column:)
Second tableau:

x1 x2 x3 x4 a1
0 -8 -3/2 0 0 -11
P
0 8 3/2 0 1 11
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1 -2 -1/2 0 0 1 011210 15
rhs

(The basic variable x4 leaving, but cannot delete the relevant x4 column:)
Third tableau:
 0 P
0 -1/22 8/11
-1/11 1/11
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111
x1 x2 x3 x4
a1 0
rhs
0 0
1 0
0 1
1/22 -8/11 -3/22 2/11
1
2/11 1/11
0 0
41/11 15/11

(The basic variable a1 leaving, so delete the irrelevant a1 column:)
Third tableau:
x1 x2 x3 x4 P
rhs 0000 0
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0 0 1 -16 2 1 0 0 -2 4 0103 1

Phase–II Problem: P=2×1 + 3×2
Initial tableau:
-P
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x1 x2 x3 x4
2300 0
0 0 1 -16 2 1 0 0 -2 4 0103 1
rhs

Phase–II Problem: P=2×1 + 3×2
First tableau (Use elimination to the first row to make the entries for the initial basic variables 0):
-P
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x1 x2 x3 x4
0 0 0 -5 -11
0 0 1 -16 2 1 0 0 -2 4 0103 1
rhs

Phase–II Problem: P=2×1 + 3×2
Second tableau:
-P
-28/3 22/3 14/3
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115
x1 x2 x3 x4 0 5/3 0 0
rhs
016/3 1 0 1 2/3 0 0 0 1/3 0 1
1/3

So, the optimal solution is
 P*   28 , or P*  28
33
which is attained at the following minimizer:
x* 14, x* 0, x* 22, x* 1. 132 3343
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An Exercise
minP4x x x 123
subjectto 2x x 2x 4 123
3x 3x x 3 123
x 0, i1,2,3 i

Degeneracy
A linear program is degenerate, if a basic variable equals zero, i.e., the last column b contains a zero.
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x ba0b0 t min i ˆi,t  s
ˆˆ 1ima a
Cycling
In case of degeneracy, it may happen that ˆˆ
 i,t  s,t
Then, the performance function does not improve:
ˆˆ ˆ
PPcxP tt
eventually leading to the cycling problem,
i.e., the (leaving) basic variable xs will be re-chosen as the entering variable in a few steps.
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120
A Degenerate LP Problem
For the following LP problem, the simplex method cycles indefinitely!
minP3x 150x  1 x 6x 41 2503 4
subjectto 1x60x1x9x0 41 2253 4
1x 90x  1 x 3x 0 21 2503 4
x3 1 xi 0, 1i4.

Bland’s Rule
Goal: to avoid cycling. Make the following changes in the simplex method,
a) select the lowest‐indexed favorable column to enter the basis, using the rule:
b) In case of ties or undecision, select the lowest indexed column to leave the basis.

jmin j: c 0 . ˆj
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The Perturbation Method
This is another efficient method to avoid cycling or minimize computation time.
The simplex method will surely terminate if applied to the LP with perturbed constraints:
Ax  b where,forsufficientlysmall0 0,
 ,2,,m 000