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Par f 1 ;f 2 ;f 3
with respect to the three objective functions f 1 ;f 2 ;f 3 . For a cell C 2
gen
X
Now it remains to consider the Pareto-optimality of the set
C
we define
the collapsing and the remaining part of C with respect to Q-criteria optimality by
col Q .C/ WD ˚ x 2 C W x
Par f 1 ;:::;f Q
X
rem Q .C/ WD ˚ x 2 C W x 2
Par f 1 ;:::;f Q :
X
Summing up the preceding results we get a complete geometric characteriza-
tion of the set of Pareto solutions for the three criteria case. For each cell C,
col Q .C/ P [ rem Q .C/ D C and,asshownbyNickeletal.( 2005b ), determining both
sets can be done with the gradients of the objective functions with a complexity of
O.Qlog Q/.
Theorem 9.6 The set of Pareto solutions satisfies:
X Par f 1 ;f 2 ;f 3 D X
Par f 1 ;f 2 ;f 3 [ encl X
Par f 1 ;f 2 ;f 3
gen
gen
Par f 1 ;f 2 ;f 3 ;x 2 col 3 .C/ g :
gen
2 W9 C 2
nf x 2
C
;C
X
R
X Par f 1 ;f 2 ;f 3 . Then we have, by Theorem 9.5 ,thaty 2
Proof Let y 2
Par f 1 ;f 2 ;f 3 [ encl X
Par f 1 ;f 2 ;f 3 : Moreover for C 2
gen
gen
X
C
with y 2 C
we have y 2 rem 3 .C/,i.e.,y col 3 .C/. This implies
y 2 X
Par f 1 ;f 2 ;f 3 [ encl X
Par f 1 ;f 2 ;f 3
gen
gen
Par f 1 ;f 2 ;f 3 ;x 2 col 3 .C/ g :
gen
2 W9 C 2
nf x 2
R
C
;C
X
We distinguish the following cases :
Case 1: y 2 encl X
Par f 1 ;f 2 ;f 3 .Theny 2
X Par f 1 ;f 2 ;f 3 by Theo-
gen
rem 9.5 .
Case 2 : y 2
Par f 1 ;f 2 ;f 3 .
Case 2.1 : 9 C 2
gen
X
Par f 1 ;f 2 ;f 3 with y 2 C
) y col 3 .C/ ) y 2 rem 3 .C/ ) y 2
gen
C
;C
X
X Par f 1 ;f 2 ;f 3 .
Par f 1 ;f 2 ;f 3 with y 2 C
) L .f p ;f p .y// \ L .f q ;f q .y// Df y g for some p;q 2f 1;2;3 g ;p<q
) T qD1 L .f q ;f q .y// D y g) y 2
gen
Case 2.2 : 69 C 2
C
;C
X
X sPar f 1 ;f 2 ;f 3
X Par f 1 ;f 2 ;f 3 :
In the case of median functions the gradients r f q .x/; q 2f 1;2;3 g ; (in
those points where they are well-defined) can be computed in O.M log.G max //
time (analogous to the evaluation of the function). Therefore, we can test in
O.M log.G max // time if a cell C 2
Par f 1 ;f 2 ;f 3 collapses. We
gen
C
;C
X
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