Engineering Optimization: Theory and Practice, Fourth Edition

(Martin Jones) #1

82 Classical Optimization Techniques


In terms of the notation of our equations, let us take the independent variables as

x 3 =y 3 and x 4 =y 4 so that x 1 =y 1 and x 2 =y 2

Then the Jacobian of Eq. (2.27) becomes

J

(

g 1 , g 2
x 1 , x 2

)

=

∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣

∂g 1
∂y 1

∂g 1
∂y 2
∂g 2
∂y 1

∂g 2
∂y 2

∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣

=





12

1 2




∣=^0

and hence the necessary conditions of Eqs. (2.26) cannot be applied.
Next, let us take the independent variables asx 3 =y 2 andx 4 =y 4 so thatx 1 =y 1
andx 2 =y 3. Then the Jacobian of Eq. (2.27) becomes

J

(

g 1 , g 2
x 1 , x 2

)

=

∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣

∂g 1
∂y 1

∂g 1
∂y 3
∂g 2
∂y 1

∂g 2
∂y 3

∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣

=





1 3

1 5




∣=^2 =^0

and hence the necessary conditions of Eqs. (2.26) can be applied. Equations (2.26) give
fork=m+ 1 = 3
∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣
∂f
∂x 3

∂f
∂x 1

∂f
∂x 2
∂g 1
∂x 3

∂g 1
∂x 1

∂g 1
∂x 2
∂g 2
∂x 3

∂g 2
∂x 1

∂g 2
∂x 2

∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣

=

∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣

∂f
∂y 2

∂f
∂y 1

∂f
∂y 3
∂g 1
∂y 2

∂g 1
∂y 1

∂g 1
∂y 3
∂g 2
∂y 2

∂g 2
∂y 1

∂g 2
∂y 3

∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ =

∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣

y 2 y 1 y 3
2 1 3
2 1 5

∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣

=y 2 ( 5 − 3 )−y 1 ( 01 − 6 )+y 3 ( 2 − 2 )
= 2 y 2 − 4 y 1 = 0 (E 4 )

and fork=m+ 2 =n=4,
∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣
∂f
∂x 4

∂f
∂x 1

∂f
∂x 2
∂g 1
∂x 4

∂g 1
∂x 1

∂g 1
∂x 2
∂g 2
∂x 4

∂g 2
∂x 1

∂g 2
∂x 2

∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣

=

∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣

∂f
∂y 4

∂f
∂y 1

∂f
∂y 3
∂g 1
∂y 4

∂g 1
∂y 1

∂g 1
∂y 3
∂g 2
∂y 4

∂g 2
∂y 1

∂g 2
∂y 3

∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣ ∣
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