optimization_1
DESCRIPTION
Engineering AnalysisTRANSCRIPT
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Advanced Engineering Mathematics
E. Kreyszig
Chapter 22
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Continued
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g(t) = 0 to find t
Method of steepest descent
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Continued
Example
x = x1i + x2j
f = (/ x i + / y j)f
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New x1 = (1-2t)x1
New x2 = (1-6t)x2
63
13.9
3.08
0.68
f (x)=(x12+3x22)
t = (x12+9x22)/(x12+54x22)
z(t) = (1-2t)x1i + (1-6t)x2j
f = x12 + 3x22
0
1
2
3
x1
x2
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Continued
Linear programming
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Example
Continued
Objective function to be maximized
Constraints
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Feasibility region
2x1+ 8x2 = 60
5x1 + 2x2 = 60
z = 40x1+ 88x2
(10, 5)
Max hourly revenue
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2x1+ 8x2 60
5x1+ 2x2 60
Inequalities
Slack variables
O
C
B
A
Vertices A, B, C and O are basic feasible solutions (defined as having n-m zero variables) Vertex B is optimal solution Any point inside blue region represents a feasible solutionEngineering Analysis
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Continued
All bj nonnegative. If a bj < 0, multiply equation by -1 x1, .., xn include slack variables ( for which cj of these variables in f are zero)Engineering Analysis
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1.psd -
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Continued
Vertex O
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Continued
Operation O1
Operation O2
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Vertex A
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Row 1 + 20 Row 2
Row 3 2.5 Row 2
x1=30 x2=0 x3=0 x4= -90 z=1200
Basic variables
0 72 20 0 1200
0 -18 -2.5 1 -90
T1
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Continued
Operation O1
Operation O2
Basic variables
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Vertex B
O
C
B
A
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12.psd13.psd14.psd