Engineering Optimization: Theory and Practice, Fourth Edition

(Martin Jones) #1
7.7 Zoutendijk’s Method of Feasible Directions 397

s 1

∂g 2
∂x 1

+s 2

∂g 2
∂x 2

+ · · · +sn

∂g 2
∂xn

+θ 2 α≤ 0

..
.

s 1

∂gp
∂x 1

+s 2

∂gp
∂x 2

+ · · · +sn

∂gp
∂xn

+θpα≤ 0 (7.39)

s 1

∂f
∂x 1

+s 2

∂f
∂x 2

+ · · · +sn

∂f
∂xn

+α≤ 0

s 1 − 1 ≤ 0

s 2 − 1 ≤ 0
..
.
sn− 1 ≤ 0
− 1 −s 1 ≤ 0

− 1 −s 2 ≤ 0
..
.
− 1 −sn≤ 0

wherepis the number of active constraints and the partial derivatives∂g 1 /∂x 1 , ∂g 1 /∂x 2 ,
... , ∂gp/∂xn, ∂f/∂x 1 ,... , ∂f/∂xnhave been evaluated at pointXi. Since the com-
ponents of the search direction,si, i= 1 ton, can take any value between−1 and 1,
we define new variablestiasti=si+ , 1 i=1 ton, so that the variables will always
be nonnegative. With this change of variables, the problem above can be restated as
a standard linear programming problem as follows:


Find(t 1 , t 2 ,... , tn, α, y 1 , y 2 ,... , yp+n+ 1 ) hichw

minimizes −α
subject to


t 1

∂g 1
∂x 1

+t 2

∂g 1
∂x 2

+ · · · +tn

∂g 1
∂xn

+θ 1 α+y 1 =

∑n

i= 1

∂g 1
∂xi

t 1

∂g 2
∂x 1

+t 2

∂g 2
∂x 2

+ · · · +tn

∂g 2
∂xn

+θ 2 α+y 2 =

∑n

i= 1

∂g 2
∂xi

..
.

t 1

∂gp
∂x 1

+t 2

∂gp
∂x 2

+ · · · +tn

∂gp
∂xn

+θpα+yp=

∑n

i= 1

∂gp
∂xi

(7.40)
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